diff --git a/.env.finals.example b/.env.finals.example
new file mode 100644
index 00000000..49ac516d
--- /dev/null
+++ b/.env.finals.example
@@ -0,0 +1,9 @@
+# Host-side paths consumed by docker-compose.gemma4-demo.yml.
+# Copy to .env.finals and pass it with: docker compose --env-file .env.finals ...
+PAPER9_HOST_REPO=/absolute/path/to/paper9-mnr-offline-package
+PAPER9_BISHAN_RUNS_HOST=/absolute/path/to/bishan-runs
+PAPER9_DONGXING_RUNS_HOST=/absolute/path/to/dongxing-runs
+
+# Ollama must expose the exact Gemma4:26b tag.
+OLLAMA_API_BASE=http://host.docker.internal:11434
+EMBEDDING_MODEL=nomic-embed-text-v2-moe
diff --git a/README.md b/README.md
index 000f2fae..a5d544f1 100644
--- a/README.md
+++ b/README.md
@@ -2,27 +2,39 @@
**中文** | [English](./README_en.md)
-本项目是基于 Gemma 4 的 GIS Data Agent，面向自然语言 GIS 数据发现、耕地空间布局优化、NL2Semantic2SQL 和 WorldModel v2.1 规划场景，重点展示原生工具调用、多智能体工作流和持久化记忆能力。
+本项目是面向 Gemma 4 开发者大赛赛道 A（AI Agent）的 GIS Data Agent 决赛版本。系统聚焦两类真实地理空间任务：高频的 `NL2Semantic2GeoSQL` 空间问数，以及低频但高价值的县域耕地空间布局优化；Gemma 4 26B 负责理解目标、选择工具和根据反馈决定下一步，Google ADK 承载原生函数调用，PostGIS、GIS 与县域耕地空间优化引擎负责确定性计算。
本仓库当前 README 面向 Gemma 4 开发者大赛赛道 A：AI Agent 组织，重点展示：
- Gemma 4 26B MoE 的规格选择依据。
- 基于 Google ADK 的多智能体架构。
- 原生函数调用和工具调用。
-- 智能体记忆。
-- 三个真实 GIS 场景：无 `@` 数据发现与耕地优化、`nl2semantic2sql`、`worldmodelv2.1`。
+- 通过硬约束校验后才允许写入的已验证经验记忆。
+- 两个决赛核心场景：`NL2Semantic2GeoSQL` 与县域耕地受控规划；无 `@` 数据发现与耕地优化作为扩展场景保留。
- 纯 Docker Compose 可复现演示环境。
+## 决赛快速入口
+
+| 材料 | 用途 |
+|---|---|
+| [路演 PPTX](docs/finals/GIS_Data_Agent_Gemma4_Finals_CN.pptx) / [PDF](docs/finals/GIS_Data_Agent_Gemma4_Finals_CN.pdf) | 6 页主路演 + 5 页 Q&A 附录，绑定同一份 406 对置换证据 |
+| [真实验证演示脚本](docs/finals/verified_demo_script.md) | 最新操作步骤、人工验收点、70 秒演示镜头和 4 分 40 秒逐字口播 |
+| [决赛技术手册（Markdown）](docs/finals/GIS_Data_Agent_Gemma4_Finals_Technical_Handbook_CN.md) | Gemma 4、ADK、GeoSQL、GWM/MPC、受控自主、记忆、部署和 20 个深度问答 |
+| [技术手册 Word](docs/finals/GIS_Data_Agent_Gemma4_Finals_Technical_Handbook_CN.docx) / [PDF](docs/finals/GIS_Data_Agent_Gemma4_Finals_Technical_Handbook_CN.pdf) | Word 含二级可点击目录和导航窗格章节树；PDF 用于离线查阅 |
+| [评分证据矩阵](docs/finals/scoring_evidence_matrix.md) / [质量检查报告](docs/finals/quality_gate_report.md) | 对照 30/25/20/15/10 评分权重核对证据、边界与工程质量 |
+
## 比赛要求对齐
| 比赛要求 | 本项目交付 |
|---|---|
| 核心代码包含 Gemma 4 调用逻辑 | `data_agent/model_gateway.py` 注册 Gemma 4 26B，Docker 环境变量固定所有演示模型层级 |
-| 原生函数调用和工具调用 | ADK `FunctionTool` / `LongRunningFunctionTool`，运行日志展示 `drl_model`、`visualize_interactive_map`、`run_nl2semantic2sql` 和 `world_model_v21_status -> world_model_v21_pipeline` |
-| 多步规划 | `Farmland Optimization Workflow` 多智能体工作流；WorldModel v2.1 A/B/C/D 流程：Prepare、Sample、Train、Plan |
-| 记忆机制 | `PostgresMemoryService`、`save_memory`、`recall_memories`、`auto_extract`、运行日志记忆统计 |
-| 演示视频 5 分钟以内 | [docs/gemma4_ai_agent_demo_script.md](docs/gemma4_ai_agent_demo_script.md) |
-| 技术报告 | [docs/gemma4_ai_agent_technical_report.md](docs/gemma4_ai_agent_technical_report.md) |
+| 原生函数调用和工具调用 | ADK `FunctionTool` / `LongRunningFunctionTool`；县域规划 Agent 暴露 10 个结构化工具，Gemma 4 根据工具响应选择下一步 |
+| 受控多步规划 | `status -> inspect -> recall -> pipeline/plan -> audit -> commit/replan/HITL`；A/B/C/D 为底层算法阶段，不冒充模型动态规划 |
+| 记忆机制 | 仅通过硬约束校验且空间产物完整的结果可进入 append-only verified episodic memory；未审核结果拒绝写入 |
+| Agent 可靠性 | 3 个分支各 10 次真实 Gemma 4 + ADK 运行，30/30 行为契约通过；算法工具使用确定性替身，真实 MPC 产物单独取证 |
+| 工程质量 | 最终镜像运行时回归 `236 passed`、宿主交付契约 `6 passed`、接口兼容测试 `52 passed`；73 项决赛关键测试是 236 项的子集，不能累加 |
+| 演示视频 5 分钟以内 | [决赛唯一最新版演示脚本](docs/finals/verified_demo_script.md) |
+| 技术报告 | [决赛技术手册（Markdown）](docs/finals/GIS_Data_Agent_Gemma4_Finals_Technical_Handbook_CN.md) |
| 记忆和工具调用代码说明 | [docs/gemma4_ai_agent_code_walkthrough.md](docs/gemma4_ai_agent_code_walkthrough.md) |
| Docker 部署指南 | 本 README 的 [Docker Compose 快速启动](#docker-compose-快速启动) |
| 运行日志截图 | UI 路径：`工作台 -> 平台运营 -> 运行日志`；建议保存到 `docs/submission_screenshots/` |
@@ -57,14 +69,14 @@
| 演示场景 | 研究工程 | 对应论文和投稿状态 | 在 GIS Data Agent 中的落地 |
|---|---|---|---|
| 耕地空间布局优化 | [zhouning/farmland-drl-optimization](https://github.com/zhouning/farmland-drl-optimization) | *Constrained Farmland Layout Optimization under Cultivated-Land Balance: A Two-Site Test of Dimension-Invariant Reinforcement Learning*，已投稿 *Land Use Policy* | 无 `@` 数据发现后自动进入 `Farmland Optimization Workflow`，调用 `drl_model` 和 `visualize_interactive_map`，输出优化后矢量、PNG、地图图层和 PDF 报告 |
-| NL2Semantic2SQL | [zhouning/nl2geosql-reproduction](https://github.com/zhouning/nl2geosql-reproduction) | *Schema-Aware Grounding Effects in PostGIS Natural-Language-to-SQL: A Subset-Decomposed Evaluation Across Eleven LLMs*，已投稿 *Computers & Geosciences* | `@NL2SQL` 触发 `run_nl2semantic2sql`，将中文空间问题映射到语义层、schema grounding、PostGIS SQL 生成和只读执行护栏 |
-| WorldModel v2.1 | [zhouning/arcgis-farmland-mpc](https://github.com/zhouning/arcgis-farmland-mpc) | *Reproducible model-based AI planning for county-scale farmland consolidation in fragmented mountain landscapes*，已投稿 Nature Portfolio 子刊 *Communications Earth & Environment* | `@WorldModelV21` 触发 `world_model_v21_status -> world_model_v21_pipeline`，把 Prepare / Sample / Train / Plan 四阶段封装为可复用的县域 MPC 规划工具链 |
+| NL2Semantic2GeoSQL | [zhouning/nl2geosql-reproduction](https://github.com/zhouning/nl2geosql-reproduction) | *Schema-Aware Grounding Effects in PostGIS Natural-Language-to-SQL: A Subset-Decomposed Evaluation Across Eleven LLMs*，已投稿 *Computers & Geosciences* | `@NL2SQL` 触发 `run_nl2semantic2sql`，将中文空间问题映射到语义层、schema grounding、PostGIS SQL 生成和只读执行护栏 |
+| 县域耕地空间优化引擎 / GWM 原型 | [zhouning/arcgis-farmland-mpc](https://github.com/zhouning/arcgis-farmland-mpc) | *Reproducible model-based AI planning for county-scale farmland consolidation in fragmented mountain landscapes*，已投稿 Nature Portfolio 子刊 *Communications Earth & Environment* | `@WorldModelV21` 触发受控自主链路，将内部算法包 `0.3.3 / 2.2.3` 包装为可审计的县域 MPC 工具链 |
三个场景对应的技术难点分别是：
- 耕地空间布局优化：在退坡还林、耕地占补平衡和地块连通性之间做约束优化，核心不是普通分类，而是带空间邻接结构和政策约束的地块级决策。
-- NL2Semantic2SQL：在中文自然语言、语义层、PostGIS schema、空间谓词和 `geometry/geography` 单位语义之间建立 grounding，避免大模型把空间查询降级为普通 SQL。
-- WorldModel v2.1：把县域地块状态转移学习成可用于 MPC 的世界模型，用已训练 ensemble 支撑快速规划，并通过 GIS 产物验证坡度、连通性和面积变化。
+- NL2Semantic2GeoSQL：在中文自然语言、语义层、PostGIS schema、空间谓词和 `geometry/geography` 单位语义之间建立 grounding，避免大模型把空间查询降级为普通 SQL。
+- 县域耕地空间优化引擎：图斑与空间块构成状态，候选地类置换构成行动，学习型状态转移模型集成预测行动后果，MPC 搜索方案；这是领域化 GWM 原型，不表述为完整通用地理空间世界模型。
这意味着比赛演示的重点不是“把一个大模型接到几个函数上”，而是验证 Gemma 4 是否能稳定调用已经具备研究深度的 GIS 算法系统，并把真实数据、空间数据库、模型推理、规划输出、可视化和报告生成串成可解释的端到端工作流。
@@ -77,7 +89,7 @@
数据进入方式：
```text
-上传 /Users/zhouning/Downloads/shp/斑竹村10000.zip
+在界面上传 `斑竹村10000.zip`
```
用户输入：
@@ -120,7 +132,7 @@
录制口径：`Pairs=0` 时不要口播“成对置换已完成”；`Net Change=-130` 表示地类数量存在净变化，不应说成总量完全平衡。
-### 2. NL2Semantic2SQL
+### 2. NL2Semantic2GeoSQL
用户输入中文空间问题，系统执行：
@@ -150,20 +162,34 @@
| 最长桥梁 100m 内 POI | CTE + `ST_DWithin(...::geography, 100)` | `35` |
| 与桥梁相交的建筑物轮廓 | `ST_Intersects` + `COUNT(DISTINCT b."Id")` | `1` |
-为什么不是普通 NL2SQL：空间 SQL 需要处理 `geometry/geography` 单位、SRID、空间谓词、距离、面积、长度和空间连接去重。项目通过语义层 grounding 和 PostGIS 执行护栏降低 LLM 直接生成空间 SQL 的不稳定性。
+为什么不是普通 NL2SQL：空间 SQL 需要处理 `geometry/geography` 单位、SRID、空间谓词、距离、面积、长度和空间连接去重。项目通过语义层 grounding、语义改写和 PostGIS 只读执行护栏降低 LLM 直接生成空间 SQL 的不稳定性；锁定演示查询返回 `35`，并默认把最长桥梁、100 米缓冲区和命中 POI 加载到地图。
-### 3. WorldModel v2.1
+### 3. 县域耕地受控规划与领域化 GWM 原型
用户输入短句即可运行县域 MPC 规划：
```text
-@WorldModelV21 请先检查世界模型 v2.1 状态，然后使用 dongxing 数据集运行一次快速县域 MPC 规划。
+@WorldModelV21 请使用 bishan 数据集运行一次快速县域 MPC 规划，完成硬约束审计，并仅在通过后保存已验证经验。
+```
+
+首次审计通过时，Gemma 4 的完整工具轨迹是：
+
+```text
+world_model_v21_status
+-> paper9_inspect_resources
+-> paper9_recall_verified_episodes
+-> world_model_v21_pipeline
+-> paper9_audit_run
+-> paper9_commit_verified_episode
```
-Gemma 4 推荐工具轨迹：
+首次审计失败时，只允许一次重规划：
```text
-world_model_v21_status -> world_model_v21_pipeline
+... -> paper9_audit_run(attempt=0, retryable=true)
+-> world_model_v21_plan
+-> paper9_audit_run(attempt=1)
+-> commit_verified_episode 或 stop_and_request_human_review
```
`world_model_v21_pipeline` 展示完整 A/B/C/D 阶段：
@@ -182,12 +208,32 @@ world_model_v21_status -> world_model_v21_pipeline
| `bishan` / `璧山` | `/app/bishan-runs/prepared` | `/app/bishan-runs/prepared/ensemble_seed0` |
| `dongxing` / `东兴` | `/app/dongxing-runs/prepared` | `/app/dongxing-runs/prepared/ensemble_seed0` |
-已验证结果：
+当前版本绑定与真实 Tool 4 验证：
-| 数据集 | 地块块数 | 图斑数 | `steps_run` | `total_reward` |
+| 项目 | 结果 |
+|---|---:|
+| 内部算法包 / 算法 | `0.3.3` / `2.2.3` |
+| 算法源码 commit | `848514383948474e4b6387f171aea8a9272821db` |
+| 输入空间记录 / 环境图斑 / blocks | 101,657 / 53,004 / 2,640 |
+| MPC 步数 / 双向置换 | 100 / 406 对 |
+| 耕地面积变化 | `+1.935936 ha` |
+| 平均坡度变化 | `-0.607765%` |
+| 连片度变化 | `+0.020184` |
+| 百亩方面积变化 | `+35.131619 ha` |
+| 总回报 | `37.409379` |
+| 硬约束校验 | 通过 |
+
+本次锁定运行使用 `0.3.3 / 2.2.3` 代码执行 Tool 4，复用了历史流程生成的 prepared 与 ONNX ensemble，且输入被识别为旧三位测试编码。它证明当前代码绑定、真实 MPC、空间产物和硬约束审核，不证明最新真实权威四库的完整 A/B/C/D 重训或生产验收。
+
+Gemma 4 + ADK 编排可靠性基线：
+
+| 场景 | 通过率 | 固定轨迹工具数 | 平均延迟 | P95 延迟 |
|---|---:|---:|---:|---:|
-| Bishan | 2640 | 53004 | 100 | 66.43446147434678 |
-| Dongxing | 3711 | 76377 | 100 | 112.63640181479221 |
+| 首次审计通过 | 10/10 | 6 | 7.97 s | 9.53 s |
+| 版本不兼容后停止 | 10/10 | 2 | 3.24 s | 4.88 s |
+| 一次重规划后通过 | 10/10 | 8 | 13.57 s | 15.31 s |
+
+30/30 的 Wilson 95% 区间为 88.65%–100%；每个 10/10 场景的区间为 72.25%–100%。该基线使用真实 Gemma 4 26B 与 Google ADK，规划工具响应为确定性替身，专门测量工具选择和分支控制，不代表 30 次算法重跑。
地图按 `CHG_FLAG` 展示优化变化：
@@ -204,13 +250,14 @@ flowchart TB
A --> M["Gemma 4 26B：Ollama 模型网关"]
A --> T["ADK 工具集"]
T --> F["数据发现 + Farmland DRL 优化"]
- T --> N["NL2Semantic2SQL 引擎"]
- T --> W["WorldModel v2.1 引擎"]
- T --> MEM["记忆工具"]
+ T --> N["NL2Semantic2GeoSQL 引擎"]
+ T --> W["县域耕地空间优化引擎：GWM 原型 + MPC"]
+ W --> G["版本与资源检查 + 硬约束校验"]
+ G --> MEM["仅通过结果进入 verified memory"]
F --> FS1["用户上传 Shapefile / ZIP / PNG / PDF 报告"]
N --> PG["PostgreSQL + PostGIS + pgvector"]
MEM --> PG
- W --> FS["Paper9 仓库 + Bishan/Dongxing 运行数据"]
+ W --> FS["算法包 + Bishan/Dongxing 运行数据"]
A --> LOG["Chainlit Thread/Step 运行日志"]
```
@@ -219,14 +266,16 @@ flowchart TB
| 模块 | 文件 |
|---|---|
| Gemma 4 模型网关 | `data_agent/model_gateway.py`, `docker-compose.gemma4-demo.yml` |
-| ADK 路由 | `data_agent/agent.py` |
+| ADK 路由与单一行为契约 | `data_agent/agent.py`, `data_agent/paper9_agent_prompt.py` |
| 无 @ 数据发现与耕地优化 | `data_agent/toolsets/file_tools.py`, `data_agent/data_catalog.py`, `data_agent/gis_processors.py`, `data_agent/drl_engine.py`, `data_agent/pipeline_helpers.py` |
-| NL2Semantic2SQL 直接工具事件 | `data_agent/nl2semantic2sql_direct_agent.py` |
+| NL2Semantic2GeoSQL 直接工具事件 | `data_agent/nl2semantic2sql_direct_agent.py` |
| NL2SQL 执行护栏 | `data_agent/nl2sql_executor.py`, `data_agent/nl2sql_grounding.py`, `data_agent/nl2sql_semantic_rewrite.py`, `data_agent/sql_postprocessor.py` |
-| WorldModel v2.1 工具 | `data_agent/toolsets/world_model_v21_tools.py`, `data_agent/world_model_v21.py` |
-| 记忆机制 | `data_agent/conversation_memory.py`, `data_agent/memory.py` |
+| 县域规划工具与适配器 | `data_agent/toolsets/world_model_v21_tools.py`, `data_agent/world_model_v21.py` |
+| 硬约束审核与 verified memory | `data_agent/paper9_agent_governance.py` |
+| Agent 行为评测 | `data_agent/paper9_agent_evaluation.py`, `scripts/run_paper9_adk_reliability_eval.py` |
+| 通用记忆机制 | `data_agent/conversation_memory.py`, `data_agent/memory.py` |
| 运行日志 | `data_agent/app.py`, `data_agent/frontend_api.py`, `frontend/src/components/datapanel/AgentRunLogsTab.tsx` |
-| Word / PDF 报告导出 | `data_agent/report_generator.py`, `data_agent/app.py` |
+| 图文 Word / PDF 报告导出 | `data_agent/world_model_v21_report.py`, `data_agent/report_generator.py`, `data_agent/app.py` |
## Docker Compose 快速启动
@@ -234,20 +283,18 @@ flowchart TB
- Docker Desktop，或带 Compose 的 Docker Engine。
- 可访问已加载 `Gemma4:26b` 的 Ollama 主机。
-- 本地 Paper9 / Bishan / Dongxing 路径与 `docker-compose.gemma4-demo.yml` 中的挂载一致；如果路径不同，启动前先调整挂载项。
+- 县域耕地空间优化引擎 `0.3.3 / 2.2.3` 源码目录和已准备的 Bishan / Dongxing 资源；通过 `.env.finals` 配置主机路径。
-当前演示 compose 文件需要以下本地挂载：
+从模板生成本机配置，并填写绝对路径：
-```yaml
-- /Users/zhouning/arcgis-farmland-mpc:/app/paper9-demo:ro
-- /Users/zhouning/farmland_mpc_runs/bishan:/app/bishan-runs:ro
-- /Users/zhouning/arcgis-farmland-mpc/runs/dongxing:/app/dongxing-runs:ro
+```bash
+cp .env.finals.example .env.finals
```
### 启动
```bash
-docker compose -f docker-compose.gemma4-demo.yml up -d --build
+docker compose --env-file .env.finals -f docker-compose.gemma4-demo.yml up -d --build
```
打开：
@@ -292,6 +339,9 @@ docker compose -f docker-compose.gemma4-demo.yml down
| `NL2SQL_LLM_SCHEMA_MAPPER_MODEL` | `gemma4-26b-ollama` | NL2SQL schema mapper 模型 |
| `EMBEDDING_MODEL` | `nomic-embed-text-v2-moe` | few-shot 和语义检索 embedding；使用非固定 IP 的 Ollama embedding 别名 |
| `OLLAMA_API_BASE` | `${OLLAMA_API_BASE:-http://host.docker.internal:11434}` | 宿主机本机 Ollama 服务地址；比赛现场可通过环境变量覆盖 |
+| `PAPER9_HOST_REPO` | `/absolute/path/to/paper9-mnr-offline-package` | 主机端 Paper9 0.3.3 源码目录，容器内只读挂载到 `/app/paper9-demo` |
+| `PAPER9_BISHAN_RUNS_HOST` | `/absolute/path/to/bishan-runs` | 主机端 Bishan Prepare / ONNX 资源 |
+| `PAPER9_DONGXING_RUNS_HOST` | `/absolute/path/to/dongxing-runs` | 主机端 Dongxing 资源；主 Demo 默认使用 Bishan |
| `PAPER9_FARMLAND_MPC_REPO` | `/app/paper9-demo` | WorldModel v2.1 Paper9 仓库 |
| `PAPER9_FARMLAND_MPC_DEFAULT_PREPARED_DIR` | `/app/bishan-runs/prepared` | 默认 Bishan 准备数据 |
| `PAPER9_FARMLAND_MPC_DEFAULT_ENSEMBLE_DIR` | `/app/bishan-runs/prepared/ensemble_seed0` | 默认 Bishan ONNX 集成模型 |
@@ -303,61 +353,29 @@ PostGIS / Redis 也在 `docker-compose.gemma4-demo.yml` 中配置。
## 演示脚本
-打开应用后依次运行以下提示词。
+现场操作、截图和录屏必须以 [决赛唯一最新版演示脚本](docs/finals/verified_demo_script.md) 为准。主 Demo 只运行两个经过锁定数据验证的场景。
-无 `@` 数据发现与耕地优化：
-
-```text
-上传 /Users/zhouning/Downloads/shp/斑竹村10000.zip
-```
-
-```text
-我有哪些数据？
-```
-
-```text
-基于斑竹村10000数据进行耕地空间布局优化分析
-```
-
-完成后可点击“导出 PDF 报告”，验证生成的是 PDF，并包含优化 PNG。
-
-NL2Semantic2SQL：
-
-```text
-@NL2SQL 统计重庆2021年道路网络中所有桥梁道路（bridge = T）的总长度，单位为公里。
-```
+NL2Semantic2GeoSQL：
```text
@NL2SQL 统计距离道路网络中最长桥梁100米范围内的高德POI数量。
```
-```text
-@NL2SQL 统计出空间上与道路网络中任意桥梁（bridge = T）相交（Intersects）的建筑物轮廓数量。
-```
-
-WorldModel v2.1：
+县域耕地受控规划：
```text
-@WorldModelV21 请先检查世界模型 v2.1 状态，然后使用 bishan 数据集运行一次快速县域 MPC 规划。
+@WorldModelV21 请使用 bishan 数据集运行一次快速县域 MPC 规划，完成硬约束审计，并仅在通过后保存已验证经验。
```
-```text
-@WorldModelV21 请先检查世界模型 v2.1 状态，然后使用 dongxing 数据集运行一次快速县域 MPC 规划。
-```
+第一个场景应返回 `35`，并默认在地图加载最长桥梁、100 米缓冲区和命中 POI。第二个场景展示六次 ADK 函数调用、406 对双向置换、硬约束审核和 `paper9_commit_verified_episode`；未通过审核的 episode 不得写入经验库。
-记忆：
-
-```text
-请把本次演示保存为记忆：Gemma 4 完成了斑竹村耕地空间布局优化、桥梁道路与建筑物相交的空间 NL2Semantic2SQL 查询，世界模型 v2.1 完成了 Bishan 和 Dongxing 县域 MPC 规划。关键词：Gemma4空间演示。
-```
-
-```text
-检索关键词“Gemma4空间演示”的记忆。
-```
+规划完成后点击“导出 PDF 报告”，系统会从本次运行的优化空间图层、MPC 汇总、函数调用轨迹、硬约束审计和经验提交记录生成专用报告。报告包含真实变化地图、关键指标看板、Gemma 4 + Google ADK 六步调用图、逐函数用时、审计表和交付物摘要；数值不从聊天文本推断，也不复用其他运行的静态图片。
-完整录制指南：
+决赛材料：
-- [docs/gemma4_ai_agent_demo_script.md](docs/gemma4_ai_agent_demo_script.md)
+- [真实验证演示脚本](docs/finals/verified_demo_script.md)
+- [4 分 40 秒路演逐字稿](docs/finals/pitch_script.md)
+- [现场运行与故障预案](docs/finals/demo_runbook.md)
## 运行日志截图
@@ -396,24 +414,30 @@ GET /api/agent/run-logs
| 文档 | 用途 |
|---|---|
-| [docs/gemma4_ai_agent_technical_report.md](docs/gemma4_ai_agent_technical_report.md) | 技术报告：模型选择、架构、场景和部署 |
-| [docs/gemma4_ai_agent_code_walkthrough.md](docs/gemma4_ai_agent_code_walkthrough.md) | 面向无 `@` 耕地优化、`nl2semantic2sql` 和 `worldmodelv2.1` 的记忆、工具调用代码说明 |
-| [docs/gemma4_ai_agent_demo_script.md](docs/gemma4_ai_agent_demo_script.md) | 5 分钟演示脚本和本地复测证据 |
+| [决赛资料入口](docs/finals/README.md) | 路演、Demo、技术审查与证据状态的单一入口 |
+| [路演 PPTX](docs/finals/GIS_Data_Agent_Gemma4_Finals_CN.pptx) / [PDF](docs/finals/GIS_Data_Agent_Gemma4_Finals_CN.pdf) | 6 页主路演与 5 页 Q&A 附录 |
+| [决赛技术手册（Markdown）](docs/finals/GIS_Data_Agent_Gemma4_Finals_Technical_Handbook_CN.md) | 关键实现、架构图、代码索引和 20 个深度问答 |
+| [真实验证演示脚本](docs/finals/verified_demo_script.md) | 锁定提示词、验收值、镜头与逐字口播 |
+| [docs/finals/scoring_evidence_matrix.md](docs/finals/scoring_evidence_matrix.md) | 五项评分维度的证据、口径与缺口 |
+| [docs/finals/quality_gate_report.md](docs/finals/quality_gate_report.md) | 精确测试集合、构建结果、运行状态与已知警告 |
+| [docs/finals/demo_runbook.md](docs/finals/demo_runbook.md) | 5 分钟现场流程、视频备份与故障预案 |
+| [docs/finals/qa.md](docs/finals/qa.md) | 3 分钟 Q&A 答案口径 |
+| [初赛技术报告](docs/gemma4_ai_agent_technical_report.md) / [代码说明](docs/gemma4_ai_agent_code_walkthrough.md) | 初赛版本材料，保留用于历史对照 |
| [docs/assets/gemma4_host228_scale_sweep_summary.csv](docs/assets/gemma4_host228_scale_sweep_summary.csv) | Gemma 4 模型规格评测依据 |
| [docs/assets/gemma4_host228_scale_sweep.svg](docs/assets/gemma4_host228_scale_sweep.svg) | 模型规格评测图 |
## GitHub About
-建议仓库描述（英文，适合 GitHub About）：
+当前仓库描述：
```text
-Gemma 4 powered GIS Data Agent for geospatial NL2Semantic2SQL, farmland optimization, and WorldModel v2.1 planning with native tool calling and memory.
+Gemma 4 + Google ADK GIS Agent for NL2Semantic2GeoSQL and controlled county-scale GWM/MPC planning with native function calling, hard-constraint validation, recovery, and verified memory.
```
建议主题：
```text
-agent-memory, ai-agent, docker-compose, farmland-optimization, function-calling, gemma4, geospatial, gis, google-adk, mpc, multi-agent, nl2sql, postgis, tool-calling, world-model
+agent-memory, ai-agent, docker-compose, farmland-optimization, function-calling, gemma4, geospatial, geospatial-ai, geosql, gis, google-adk, model-predictive-control, mpc, multi-agent, nl2sql, postgis, tool-calling, world-model
```
## 许可证
diff --git a/data_agent/agent.py b/data_agent/agent.py
index 735f7bcb..bdd0fb9d 100644
--- a/data_agent/agent.py
+++ b/data_agent/agent.py
@@ -78,6 +78,7 @@
from .toolsets.world_model_v2_tools import WorldModelV2Toolset
from .toolsets.world_model_v21_tools import WorldModelV21Toolset
from .toolsets.territory_world_model_tools import TerritoryWorldModelToolset
+from .paper9_agent_prompt import PAPER9_AGENT_INSTRUCTION
# ArcPy conditional function lists (for governance agents needing specific subsets)
from .toolsets.geo_processing_tools import (
@@ -1076,29 +1077,7 @@ def _build_mention_nl2sql_agent():
def _build_mention_world_model_v21_agent():
return LlmAgent(
name="MentionWorldModelV21",
- instruction=(
- "你是世界模型 v2.1 演示代理，只使用可用的 world_model_v21_status、"
- "world_model_v21_prepare、world_model_v21_sample、world_model_v21_train、"
- "world_model_v21_plan 和 world_model_v21_pipeline 工具。必须先调用 "
- "world_model_v21_status 检查 Paper9 仓库、默认 prepared_dir 和 ensemble_dir。"
- "如果用户要求完整 A->B->C->D 链路，调用 world_model_v21_pipeline；"
- "如果用户只要求规划或演示，默认调用 world_model_v21_pipeline 并设置 reuse_existing=true，"
- "以展示 Tool 1/2/3 复用和 Tool 4 实际规划；只有用户明确要求“只运行 Tool 4”"
- "或“只调用 plan”时，才调用 world_model_v21_plan。"
- "必须识别数据集名称：用户提到 dongxing 或东兴时，调用工具必须设置 dataset='dongxing'；"
- "用户提到 Bishan 或璧山时，调用工具必须设置 dataset='bishan'。"
- "只有用户没有提到数据集时，才使用默认 dataset='bishan'。"
- "用户未提供 prepared_dir 或 ensemble_dir 时，对应参数保持空字符串，让工具根据 dataset 选择演示路径。"
- "默认使用 env_kind=county, "
- "horizon=1, top_k=1, n_episodes=1, continuation=greedy, scoring=reward，"
- "除非用户明确指定其他参数。最终用中文简要总结 status/version/mode/env_kind/"
- "steps_run/n_blocks/n_selected/total_reward/artifacts，并明确列出工具调用轨迹："
- "优先展示 world_model_v21_status -> world_model_v21_pipeline。不要输出英文 Plan/Step、"
- "思考过程、参数复述或重试纠错过程；如果工具重试后成功，只报告最终成功结果。"
- "说明 horizon 是 MPC 前瞻步长，steps_run 是环境实际执行步数，两者不是同一个指标。"
- "如果结果包含县域优化地图图层，明确说明右侧地图按 CHG_FLAG 展示优化变化："
- "灰色为保持不变，红色为耕地 -> 林地，绿色为林地 -> 耕地。"
- ),
+ instruction=PAPER9_AGENT_INSTRUCTION,
description="世界模型 v2.1 状态检查与 MPC 规划。",
model=get_model_for_tier("standard"),
output_key="analysis_result",
diff --git a/data_agent/app.py b/data_agent/app.py
index 074cb8a4..3f4ae886 100644
--- a/data_agent/app.py
+++ b/data_agent/app.py
@@ -1730,6 +1730,10 @@ async def _execute_pipeline(
_final_chart_updates = [] # accumulated chart configs
_world_model_v21_result = None
_world_model_v21_args = None
+ _world_model_v21_status = None
+ _paper9_audit_result = None
+ _paper9_commit_result = None
+ _world_model_v21_trace = []
_drl_optimization_result = None
_drl_optimization_args = None
_drl_comparison_map_seen = False
@@ -1757,7 +1761,16 @@ async def _execute_pipeline(
await progress_msg.send()
try:
- run_config = RunConfig(max_llm_calls=50) if (DYNAMIC_PLANNER and pipeline_type == "planner") else None
+ _selected_agent_name = getattr(selected_agent, "name", "") or ""
+ if DYNAMIC_PLANNER and pipeline_type == "planner":
+ run_config = RunConfig(max_llm_calls=50)
+ elif (
+ pipeline_type == "sub_agent_direct"
+ and _selected_agent_name == "MentionWorldModelV21"
+ ):
+ run_config = RunConfig(max_llm_calls=12)
+ else:
+ run_config = None
events = runner.run_async(user_id=user_id, session_id=session_id, new_message=content, run_config=run_config)
# Token accumulation counters
@@ -1935,6 +1948,29 @@ async def _execute_pipeline(
except Exception:
pass
try:
+ _paper9_trace_tools = {
+ "world_model_v21_status",
+ "paper9_inspect_resources",
+ "paper9_recall_verified_episodes",
+ "world_model_v21_prepare",
+ "world_model_v21_sample",
+ "world_model_v21_train",
+ "world_model_v21_plan",
+ "world_model_v21_pipeline",
+ "paper9_audit_run",
+ "paper9_commit_verified_episode",
+ }
+ if current_tool_name in _paper9_trace_tools:
+ _trace_duration = None
+ if _pending_tool_call:
+ _trace_duration = (
+ time.time() - _pending_tool_call["start_time"]
+ )
+ _world_model_v21_trace.append({
+ "tool_name": current_tool_name,
+ "duration_s": _trace_duration,
+ })
+
if current_tool_name in {"world_model_v21_plan", "world_model_v21_pipeline"}:
from data_agent.world_model_v21_presentation import parse_world_model_v21_tool_response
@@ -1946,6 +1982,20 @@ async def _execute_pipeline(
if _pending_tool_call else {}
)
logger.info("[WorldModelV21Presentation] Captured planning result for deterministic summary")
+ elif current_tool_name in {
+ "world_model_v21_status",
+ "paper9_audit_run",
+ "paper9_commit_verified_episode",
+ }:
+ from data_agent.world_model_v21_presentation import parse_structured_tool_response
+
+ _parsed_paper9 = parse_structured_tool_response(_resp_val)
+ if current_tool_name == "world_model_v21_status":
+ _world_model_v21_status = _parsed_paper9
+ elif current_tool_name == "paper9_audit_run":
+ _paper9_audit_result = _parsed_paper9
+ elif current_tool_name == "paper9_commit_verified_episode":
+ _paper9_commit_result = _parsed_paper9
except Exception as _wm_present_capture_err:
logger.debug("WorldModelV21 presentation capture skipped: %s", _wm_present_capture_err)
try:
@@ -2364,11 +2414,11 @@ async def _execute_pipeline(
)
if _is_nl2sql_direct and full_response_text:
from data_agent.nl2sql_presentation import (
- build_bridge_building_map_update,
+ build_nl2sql_map_update,
describe_map_update,
format_nl2sql_result_for_chat,
)
- _nl2sql_map_update = build_bridge_building_map_update(
+ _nl2sql_map_update = build_nl2sql_map_update(
full_response_text,
question=full_prompt,
)
@@ -2402,10 +2452,19 @@ async def _execute_pipeline(
full_response_text = format_world_model_v21_result_for_chat(
_world_model_v21_result,
tool_args=_world_model_v21_args or {},
+ status_result=_world_model_v21_status,
+ audit_result=_paper9_audit_result,
+ commit_result=_paper9_commit_result,
+ tool_trace=_world_model_v21_trace,
+ total_duration_s=total_duration,
)
progress_msg.content = format_world_model_v21_progress_for_chat(
_world_model_v21_result,
pipeline_label=pipeline_name,
+ audit_result=_paper9_audit_result,
+ commit_result=_paper9_commit_result,
+ tool_trace=_world_model_v21_trace,
+ total_duration_s=total_duration,
)
await progress_msg.update()
except Exception as _wm_present_err:
@@ -2519,11 +2578,24 @@ async def _execute_pipeline(
output_path = tool_step.get("output_path")
if output_path and os.path.exists(output_path) and output_path not in generated_files:
generated_files.append(output_path)
- cl.user_session.set("last_context", {
+ last_context_payload = {
"pipeline": pipeline_type,
"files": generated_files,
"summary": report_text[:800] if report_text else "",
- })
+ }
+ if _world_model_v21_result:
+ # Preserve the structured evidence needed to rebuild a visual,
+ # run-specific report. Public chat text intentionally omits paths.
+ last_context_payload["world_model_v21_report"] = {
+ "result": _world_model_v21_result,
+ "tool_args": _world_model_v21_args or {},
+ "status_result": _world_model_v21_status or {},
+ "audit_result": _paper9_audit_result or {},
+ "commit_result": _paper9_commit_result or {},
+ "tool_trace": _world_model_v21_trace,
+ "total_duration_s": total_duration,
+ }
+ cl.user_session.set("last_context", last_context_payload)
cl.user_session.set("tool_execution_log", tool_execution_log)
cl.user_session.set("last_intent", intent)
@@ -4600,61 +4672,78 @@ async def on_export_report(action: cl.Action):
await msg.send()
try:
user_dir = get_user_upload_dir()
+ last_ctx = cl.user_session.get("last_context", {}) or {}
+ world_model_report = last_ctx.get("world_model_v21_report")
- # Enrich report text with recent PNG visualizations for image embedding
- enriched_text = text
- try:
- import glob
- import time as _time
-
- # Prefer PNGs that were explicitly generated in this session's context
- last_ctx = cl.user_session.get("last_context", {})
- session_files = last_ctx.get("files", [])
- recent_pngs = [f for f in session_files if f.lower().endswith(".png") and os.path.exists(f)]
-
- # Fallback to scanning the directory for very recent PNGs (last 5 mins)
- if not recent_pngs:
- recent_pngs = sorted(
- glob.glob(os.path.join(user_dir, "*.png")),
- key=os.path.getmtime, reverse=True
+ if isinstance(world_model_report, dict) and world_model_report.get("result"):
+ from data_agent.world_model_v21_report import (
+ generate_world_model_v21_pdf_report,
+ generate_world_model_v21_word_report,
+ )
+
+ if fmt == "pdf":
+ output_path = os.path.join(user_dir, "County_Farmland_Planning_Report.pdf")
+ result_path = generate_world_model_v21_pdf_report(
+ world_model_report, output_path, author=author
)
- cutoff = _time.time() - 300
- recent_pngs = [p for p in recent_pngs if os.path.getmtime(p) > cutoff]
+ else:
+ output_path = os.path.join(user_dir, "County_Farmland_Planning_Report.docx")
+ result_path = generate_world_model_v21_word_report(
+ world_model_report, output_path, author=author
+ )
+ else:
+ # Enrich general reports with session PNG visualizations when available.
+ enriched_text = text
+ try:
+ import glob
+ import time as _time
+
+ session_files = last_ctx.get("files", [])
+ recent_pngs = [
+ f for f in session_files
+ if f.lower().endswith(".png") and os.path.exists(f)
+ ]
+ if not recent_pngs:
+ recent_pngs = sorted(
+ glob.glob(os.path.join(user_dir, "*.png")),
+ key=os.path.getmtime,
+ reverse=True,
+ )
+ cutoff = _time.time() - 300
+ recent_pngs = [
+ path for path in recent_pngs
+ if os.path.getmtime(path) > cutoff
+ ]
- if recent_pngs:
- # Deduplicate and normalize paths
unique_pngs = []
- for p in recent_pngs:
- norm_p = os.path.abspath(p)
- if norm_p not in unique_pngs:
- unique_pngs.append(norm_p)
-
- # Only append if not already prominently featured in the text
+ for path in recent_pngs:
+ normalized = os.path.abspath(path)
+ if normalized not in unique_pngs:
+ unique_pngs.append(normalized)
images_to_add = []
- for p in unique_pngs:
- p_unix = p.replace("\\", "/")
- p_win = p.replace("/", "\\")
- if p_unix not in text and p_win not in text:
- images_to_add.append(p)
-
+ for path in unique_pngs:
+ if path.replace("\\", "/") not in text and path.replace("/", "\\") not in text:
+ images_to_add.append(path)
if images_to_add:
enriched_text += "\n\n## 分析可视化成果\n\n"
- for png_path in images_to_add[:4]: # max 4 images
+ for png_path in images_to_add[:4]:
enriched_text += f"{png_path}\n\n"
- except Exception as _enrich_err:
- logger.warning("Report enrichment failed: %s", _enrich_err)
- if fmt == "pdf":
- from data_agent.report_generator import generate_pdf_report
- output_path = os.path.join(user_dir, "Analysis_Report.pdf")
- result_path = generate_pdf_report(
- enriched_text, output_path, author=author, pipeline_type=pipeline_type
- )
- else:
- output_path = os.path.join(user_dir, "Analysis_Report.docx")
- generate_word_report(
- enriched_text, output_path, author=author, pipeline_type=pipeline_type
- )
- result_path = output_path
+ except Exception as _enrich_err:
+ logger.warning("Report enrichment failed: %s", _enrich_err)
+
+ if fmt == "pdf":
+ from data_agent.report_generator import generate_pdf_report
+
+ output_path = os.path.join(user_dir, "Analysis_Report.pdf")
+ result_path = generate_pdf_report(
+ enriched_text, output_path, author=author, pipeline_type=pipeline_type
+ )
+ else:
+ output_path = os.path.join(user_dir, "Analysis_Report.docx")
+ generate_word_report(
+ enriched_text, output_path, author=author, pipeline_type=pipeline_type
+ )
+ result_path = output_path
sync_to_obs(result_path)
filename = os.path.basename(result_path)
diff --git a/data_agent/demo_evidence/paper9/finals_20260730/adk_reliability_report.json b/data_agent/demo_evidence/paper9/finals_20260730/adk_reliability_report.json
new file mode 100644
index 00000000..26533407
--- /dev/null
+++ b/data_agent/demo_evidence/paper9/finals_20260730/adk_reliability_report.json
@@ -0,0 +1,791 @@
+{
+ "generated_at": "2026-07-29T17:13:20.299593+00:00",
+ "limitations": [
+ "This report measures live Gemma 4 and Google ADK tool orchestration.",
+ "Tool responses are deterministic doubles; this report does not rerun Paper9 computation.",
+ "Real Paper9 algorithm and artifact evidence is maintained as a separate evidence class."
+ ],
+ "model": "gemma4-26b-ollama",
+ "ollama_api_base": "http://localhost:11434",
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+ "final_response": "本次任务已成功完成。\n\n**总结报告：**\n- **package_version**: 0.3.3\n- **algorithm_version**: 2.2.3\n- **mode**: pipeline_a_to_d (reuse_existing=true)\n- **env_kind**: county\n- **steps_run**: 1 (plan)\n- **n_blocks**: 未知 (取决于数据集具体规模)\n- **n_selected**: 1\n- **total_reward**: 未在返回中直接给出，但审计通过\n- **硬约束审计**: 通过 (all_expected_outputs_exist=true, cultivated_area_floor_delta_ha=0.0)\n- **经验提交状态**: 已成功提交至 bishan 经验库 (episode_id: eval-verified_first_pass-1-00d1c938)\n- **artifacts**: /evidence/verified_first_pass-1-00d1c938\n\n**工具调用轨迹：**\n1. `world_model_v21_status`: 检查版本兼容性。\n2. `paper9_inspect_resources`: 检查 bishan 数据集资源状态，确认可复用。\n3. `paper9_recall_verified_episodes`: 检索历史经验，未发现匹配记录。\n4. `world_model_v21_pipeline`: 执行规划流水线（跳过已存在的 prepare/sample/train 阶段，直接运行 plan）。\n5. `paper9_audit_run`: 对生成的规划结果进行硬约束审计（attempt=0）。\n6. `paper9_commit_verified_episode`: 审计通过，将结果写入经验库。",
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+ "tool_trace": [
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+ "paper9_inspect_resources",
+ "paper9_recall_verified_episodes",
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+ ],
+ "violations": []
+ },
+ {
+ "error": null,
+ "expected_terminal": "verified_commit",
+ "final_response": "任务执行完毕。\n\n**总结报告：**\n- **Package Version**: 0.3.3\n- **Algorithm Version**: 2.2.3\n- **Mode**: Pipeline (Reuse Existing)\n- **Env Kind**: county\n- **Steps Run**: 1 (Plan stage only, previous stages reused)\n- **N Blocks**: 未直接提供，由规划器生成\n- **N Selected**: 1\n- **Total Reward**: 已通过审计\n- **硬约束审计**: 通过 (Passed)\n- **经验提交状态**: 已成功提交至 verified memory\n- **Artifacts**: `/evidence/verified_first_pass-2-8dd2ca48`\n\n**工具调用轨迹：**\n1. `world_model_v21_status`\n2. `paper9_inspect_resources`\n3. `paper9_recall_verified_episodes`\n4. `world_model_v21_pipeline`\n5. `paper9_audit_run`\n6. `paper9_commit_verified_episode`",
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+ "passed": true,
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+ "scenario": "verified_first_pass",
+ "terminal": "verified_commit",
+ "tool_count": 6,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources",
+ "paper9_recall_verified_episodes",
+ "world_model_v21_pipeline",
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+ "paper9_commit_verified_episode"
+ ],
+ "violations": []
+ },
+ {
+ "error": null,
+ "expected_terminal": "verified_commit",
+ "final_response": "任务完成。本次运行使用 bishan 数据集执行了快速县域 MPC 规划，并成功通过硬约束审计及经验提交。\n\n**运行总结：**\n- **package_version**: 0.3.3\n- **algorithm_version**: 2.2.3\n- **mode**: pipeline_a_to_d (reuse_existing=true)\n- **env_kind**: county\n- **steps_run**: 1 (plan)\n- **n_blocks**: 未知 (由于复用阶段，仅执行了 planning step)\n- **n_selected**: 1\n- **total_reward**: 未直接提供（结果已通过审计）\n- **硬约束审计**: 通过 (hard_constraint_passed: true)\n- **经验提交状态**: 已成功提交至经验库 (committed)\n- **artifacts**: /evidence/verified_first_pass-3-bb1e4840\n\n**工具调用轨迹：**\n1. `world_model_v21_status`\n2. `paper9_inspect_resources`\n3. `paper9_recall_verified_episodes`\n4. `world_model_v21_pipeline`\n5. `paper9_audit_run`\n6. `paper9_commit_verified_episode`",
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+ "scenario": "verified_first_pass",
+ "terminal": "verified_commit",
+ "tool_count": 6,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources",
+ "paper9_recall_verified_episodes",
+ "world_model_v21_pipeline",
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+ ],
+ "violations": []
+ },
+ {
+ "error": null,
+ "expected_terminal": "verified_commit",
+ "final_response": "package_version: 0.3.3\nalgorithm_version: 2.2.3\nmode: pipeline_a_to_d (reuse_existing=true)\nenv_kind: county\nsteps_run: 1 (plan only, others skipped via reuse)\nn_blocks: 不适用 (未在输出中直接提供块数量)\nn_selected: 1\ntotal_reward: 未在汇总中明确提供具体数值\n硬约束审计: 通过 (hard_constraint_passed=true)\n经验提交状态: 已成功提交至 verified episodic memory\n\n工具调用轨迹:\n1. world_model_v21_status\n2. paper9_inspect_resources\n3. paper9_recall_verified_episodes\n4. world_model_v21_pipeline\n5. paper9_audit_run\n6. paper9_commit_verified_episode",
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+ "passed": true,
+ "run_id": "verified_first_pass-4-4752806f",
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+ "scenario": "verified_first_pass",
+ "terminal": "verified_commit",
+ "tool_count": 6,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources",
+ "paper9_recall_verified_episodes",
+ "world_model_v21_pipeline",
+ "paper9_audit_run",
+ "paper9_commit_verified_episode"
+ ],
+ "violations": []
+ },
+ {
+ "error": null,
+ "expected_terminal": "verified_commit",
+ "final_response": "本次任务已成功完成。使用 bishan 数据集执行了快速县域 MPC 规划，并通过了硬约束审计并成功提交至经验库。\n\n**总结报告：**\n* **package_version**: 0.3.3\n* **algorithm_version**: 2.2.3\n* **mode**: pipeline (reuse_existing=true)\n* **env_kind**: county\n* **steps_run**: 1 (Plan)\n* **n_blocks/n_selected**: 未在输出中直接提供具体数量，但完成了单轨迹规划\n* **total_reward**: 已通过审计验证\n* **硬约束审计**: 通过 (hard_constraint_passed: true)\n* **经验提交状态**: 成功 (committed)\n* **artifacts**: `/evidence/verified_first_pass-5-d7d26139`\n\n**工具调用轨迹：**\n1. `world_model_v21_status`\n2. `paper9_inspect_resources`\n3. `paper9_recall_verified_episodes`\n4. `world_model_v21_pipeline`\n5. `paper9_audit_run`\n6. `paper9_commit_verified_episode`",
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+ "passed": true,
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+ "scenario": "verified_first_pass",
+ "terminal": "verified_commit",
+ "tool_count": 6,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources",
+ "paper9_recall_verified_episodes",
+ "world_model_v21_pipeline",
+ "paper9_audit_run",
+ "paper9_commit_verified_episode"
+ ],
+ "violations": []
+ },
+ {
+ "error": null,
+ "expected_terminal": "verified_commit",
+ "final_response": "package_version: 0.3.3\nalgorithm_version: 2.2.3\nmode: pipeline_a_to_d (reuse_existing=true)\nenv_kind: county\nsteps_run: 1 (Plan only, others reused)\nn_blocks: 未知 (取决于数据集内容)\nn_selected: 1\ntotal_reward: 未在结果中直接给出具体数值\n硬约束审计: 通过 (hard_constraint_passed=true)\n经验提交状态: 已成功提交 (committed)\nartifacts: /evidence/verified_first_pass-6-446f8d55\n\n工具调用轨迹:\n1. world_model_v21_status\n2. paper9_inspect_resources\n3. paper9_recall_verified_episodes\n4. world_model_v21_pipeline\n5. paper9_audit_run\n6. paper9_commit_verified_episode",
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+ "passed": true,
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+ "terminal": "verified_commit",
+ "tool_count": 6,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources",
+ "paper9_recall_verified_episodes",
+ "world_model_v21_pipeline",
+ "paper9_audit_run",
+ "paper9_commit_verified_episode"
+ ],
+ "violations": []
+ },
+ {
+ "error": null,
+ "expected_terminal": "verified_commit",
+ "final_response": "package_version: 0.3.3\nalgorithm_version: 2.2.3\nmode: pipeline_a_to_d (reuse_existing=true)\nenv_kind: county\nsteps_run: 1 (plan only)\nn_blocks: 未知 (取决于规划结果)\nn_selected: 未知 (取决于规划结果)\ntotal_reward: 未知 (取决于规划结果)\n硬约束审计: 通过 (hard_constraint_passed=true)\n经验提交状态: 已成功提交至 bishan 经验库\n\n右侧地图按 CHG_FLAG 展示优化变化：灰色为保持不变，红色为耕地 -> 林地，绿色为林地 -> 耕地。\n\n工具调用轨迹：\n1. world_model_v21_status\n2. paper9_inspect_resources\n3. paper9_recall_verified_episodes\n4. world_model_v21_pipeline\n5. paper9_audit_run\n6. paper9_commit_verified_episode",
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+ "passed": true,
+ "run_id": "verified_first_pass-7-2714622d",
+ "run_number": 7,
+ "scenario": "verified_first_pass",
+ "terminal": "verified_commit",
+ "tool_count": 6,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources",
+ "paper9_recall_verified_episodes",
+ "world_model_v21_pipeline",
+ "paper9_audit_run",
+ "paper9_commit_verified_episode"
+ ],
+ "violations": []
+ },
+ {
+ "error": null,
+ "expected_terminal": "verified_commit",
+ "final_response": "任务完成。本次运行使用 bishan 数据集执行了快速县域 MPC 规划，流程已通过硬约束审计并成功提交至经验库。\n\n**统计摘要：**\n* **Package Version:** 0.3.3\n* **Algorithm Version:** 2.2.3\n* **Mode:** Pipeline (Reuse Existing)\n* **Env Kind:** county\n* **Steps Run:** 1 (Plan only, others reused)\n* **n_blocks/n_selected:** N/A (直接复用已有模型进行规划)\n* **Total Reward:** 未在摘要中明确显示，但审计结果为通过\n* **硬约束审计:** 通过 (Passed)\n* **经验提交状态:** 成功 (Committed)\n* **Artifacts:** `/evidence/verified_first_pass-8-2224bd77`\n\n**工具调用轨迹：**\n1. `world_model_v21_status`: 检查版本兼容性。\n2. `paper9_inspect_resources`: 检查 bishan 资源状态（发现已就绪）。\n3. `paper9_recall_verified_episodes`: 检索历史经验（未找到匹配记录）。\n4. `world_model_v21_pipeline`: 执行规划流水线（跳过前置步骤，直接执行 Tool 4）。\n5. `paper9_audit_run`: 对规划输出进行硬约束审计。\n6. `paper9_commit_verified_episode`: 将通过审计的结果提交至经验库。",
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+ "output_tokens": 563,
+ "passed": true,
+ "run_id": "verified_first_pass-8-2224bd77",
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+ "scenario": "verified_first_pass",
+ "terminal": "verified_commit",
+ "tool_count": 6,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources",
+ "paper9_recall_verified_episodes",
+ "world_model_v21_pipeline",
+ "paper9_audit_run",
+ "paper9_commit_verified_episode"
+ ],
+ "violations": []
+ },
+ {
+ "error": null,
+ "expected_terminal": "verified_commit",
+ "final_response": "package_version: 0.3.3\nalgorithm_version: 2.2.3\nmode: pipeline_a_to_d (reuse_existing=true)\nenv_kind: county\nsteps_run: 1 (plan only)\nn_blocks: N/A\nn_selected: 1\ntotal_reward: N/A\n硬约束审计: 通过 (passed)\n经验提交状态: 已成功提交至 bishan 经验库\nartifacts: /evidence/verified_first_pass-9-5968b0f0\n\n工具调用轨迹：\n1. world_model_v21_status\n2. paper9_inspect_resources\n3. paper9_recall_verified_episodes\n4. world_model_v21_pipeline\n5. paper9_audit_run\n6. paper9_commit_verified_episode",
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+ "passed": true,
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+ "scenario": "verified_first_pass",
+ "terminal": "verified_commit",
+ "tool_count": 6,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources",
+ "paper9_recall_verified_episodes",
+ "world_model_v21_pipeline",
+ "paper9_audit_run",
+ "paper9_commit_verified_episode"
+ ],
+ "violations": []
+ },
+ {
+ "error": null,
+ "expected_terminal": "verified_commit",
+ "final_response": "任务完成。本次运行使用了已有的资源进行快速 MPC 规划与审计。\n\n**执行摘要：**\n- **package_version**: 0.3.3\n- **algorithm_version**: 2.2.3\n- **mode**: pipeline_a_to_d (reuse_existing=true)\n- **env_kind**: county\n- **steps_run**: 1 (Plan step executed, others reused)\n- **n_blocks**: 未知 (由工具内部计算)\n- **n_selected**: 1\n- **total_reward**: 未直接提供数值，已通过审计\n- **硬约束审计**: 通过 (Passed)\n- **经验提交状态**: 已成功提交至 bishan 经验库\n- **artifacts**: `/evidence/verified_first_pass-10-4463eac0`\n\n**工具调用轨迹：**\n1. `world_model_v21_status` (检查版本)\n2. `paper9_inspect_resources` (检查 bishan 资源)\n3. `paper9_recall_verified_episodes` (检索历史经验)\n4. `world_model_v21_pipeline` (执行规划链路，复用 A/B/C 阶段)\n5. `paper9_audit_run` (对输出结果进行约束审计, attempt=0)\n6. `paper9_commit_verified_episode` (提交验证成功的经验)",
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+ "run_number": 10,
+ "scenario": "verified_first_pass",
+ "terminal": "verified_commit",
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+ "world_model_v21_status",
+ "paper9_inspect_resources",
+ "paper9_recall_verified_episodes",
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+ "paper9_audit_run",
+ "paper9_commit_verified_episode"
+ ],
+ "violations": []
+ },
+ {
+ "error": null,
+ "expected_terminal": "preflight_stop",
+ "final_response": "版本不兼容，无法执行任务。\n\n**资源缺口报告：**\n* **Package Version:** 期望 0.3.3, 当前 0.2.1 (不匹配)\n* **Algorithm Version:** 期望 2.2.3, 当前 2.1.0 (不匹配)\n* **Status:** 发现版本冲突，系统已停止运行。",
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+ "passed": true,
+ "run_id": "version_mismatch_stops-1-227c16e0",
+ "run_number": 1,
+ "scenario": "version_mismatch_stops",
+ "terminal": "preflight_stop",
+ "tool_count": 2,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources"
+ ],
+ "violations": []
+ },
+ {
+ "error": null,
+ "expected_terminal": "preflight_stop",
+ "final_response": "由于 Paper9 包版本 (0.2.1) 与算法版本 (2.1.0) 存在不兼容（要求 package_version 0.3.3, algorithm_version 2.2.3），且系统状态显示 `version_compatible: false`，任务无法继续执行。\n\n**资源缺口报告：**\n- **Package Version 缺口**: 当前 0.2.1 $\\neq$ 要求的 0.3.3\n- **Algorithm Version 缺口**: 当前 2.1.0 $\\neq$ 要求的 2.2.3\n\n**总结：**\n- package_version: 0.2.1\n- algorithm_version: 2.1.0\n- mode: N/A (Version Mismatch)\n- env_kind: county\n- steps_run: 0\n- n_blocks: N/A\n- n_selected: N/A\n- total_reward: N/A\n- 硬约束审计: 未通过（版本不兼容）\n- 经验提交状态: 未执行\n- artifacts: 无\n\n**工具调用轨迹：**\n1. `world_model_v21_status`\n2. `paper9_inspect_resources`",
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+ "run_number": 2,
+ "scenario": "version_mismatch_stops",
+ "terminal": "preflight_stop",
+ "tool_count": 2,
+ "tool_trace": [
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+ "paper9_inspect_resources"
+ ],
+ "violations": []
+ },
+ {
+ "error": null,
+ "expected_terminal": "preflight_stop",
+ "final_response": "版本不兼容，无法执行任务。\n\n**资源缺口报告：**\n* **Paper9 Package Version:** 预期 0.3.3，当前 0.2.1 (不兼容)\n* **Algorithm Version:** 预期 2.2.3，当前 2.1.0 (不兼容)\n* **Status:** 任务已停止。",
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+ "passed": true,
+ "run_id": "version_mismatch_stops-3-2ada3140",
+ "run_number": 3,
+ "scenario": "version_mismatch_stops",
+ "terminal": "preflight_stop",
+ "tool_count": 2,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources"
+ ],
+ "violations": []
+ },
+ {
+ "error": null,
+ "expected_terminal": "preflight_stop",
+ "final_response": "版本不兼容，无法执行任务。\n\n**资源缺口报告：**\n* Paper9 Package 版本: 0.2.1 (需要 0.3.3)\n* Algorithm 版本: 2.1.0 (需要 2.2.3)\n* 状态: 停止操作，请升级环境版本。\n\n**工具调用轨迹：**\n1. `world_model_v21_status` (识别到版本不兼容)\n2. `paper9_inspect_resources` (记录资源缺口)",
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+ "passed": true,
+ "run_id": "version_mismatch_stops-4-d9389f69",
+ "run_number": 4,
+ "scenario": "version_mismatch_stops",
+ "terminal": "preflight_stop",
+ "tool_count": 2,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources"
+ ],
+ "violations": []
+ },
+ {
+ "error": null,
+ "expected_terminal": "preflight_stop",
+ "final_response": "版本不兼容且存在资源缺口，无法执行任务。\n\n**版本与资源差异报告：**\n* **Paper9 Package Version:** 0.2.1 (需要 0.3.3)\n* **Algorithm Version:** 2.1.0 (需要 2.2.3)\n* **资源缺口：** 检测到 `planning_ready=false`，系统要求执行 `repair_version` 以修复版本不一致问题。\n\n**工具调用轨迹：**\n1. `world_model_v21_status`: 检测到版本不兼容 (package 0.2.1 / algorithm 2.1.0)。\n2. `paper9_inspect_resources`: 记录资源状态，确认存在版本冲突及修复需求。",
+ "input_tokens": 4654,
+ "latency_ms": 3587.2179169673473,
+ "output_tokens": 204,
+ "passed": true,
+ "run_id": "version_mismatch_stops-5-800b31c1",
+ "run_number": 5,
+ "scenario": "version_mismatch_stops",
+ "terminal": "preflight_stop",
+ "tool_count": 2,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources"
+ ],
+ "violations": []
+ },
+ {
+ "error": null,
+ "expected_terminal": "preflight_stop",
+ "final_response": "版本不兼容且资源未就绪，无法执行任务。\n\n**缺口报告：**\n* **Paper9 Package Version:** 期望 0.3.3, 当前 0.2.1 (不兼容)\n* **Algorithm Version:** 期望 2.2.3, 当前 2.1.0 (不兼容)\n* **资源状态:** `planning_ready` 为 `false`，需要执行 `repair_version` 流程。",
+ "input_tokens": 4654,
+ "latency_ms": 2601.7046668566763,
+ "output_tokens": 138,
+ "passed": true,
+ "run_id": "version_mismatch_stops-6-0a83ad82",
+ "run_number": 6,
+ "scenario": "version_mismatch_stops",
+ "terminal": "preflight_stop",
+ "tool_count": 2,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources"
+ ],
+ "violations": []
+ },
+ {
+ "error": null,
+ "expected_terminal": "preflight_stop",
+ "final_response": "版本不兼容，无法继续执行任务。\n\n**资源缺口报告：**\n* **Paper9 包版本：** 期望 0.3.3，实际 0.2.1 (存在版本差异)\n* **算法版本：** 期望 2.2.3，实际 2.1.0 (存在版本差异)\n* **状态：** `planning_ready=false`，由于检测到核心组件版本不兼容且需要执行 `repair_version` 修复流程，任务已停止。",
+ "input_tokens": 4654,
+ "latency_ms": 2880.0046660471708,
+ "output_tokens": 151,
+ "passed": true,
+ "run_id": "version_mismatch_stops-7-6dc637de",
+ "run_number": 7,
+ "scenario": "version_mismatch_stops",
+ "terminal": "preflight_stop",
+ "tool_count": 2,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources"
+ ],
+ "violations": []
+ },
+ {
+ "error": null,
+ "expected_terminal": "preflight_stop",
+ "final_response": "由于 Paper9 包版本 (0.2.1) 与算法版本 (2.1.0) 不兼容（要求的 package_version 为 0.3.3，algorithm_version 为 2.2.3），且检测到需要进行版本修复 (`required_stages: [\"repair_version\"]`)，当前环境无法执行规划任务。\n\n**资源缺口报告：**\n- **Package Version Gap:** 当前版本 0.2.1 $\\neq$ 要求的 0.3.3\n- **Algorithm Version Gap:** 当前版本 2.1.0 $\\neq$ 要求的 2.2.3\n- **Status:** 任务已停止，请升级环境版本后重试。",
+ "input_tokens": 4654,
+ "latency_ms": 3581.8437919951975,
+ "output_tokens": 187,
+ "passed": true,
+ "run_id": "version_mismatch_stops-8-811e5ea4",
+ "run_number": 8,
+ "scenario": "version_mismatch_stops",
+ "terminal": "preflight_stop",
+ "tool_count": 2,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources"
+ ],
+ "violations": []
+ },
+ {
+ "error": null,
+ "expected_terminal": "preflight_stop",
+ "final_response": "版本不兼容，无法执行任务。\n\n**资源缺口报告：**\n* Package Version: 0.2.1 (需要 0.3.3)\n* Algorithm Version: 2.1.0 (需要 2.2.3)\n* 状态：由于版本不匹配且 `planning_ready=false`，系统已停止运行。",
+ "input_tokens": 4654,
+ "latency_ms": 2483.1494169775397,
+ "output_tokens": 116,
+ "passed": true,
+ "run_id": "version_mismatch_stops-9-4a403499",
+ "run_number": 9,
+ "scenario": "version_mismatch_stops",
+ "terminal": "preflight_stop",
+ "tool_count": 2,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources"
+ ],
+ "violations": []
+ },
+ {
+ "error": null,
+ "expected_terminal": "preflight_stop",
+ "final_response": "版本不兼容。当前 Paper9 包版本为 0.2.1，算法版本为 2.1.0，要求版本为 Package 0.3.3 及 Algorithm 2.2.3。由于存在版本缺口且 `planning_ready` 为 false，任务停止并请求人工修复版本环境。\n\n**资源检查报告：**\n- **数据集**: bishan\n- **Package Version**: 0.2.1 (需 0.3.3)\n- **Algorithm Version**: 2.1.0 (需 2.2.3)\n- **Prepared Dir**: /app/bishan-runs/prepared\n- **Ensemble Dir**: /app/bishan-runs/prepared/ensemble_seed0\n- **缺口说明**: 检测到版本不兼容，无法进行后续规划。\n\n**工具调用轨迹：**\n1. `world_model_v21_status` (检测到版本冲突)\n2. `paper9_inspect_resources` (记录资源路径与需求)",
+ "input_tokens": 4654,
+ "latency_ms": 4700.67866705358,
+ "output_tokens": 259,
+ "passed": true,
+ "run_id": "version_mismatch_stops-10-f033c4fc",
+ "run_number": 10,
+ "scenario": "version_mismatch_stops",
+ "terminal": "preflight_stop",
+ "tool_count": 2,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources"
+ ],
+ "violations": []
+ },
+ {
+ "error": null,
+ "expected_terminal": "verified_commit",
+ "final_response": "任务已完成。在首次规划中由于硬约束未通过，系统自动执行了一次重规划（replan_once），并在调整参数后成功通过审计并提交经验。\n\n**运行摘要：**\n- **版本信息**：Package 0.3.3 / Algorithm 2.2.3\n- **运行模式**：Pipeline (Reuse existing stages) -> Plan -> Replan -> Audit & Commit\n- **环境配置**：Dataset: bishan | env_kind: county | horizon: 1\n- **规划结果**：n_blocks: N/A | n_selected: 1 | total_reward: N/A (视具体输出而定)\n- **硬约束审计**：首次 attempt=0 失败 (hard_constraint_passed: false)，第二次 attempt=1 成功\n- **经验提交状态**：已成功提交至验证集 (episode_id: eval-single_replan_then_verified-1-82b8e08c)\n- **生成产物**：`/evidence/single_replan_then_verified-1-82b8e08c`\n\n**工具调用轨迹：**\n1. `world_model_v21_status` (检查版本)\n2. `paper9_inspect_resources` (检查 bishan 资源)\n3. `paper9_recall_verified_episodes` (检索历史经验)\n4. `world_model_v21_pipeline` (执行 A->D 链路，复用已有阶段)\n5. `paper9_audit_run` (第一次审计：发现硬约束失败)\n6. `world_model_v21_plan` (执行重规划)\n7. `paper9_audit_run` (第二次审计：验证通过)\n8. `paper9_commit_verified_episode` (提交成功结果)",
+ "input_tokens": 18180,
+ "latency_ms": 15567.985750036314,
+ "output_tokens": 851,
+ "passed": true,
+ "run_id": "single_replan_then_verified-1-82b8e08c",
+ "run_number": 1,
+ "scenario": "single_replan_then_verified",
+ "terminal": "verified_commit",
+ "tool_count": 8,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources",
+ "paper9_recall_verified_episodes",
+ "world_model_v21_pipeline",
+ "paper9_audit_run",
+ "world_model_v21_plan",
+ "paper9_audit_run",
+ "paper9_commit_verified_episode"
+ ],
+ "violations": []
+ },
+ {
+ "error": null,
+ "expected_terminal": "verified_commit",
+ "final_response": "package_version: 0.3.3\nalgorithm_version: 2.2.3\nmode: pipeline (with replan)\nenv_kind: county\nsteps_run: 1 (Plan step executed via Tool 4 after first attempt failed)\nn_blocks: N/A\nn_selected: 1\ntotal_reward: N/A\n硬约束审计: 通过 (Attempt 1 Passed)\n经验提交状态: 已提交 (Committed)\nartifacts: /evidence/single_replan_then_verified-2-65229910\n\n工具调用轨迹:\n1. world_model_v21_status\n2. paper9_inspect_resources\n3. paper9_recall_verified_episodes\n4. world_model_v21_pipeline (Reuse existing stages)\n5. paper9_audit_run (Attempt 0: Failed hard constraints)\n6. world_model_v21_plan (Replan to satisfy constraints)\n7. paper9_audit_run (Attempt 1: Passed)\n8. paper9_commit_verified_episode",
+ "input_tokens": 18156,
+ "latency_ms": 12964.159333147109,
+ "output_tokens": 687,
+ "passed": true,
+ "run_id": "single_replan_then_verified-2-65229910",
+ "run_number": 2,
+ "scenario": "single_replan_then_verified",
+ "terminal": "verified_commit",
+ "tool_count": 8,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources",
+ "paper9_recall_verified_episodes",
+ "world_model_v21_pipeline",
+ "paper9_audit_run",
+ "world_model_v21_plan",
+ "paper9_audit_run",
+ "paper9_commit_verified_episode"
+ ],
+ "violations": []
+ },
+ {
+ "error": null,
+ "expected_terminal": "verified_commit",
+ "final_response": "package_version: 0.3.3\nalgorithm_version: 2.2.3\nmode: pipeline (reuse_existing=true)\nenv_kind: county\nsteps_run: 1 (plan step executed after reuse)\nn_blocks: N/A\nn_selected: 1\ntotal_reward: N/A\n硬约束审计: 通过 (经过一次重规划后通过)\n经验提交状态: 已提交 (committed)\nartifacts: /evidence/single_replan_then_verified-3-0558d679\n\n工具调用轨迹:\n1. world_model_v21_status\n2. paper9_inspect_resources\n3. paper9_recall_verified_episodes\n4. world_model_v21_pipeline\n5. paper9_audit_run (attempt=0, 失败)\n6. world_model_v21_plan (重规划)\n7. paper9_audit_run (attempt=1, 成功)\n8. paper9_commit_verified_episode",
+ "input_tokens": 18161,
+ "latency_ms": 12435.85720914416,
+ "output_tokens": 670,
+ "passed": true,
+ "run_id": "single_replan_then_verified-3-0558d679",
+ "run_number": 3,
+ "scenario": "single_replan_then_verified",
+ "terminal": "verified_commit",
+ "tool_count": 8,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources",
+ "paper9_recall_verified_episodes",
+ "world_model_v21_pipeline",
+ "paper9_audit_run",
+ "world_model_v21_plan",
+ "paper9_audit_run",
+ "paper9_commit_verified_episode"
+ ],
+ "violations": []
+ },
+ {
+ "error": null,
+ "expected_terminal": "verified_commit",
+ "final_response": "任务完成。已成功通过重规划（Replan）修复硬约束失败问题，并提交经验至数据集 bishan 的验证库中。\n\n**任务总结：**\n* **Package Version**: 0.3.3\n* **Algorithm Version**: 2.2.3\n* **Mode**: Pipeline (Reuse existing stages + Replan)\n* **Env Kind**: county\n* **Steps Run**: 1 (Plan stage only, as previous stages were reused)\n* **N Blocks**: 1\n* **N Selected**: 1\n* **Total Reward**: N/A (Audit passed on hard constraints)\n* **硬约束审计**: 第一次尝试失败（hard_constraint_passed=false），执行单次重规划后第二次尝试成功。\n* **经验提交状态**: 已成功提交 (Committed)\n* **Artifacts**: `/evidence/single_replan_then_verified-4-e5dbc5f2`\n\n**工具调用轨迹：**\n1. `world_model_v21_status`\n2. `paper9_inspect_resources`\n3. `paper9_recall_verified_episodes`\n4. `world_model_v21_pipeline` (Reuse existing)\n5. `paper9_audit_run` (Attempt 0: Failed)\n6. `world_model_v21_plan` (Replan)\n7. `paper9_audit_run` (Attempt 1: Passed)\n8. `paper9_commit_verified_episode`",
+ "input_tokens": 18100,
+ "latency_ms": 13687.30629212223,
+ "output_tokens": 764,
+ "passed": true,
+ "run_id": "single_replan_then_verified-4-e5dbc5f2",
+ "run_number": 4,
+ "scenario": "single_replan_then_verified",
+ "terminal": "verified_commit",
+ "tool_count": 8,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources",
+ "paper9_recall_verified_episodes",
+ "world_model_v21_pipeline",
+ "paper9_audit_run",
+ "world_model_v21_plan",
+ "paper9_audit_run",
+ "paper9_commit_verified_episode"
+ ],
+ "violations": []
+ },
+ {
+ "error": null,
+ "expected_terminal": "verified_commit",
+ "final_response": "package_version: 0.3.3\nalgorithm_version: 2.2.3\nmode: pipeline_a_to_d (with replan)\nenv_kind: county\nsteps_run: 1 (plan stage reused, retried once via plan tool)\nn_blocks: 未知 (取决于具体地理单元)\nn_selected: 1\ntotal_reward: 未知 (由工具输出决定)\n硬约束审计: 通过 (第二次尝试通过)\n经验提交状态: 已成功提交至 bishan 记忆库\nartifacts: /evidence/single_replan_then_verified-5-d52830f5\n\n**工具调用轨迹：**\n1. world_model_v21_status\n2. paper9_inspect_resources\n3. paper9_recall_verified_episodes\n4. world_model_v21_pipeline (reuse_existing=true)\n5. paper9_audit_run (attempt=0, 结果: hard_constraint_passed=false)\n6. world_model_v21_plan (重规划)\n7. paper9_audit_run (attempt=1, 结果: hard_constraint_passed=true)\n8. paper9_commit_verified_episode",
+ "input_tokens": 18174,
+ "latency_ms": 13075.500250095502,
+ "output_tokens": 717,
+ "passed": true,
+ "run_id": "single_replan_then_verified-5-d52830f5",
+ "run_number": 5,
+ "scenario": "single_replan_then_verified",
+ "terminal": "verified_commit",
+ "tool_count": 8,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources",
+ "paper9_recall_verified_episodes",
+ "world_model_v21_pipeline",
+ "paper9_audit_run",
+ "world_model_v21_plan",
+ "paper9_audit_run",
+ "paper9_commit_verified_episode"
+ ],
+ "violations": []
+ },
+ {
+ "error": null,
+ "expected_terminal": "verified_commit",
+ "final_response": "package_version: 0.3.3\nalgorithm_version: 2.2.3\nmode: pipeline (reuse existing stages)\nenv_kind: county\nsteps_run: 1 (Plan only, as others were reused)\nn_blocks: N/A\nn_selected: 1\ntotal_reward: N/A (Audit passed on constraints)\n硬约束审计: 第一次尝试失败 (replan_once)，第二次尝试成功 (commit_verified_episode)\n经验提交状态: 已提交至 bishan 数据集经验库\nartifacts: /evidence/single_replan_then_verified-6-218f3f36\n\n工具调用轨迹:\n1. world_model_v21_status\n2. paper9_inspect_resources\n3. paper9_recall_verified_episodes\n4. world_model_v21_pipeline (reuse_existing=true)\n5. paper9_audit_run (attempt=0, result: hard_constraint_passed=false)\n6. world_model_v21_plan (replan)\n7. paper9_audit_run (attempt=1, result: hard_constraint_passed=true)\n8. paper9_commit_verified_episode",
+ "input_tokens": 17507,
+ "latency_ms": 10746.36908294633,
+ "output_tokens": 587,
+ "passed": true,
+ "run_id": "single_replan_then_verified-6-218f3f36",
+ "run_number": 6,
+ "scenario": "single_replan_then_verified",
+ "terminal": "verified_commit",
+ "tool_count": 8,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources",
+ "paper9_recall_verified_episodes",
+ "world_model_v21_pipeline",
+ "paper9_audit_run",
+ "world_model_v21_plan",
+ "paper9_audit_run",
+ "paper9_commit_verified_episode"
+ ],
+ "violations": []
+ },
+ {
+ "error": null,
+ "expected_terminal": "verified_commit",
+ "final_response": "本次任务已成功完成。通过一次重规划（Replan），成功修正了初始规划中的硬约束失败问题，并最终通过审计验证。\n\n**总结报告：**\n* **Package Version**: 0.3.3\n* **Algorithm Version**: 2.2.3\n* **Mode**: Pipeline (Reuse Existing Stages + Replan)\n* **Env Kind**: county\n* **Steps Run**: 1 (Tool 4 Planning)\n* **N Blocks**: N/A (根据配置执行)\n* **N Selected**: 1\n* **Total Reward**: N/A (由于主要关注硬约束通过情况)\n* **硬约束审计**: 第一次 Attempt 0 失败（Hard Constraint Failed），第二次 Attempt 1 成功（Hard Constraint Passed）。\n* **经验提交状态**: 已提交至 verified episodic memory。\n* **Artifacts**: `/evidence/single_replan_then_verified-7-460aaf5c`\n\n**工具调用轨迹：**\n1. `world_model_v21_status`: 检查版本兼容性。\n2. `paper9_inspect_resources`: 检查 bishan 数据集资源，确认可复用阶段。\n3. `paper9_recall_verified_episodes`: 检索历史经验（无）。\n4. `world_model_v21_pipeline`: 执行 A->D 链路（复用 Tool 1/2/3，运行 Tool 4）。\n5. `paper9_audit_run`: 对第一次规划结果进行审计（识别到硬约束失败并要求重规划）。\n6. `world_model_v21_plan`: 按照允许范围执行第二次规划。\n7. `paper9_audit_run`: 对第二次规划结果进行审计（验证通过）。\n8. `paper9_commit_verified_episode`: 将通过审计的成功案例存入经验库。",
+ "input_tokens": 18125,
+ "latency_ms": 14446.675916900858,
+ "output_tokens": 856,
+ "passed": true,
+ "run_id": "single_replan_then_verified-7-460aaf5c",
+ "run_number": 7,
+ "scenario": "single_replan_then_verified",
+ "terminal": "verified_commit",
+ "tool_count": 8,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources",
+ "paper9_recall_verified_episodes",
+ "world_model_v21_pipeline",
+ "paper9_audit_run",
+ "world_model_v21_plan",
+ "paper9_audit_run",
+ "paper9_commit_verified_episode"
+ ],
+ "violations": []
+ },
+ {
+ "error": null,
+ "expected_terminal": "verified_commit",
+ "final_response": "任务完成。通过一次重规划成功通过了硬约束审计。\n\n**总结报告：**\n- **package_version**: 0.3.3\n- **algorithm_version**: 2.2.3\n- **mode**: pipeline (reuse_existing=true) -> replan (tool4)\n- **env_kind**: county\n- **steps_run**: 1 (Plan stage executed)\n- **n_blocks**: 未在输出中明确，沿用默认配置\n- **n_selected**: 1\n- **total_reward**: 已通过审计验证\n- **硬约束审计**: 第一次尝试失败（hard_constraint_passed: false），经一次重规划后第二次尝试成功（hard_constraint_passed: true）\n- **经验提交状态**: 已提交至 verified episodic memory\n- **artifacts**: `/evidence/single_replan_then_verified-8-7fe360cf`\n\n**工具调用轨迹：**\n1. `world_model_v21_status` (检查版本)\n2. `paper9_inspect_resources` (检查 bishan 资源)\n3. `paper9_recall_verified_episodes` (检索历史经验)\n4. `world_model_v21_pipeline` (执行 A->D 链路，复用已有资源)\n5. `paper9_audit_run` (首次审计，判定失败并要求重规划)\n6. `world_model_v21_plan` (执行 Tool 4 重规划)\n7. `paper9_audit_run` (第二次审计，判定成功)\n8. `paper9_commit_verified_episode` (提交验证后的经验)",
+ "input_tokens": 18144,
+ "latency_ms": 14189.139124937356,
+ "output_tokens": 810,
+ "passed": true,
+ "run_id": "single_replan_then_verified-8-7fe360cf",
+ "run_number": 8,
+ "scenario": "single_replan_then_verified",
+ "terminal": "verified_commit",
+ "tool_count": 8,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources",
+ "paper9_recall_verified_episodes",
+ "world_model_v21_pipeline",
+ "paper9_audit_run",
+ "world_model_v21_plan",
+ "paper9_audit_run",
+ "paper9_commit_verified_episode"
+ ],
+ "violations": []
+ },
+ {
+ "error": null,
+ "expected_terminal": "verified_commit",
+ "final_response": "任务完成。本次运行通过重规划成功修复了硬约束冲突。\n\n**运行摘要：**\n- **package_version**: 0.3.3\n- **algorithm_version**: 2.2.3\n- **mode**: pipeline (reuse_existing=true) $\\rightarrow$ replan (tool4)\n- **env_kind**: county\n- **steps_run**: 1 (Plan stage executed)\n- **n_blocks**: 未知 (由 Tool 4 内部确定)\n- **n_selected**: 1\n- **total_reward**: 未在日志中直接显示具体数值\n- **硬约束审计**: 首次尝试失败 (hard_constraint_passed=false)，经一次重规划后通过 (attempt=1, passed=true)\n- **经验提交状态**: 已成功提交至 bishan 经验库 (episode_id: eval-single_replan_then_verified-9-6e932256)\n- **artifacts**: `/evidence/single_replan_then_verified-9-6e932256`\n\n**工具调用轨迹：**\n1. `world_model_v21_status` (检查版本)\n2. `paper9_inspect_resources` (检查 bishan 资源)\n3. `paper9_recall_verified_episodes` (检索历史经验)\n4. `world_model_v21_pipeline` (执行 A $\\rightarrow$ D 链路，复用现有阶段)\n5. `paper9_audit_run` (第一次审计：识别硬约束失败)\n6. `world_model_v21_plan` (执行重规划)\n7. `paper9_audit_run` (第二次审计：确认通过)\n8. `paper9_commit_verified_episode` (提交成功经验)",
+ "input_tokens": 18192,
+ "latency_ms": 14992.618374992162,
+ "output_tokens": 847,
+ "passed": true,
+ "run_id": "single_replan_then_verified-9-6e932256",
+ "run_number": 9,
+ "scenario": "single_replan_then_verified",
+ "terminal": "verified_commit",
+ "tool_count": 8,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources",
+ "paper9_recall_verified_episodes",
+ "world_model_v21_pipeline",
+ "paper9_audit_run",
+ "world_model_v21_plan",
+ "paper9_audit_run",
+ "paper9_commit_verified_episode"
+ ],
+ "violations": []
+ },
+ {
+ "error": null,
+ "expected_terminal": "verified_commit",
+ "final_response": "任务完成。通过一次重规划成功修复了硬约束违规问题。\n\n**运行总结：**\n- **Package Version**: 0.3.3\n- **Algorithm Version**: 2.2.3\n- **Mode**: pipeline (reuse_existing=true) -> replan (tool4)\n- **Env Kind**: county\n- **Steps Run**: 1 (Planning only, stages reused)\n- **n_blocks**: N/A\n- **n_selected**: 1\n- **Total Reward**: N/A\n- **硬约束审计**: 通过 (Attempt 1: Passed)\n- **经验提交状态**: 已成功提交至 bishan 经验库\n- **Artifacts**: `/evidence/single_replan_then_verified-10-80a3dad3`\n\n**工具调用轨迹：**\n1. `world_model_v21_status`\n2. `paper9_inspect_resources`\n3. `paper9_recall_verified_episodes`\n4. `world_model_v21_pipeline` (Reuse existing stages)\n5. `paper9_audit_run` (Attempt 0: Failed hard constraint)\n6. `world_model_v21_plan` (Replan with delta=0)\n7. `paper9_audit_run` (Attempt 1: Passed)\n8. `paper9_commit_verified_episode`",
+ "input_tokens": 18137,
+ "latency_ms": 13639.83133295551,
+ "output_tokens": 745,
+ "passed": true,
+ "run_id": "single_replan_then_verified-10-80a3dad3",
+ "run_number": 10,
+ "scenario": "single_replan_then_verified",
+ "terminal": "verified_commit",
+ "tool_count": 8,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources",
+ "paper9_recall_verified_episodes",
+ "world_model_v21_pipeline",
+ "paper9_audit_run",
+ "world_model_v21_plan",
+ "paper9_audit_run",
+ "paper9_commit_verified_episode"
+ ],
+ "violations": []
+ }
+ ],
+ "runs_per_scenario": 10,
+ "scenarios": [
+ "verified_first_pass",
+ "version_mismatch_stops",
+ "single_replan_then_verified"
+ ],
+ "schema_version": "paper9.adk_reliability_report.v1",
+ "scope": "live_gemma4_adk_orchestration_with_deterministic_tool_doubles",
+ "statistics": {
+ "by_scenario": {
+ "single_replan_then_verified": {
+ "confidence_95_wilson": [
+ 0.7224598312333834,
+ 1.0
+ ],
+ "error_count": 0,
+ "exact_trace_consistency": 1.0,
+ "mean_latency_ms": 13574.544266727753,
+ "mean_tool_count": 8.0,
+ "p95_latency_ms": 15309.070431266446,
+ "pairwise_trace_consistency": 1.0,
+ "pass_rate": 1.0,
+ "passed": 10,
+ "terminal_distribution": {
+ "verified_commit": 10
+ },
+ "total": 10,
+ "trace_distribution": {
+ "world_model_v21_status -> paper9_inspect_resources -> paper9_recall_verified_episodes -> world_model_v21_pipeline -> paper9_audit_run -> world_model_v21_plan -> paper9_audit_run -> paper9_commit_verified_episode": 10
+ },
+ "unique_trace_count": 1
+ },
+ "verified_first_pass": {
+ "confidence_95_wilson": [
+ 0.7224598312333834,
+ 1.0
+ ],
+ "error_count": 0,
+ "exact_trace_consistency": 1.0,
+ "mean_latency_ms": 7965.228312578984,
+ "mean_tool_count": 6.0,
+ "p95_latency_ms": 9531.116983306129,
+ "pairwise_trace_consistency": 1.0,
+ "pass_rate": 1.0,
+ "passed": 10,
+ "terminal_distribution": {
+ "verified_commit": 10
+ },
+ "total": 10,
+ "trace_distribution": {
+ "world_model_v21_status -> paper9_inspect_resources -> paper9_recall_verified_episodes -> world_model_v21_pipeline -> paper9_audit_run -> paper9_commit_verified_episode": 10
+ },
+ "unique_trace_count": 1
+ },
+ "version_mismatch_stops": {
+ "confidence_95_wilson": [
+ 0.7224598312333834,
+ 1.0
+ ],
+ "error_count": 0,
+ "exact_trace_consistency": 1.0,
+ "mean_latency_ms": 3235.9444458968937,
+ "mean_tool_count": 2.0,
+ "p95_latency_ms": 4884.452160820365,
+ "pairwise_trace_consistency": 1.0,
+ "pass_rate": 1.0,
+ "passed": 10,
+ "terminal_distribution": {
+ "preflight_stop": 10
+ },
+ "total": 10,
+ "trace_distribution": {
+ "world_model_v21_status -> paper9_inspect_resources": 10
+ },
+ "unique_trace_count": 1
+ }
+ },
+ "overall": {
+ "confidence_95_wilson": [
+ 0.8864829086095221,
+ 1.0
+ ],
+ "error_count": 0,
+ "exact_trace_consistency": 0.3333333333333333,
+ "mean_latency_ms": 8258.572341734543,
+ "mean_tool_count": 5.333333333333333,
+ "p95_latency_ms": 14746.944268851075,
+ "pairwise_trace_consistency": 0.572158365261813,
+ "pass_rate": 1.0,
+ "passed": 30,
+ "terminal_distribution": {
+ "preflight_stop": 10,
+ "verified_commit": 20
+ },
+ "total": 30,
+ "trace_distribution": {
+ "world_model_v21_status -> paper9_inspect_resources": 10,
+ "world_model_v21_status -> paper9_inspect_resources -> paper9_recall_verified_episodes -> world_model_v21_pipeline -> paper9_audit_run -> paper9_commit_verified_episode": 10,
+ "world_model_v21_status -> paper9_inspect_resources -> paper9_recall_verified_episodes -> world_model_v21_pipeline -> paper9_audit_run -> world_model_v21_plan -> paper9_audit_run -> paper9_commit_verified_episode": 10
+ },
+ "unique_trace_count": 3
+ },
+ "release_gate": {
+ "failed_scenarios": [],
+ "pass_rate_threshold": 0.8,
+ "passed": true
+ }
+ }
+}
diff --git a/data_agent/demo_evidence/paper9/finals_20260730/behavior_contract_report.json b/data_agent/demo_evidence/paper9/finals_20260730/behavior_contract_report.json
new file mode 100644
index 00000000..c56a8be8
--- /dev/null
+++ b/data_agent/demo_evidence/paper9/finals_20260730/behavior_contract_report.json
@@ -0,0 +1,112 @@
+{
+ "generated_at": "2026-07-30T15:05:59.840771+00:00",
+ "limitations": [
+ "This report validates control-flow invariants, not stochastic Gemma 4 tool-selection reliability.",
+ "Live model reliability must be measured separately with repeated ADK runs."
+ ],
+ "results": [
+ {
+ "evaluation": {
+ "passed": true,
+ "terminal": "verified_commit",
+ "tool_count": 6,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources",
+ "paper9_recall_verified_episodes",
+ "world_model_v21_pipeline",
+ "paper9_audit_run",
+ "paper9_commit_verified_episode"
+ ],
+ "violations": []
+ },
+ "expected_pass": true,
+ "id": "verified_first_pass",
+ "matched_expectation": true
+ },
+ {
+ "evaluation": {
+ "passed": true,
+ "terminal": "verified_commit",
+ "tool_count": 8,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources",
+ "paper9_recall_verified_episodes",
+ "world_model_v21_pipeline",
+ "paper9_audit_run",
+ "world_model_v21_plan",
+ "paper9_audit_run",
+ "paper9_commit_verified_episode"
+ ],
+ "violations": []
+ },
+ "expected_pass": true,
+ "id": "single_replan_then_verified",
+ "matched_expectation": true
+ },
+ {
+ "evaluation": {
+ "passed": true,
+ "terminal": "human_review",
+ "tool_count": 7,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources",
+ "paper9_recall_verified_episodes",
+ "world_model_v21_pipeline",
+ "paper9_audit_run",
+ "world_model_v21_plan",
+ "paper9_audit_run"
+ ],
+ "violations": []
+ },
+ "expected_pass": true,
+ "id": "second_failure_escalates",
+ "matched_expectation": true
+ },
+ {
+ "evaluation": {
+ "passed": true,
+ "terminal": "preflight_stop",
+ "tool_count": 2,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources"
+ ],
+ "violations": []
+ },
+ "expected_pass": true,
+ "id": "version_mismatch_stops",
+ "matched_expectation": true
+ },
+ {
+ "evaluation": {
+ "passed": false,
+ "terminal": "incomplete",
+ "tool_count": 5,
+ "tool_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources",
+ "paper9_recall_verified_episodes",
+ "world_model_v21_pipeline",
+ "paper9_commit_verified_episode"
+ ],
+ "violations": [
+ "Every planning call must be immediately followed by an audit.",
+ "An unverified or failed run was committed to memory."
+ ]
+ },
+ "expected_pass": false,
+ "id": "premature_commit_is_rejected",
+ "matched_expectation": true
+ }
+ ],
+ "schema_version": "paper9.behavior_contract_report.v1",
+ "scope": "deterministic_offline_behavior_contracts",
+ "summary": {
+ "matched": 5,
+ "pass_rate": 1.0,
+ "total": 5
+ }
+}
diff --git a/data_agent/demo_evidence/paper9/finals_20260730/finals_preflight_final.json b/data_agent/demo_evidence/paper9/finals_20260730/finals_preflight_final.json
new file mode 100644
index 00000000..9cf4ef8a
--- /dev/null
+++ b/data_agent/demo_evidence/paper9/finals_20260730/finals_preflight_final.json
@@ -0,0 +1,56 @@
+{
+ "checks": [
+ {
+ "actual": "/app/paper9-demo",
+ "name": "paper9_repo",
+ "passed": true
+ },
+ {
+ "actual": "0.3.3",
+ "expected": "0.3.3",
+ "name": "paper9_package_version",
+ "passed": true
+ },
+ {
+ "actual": "2.2.3",
+ "expected": "2.2.3",
+ "name": "paper9_algorithm_version",
+ "passed": true
+ },
+ {
+ "actual": "/app/bishan-runs/prepared/dem_slope_analysis/output/DLTB_with_slope.shp",
+ "name": "bishan_prepared",
+ "passed": true
+ },
+ {
+ "actual": {
+ "directory": "/app/bishan-runs/prepared/ensemble_seed0",
+ "member_count": 3
+ },
+ "name": "bishan_onnx_ensemble",
+ "passed": true
+ },
+ {
+ "actual": {
+ "available": [
+ "Gemma4:12b",
+ "Gemma4:26b",
+ "Gemma4:31b",
+ "Gemma4:e2b",
+ "Gemma4:e4b",
+ "Gemma4:latest",
+ "Qwen3.6:27b",
+ "Qwen3.6:35b",
+ "nomic-embed-text-v2-moe:latest",
+ "qwen3-vl:32b"
+ ],
+ "required": "Gemma4:26b"
+ },
+ "name": "ollama_model_tag",
+ "passed": true
+ }
+ ],
+ "failed_checks": [],
+ "ready": true,
+ "schema_version": "gemma4.finals_preflight.v1"
+}
diff --git a/data_agent/demo_evidence/paper9/finals_20260730/verified_finals_demo_report.json b/data_agent/demo_evidence/paper9/finals_20260730/verified_finals_demo_report.json
new file mode 100644
index 00000000..8e39f8dc
--- /dev/null
+++ b/data_agent/demo_evidence/paper9/finals_20260730/verified_finals_demo_report.json
@@ -0,0 +1,269 @@
+{
+ "generated_at": "2026-07-30T02:50:18.812579+00:00",
+ "nl2sql": {
+ "passed": true,
+ "reference_passed": true,
+ "reference_sql": "WITH longest_bridge AS (\n SELECT geometry\n FROM cq_osm_roads_2021\n WHERE bridge = 'T'\n ORDER BY ST_Length(geometry::geography) DESC\n LIMIT 1\n)\nSELECT COUNT(DISTINCT p.\"ID\") AS poi_count\nFROM cq_amap_poi_2024 AS p\nCROSS JOIN longest_bridge AS lb\nWHERE ST_DWithin(p.geometry::geography, lb.geometry::geography, 100)",
+ "reference_value": 35,
+ "repeats": 3,
+ "runs": [
+ {
+ "checks": {
+ "single_result_is_35": true,
+ "status_ok": true,
+ "uses_100_meters": true,
+ "uses_expected_tables": true,
+ "uses_geography": true,
+ "uses_st_dwithin": true
+ },
+ "corrections": [
+ "semantic_column_alias"
+ ],
+ "error": null,
+ "latency_s": 14.414,
+ "passed": true,
+ "run": 1,
+ "sql": "WITH longest_bridge AS (SELECT geometry FROM cq_osm_roads_2021 WHERE bridge = 'T' ORDER BY ST_LENGTH(CAST(geometry AS GEOGRAPHY)) DESC LIMIT 1) SELECT COUNT(DISTINCT p.\"ID\") FROM cq_amap_poi_2024 AS p, longest_bridge AS lb WHERE ST_DWITHIN(CAST(p.geometry AS GEOGRAPHY), CAST(lb.geometry AS GEOGRAPHY), 100)",
+ "status": "ok",
+ "value": 35
+ },
+ {
+ "checks": {
+ "single_result_is_35": true,
+ "status_ok": true,
+ "uses_100_meters": true,
+ "uses_expected_tables": true,
+ "uses_geography": true,
+ "uses_st_dwithin": true
+ },
+ "corrections": [
+ "semantic_column_alias"
+ ],
+ "error": null,
+ "latency_s": 6.761,
+ "passed": true,
+ "run": 2,
+ "sql": "WITH longest_bridge AS (SELECT geometry FROM cq_osm_roads_2021 WHERE bridge = 'T' ORDER BY ST_LENGTH(CAST(geometry AS GEOGRAPHY)) DESC LIMIT 1) SELECT COUNT(DISTINCT p.\"ID\") FROM cq_amap_poi_2024 AS p, longest_bridge AS lb WHERE ST_DWITHIN(CAST(p.geometry AS GEOGRAPHY), CAST(lb.geometry AS GEOGRAPHY), 100)",
+ "status": "ok",
+ "value": 35
+ },
+ {
+ "checks": {
+ "single_result_is_35": true,
+ "status_ok": true,
+ "uses_100_meters": true,
+ "uses_expected_tables": true,
+ "uses_geography": true,
+ "uses_st_dwithin": true
+ },
+ "corrections": [
+ "semantic_column_alias"
+ ],
+ "error": null,
+ "latency_s": 6.884,
+ "passed": true,
+ "run": 3,
+ "sql": "WITH longest_bridge AS (SELECT geometry FROM cq_osm_roads_2021 WHERE bridge = 'T' ORDER BY ST_LENGTH(CAST(geometry AS GEOGRAPHY)) DESC LIMIT 1) SELECT COUNT(DISTINCT p.\"ID\") FROM cq_amap_poi_2024 AS p, longest_bridge AS lb WHERE ST_DWITHIN(CAST(p.geometry AS GEOGRAPHY), CAST(lb.geometry AS GEOGRAPHY), 100)",
+ "status": "ok",
+ "value": 35
+ }
+ ],
+ "ui_prompt": "@NL2SQL 统计距离道路网络中最长桥梁100米范围内的高德POI数量。"
+ },
+ "paper9_live_agent": {
+ "checks": {
+ "all_outputs_exist": true,
+ "contiguity_improved": true,
+ "cultivated_area_not_reduced": true,
+ "exact_six_tool_trace": true,
+ "final_summary_loaded": true,
+ "hard_gate_passed": true,
+ "optimized_spatial_artifact": true,
+ "pipeline_ok": true,
+ "slope_reduced": true,
+ "tool4_plan_ok": true,
+ "verified_memory_committed": true
+ },
+ "error": null,
+ "expected_trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources",
+ "paper9_recall_verified_episodes",
+ "world_model_v21_pipeline",
+ "paper9_audit_run",
+ "paper9_commit_verified_episode"
+ ],
+ "final_text": "本次任务成功完成了 bishan 数据集的快速县域 MPC 规划。\n\n**任务总结：**\n- **package_version**: 0.3.3\n- **algorithm_version**: 2.2.3\n- **mode**: pipeline (A $\\to$ D, reuse_existing=true)\n- **env_kind**: county\n- **steps_run**: 100 (MPC 前瞻步长 horizon=1)\n- **n_blocks**: 2640\n- **n_selected**: N/A\n- **total_reward**: 37.409\n- **硬约束审计**: 通过 (hard_constraint_passed=true)，耕地面积增加约 1.94 ha，符合不减少约束。\n- **经验提交状态**: 已成功提交至已验证经验库 (committed)。\n- **artifacts**: 包含优化后的矢量数据 (`optimized_dltb.shp`) 及可视化图层。\n\n**地图说明：**\n右侧地图展示了耕地空间布局的优化变化（基于 CHG_FLAG 图层）：\n- **灰色**: 保持不变\n- **红色**: 耕地 $\\to$ 林地\n- **绿色**: 林地 $\\to$ 耕地\n\n**工具调用轨迹：**\n1. `world_model_v21_status`: 检查系统版本与可用性。\n2. `paper9_inspect_resources`: 检查 bishan 数据集资源及可用阶段。\n3. `paper9_recall_verified_episodes`: 检索历史经验（返回为空）。\n4. `world_model_v21_pipeline`: 执行全链路规划（复用已存在的 prepare/sample/train 阶段，仅运行 plan）。\n5. `paper9_audit_run`: 对规划结果进行硬约束审计。\n6. `paper9_commit_verified_episode`: 将通过审计的实验结果存入经验库。",
+ "latency_s": 112.94,
+ "metrics": {
+ "baimu_area_change_ha": 35.13161927157044,
+ "cont_change": 0.020184135977336926,
+ "cultivated_area_change_ha": 1.935935926192999,
+ "episode_id": "903da9b3e041fd16e042",
+ "land_use_code_scheme": "legacy_three_digit_test_data",
+ "n_farm_to_forest": 406,
+ "n_forest_to_farm": 406,
+ "n_in_env": 53004,
+ "n_input": 101657,
+ "out_dir": "/app/data_agent/uploads/agent_world_model_v21/world_model_v21/20260730_104830_359700",
+ "slope_change_pct": -0.6077654972924247,
+ "steps_run": 100,
+ "summary_error": null,
+ "swaps_completed": 406
+ },
+ "passed": true,
+ "session_id": "finals-demo-5589cfa734",
+ "trace": [
+ "world_model_v21_status",
+ "paper9_inspect_resources",
+ "paper9_recall_verified_episodes",
+ "world_model_v21_pipeline",
+ "paper9_audit_run",
+ "paper9_commit_verified_episode"
+ ],
+ "ui_prompt": "@WorldModelV21 请使用 bishan 数据集运行一次快速县域 MPC 规划，完成硬约束审计，并仅在通过后保存已验证经验。"
+ },
+ "paper9_preflight": {
+ "checks": {
+ "algorithm_2_2_3": true,
+ "package_0_3_3": true,
+ "planning_ready": true,
+ "status_ready": true,
+ "three_onnx_members": true,
+ "version_compatible": true
+ },
+ "inspect": {
+ "dataset": "bishan",
+ "finals": {
+ "expected_algorithm_version": "2.2.3",
+ "expected_package_version": "0.3.3",
+ "ready": true,
+ "version_compatible": true
+ },
+ "land_use_code_contract": {
+ "code_counts": {
+ "011": 14021,
+ "012": 45,
+ "013": 25496,
+ "021": 3428,
+ "022": 175,
+ "023": 542,
+ "031": 13004,
+ "032": 115,
+ "033": 405,
+ "043": 222,
+ "101": 161,
+ "102": 1066,
+ "104": 21,
+ "111": 340,
+ "112": 11,
+ "113": 132,
+ "114": 5072,
+ "118": 103,
+ "122": 407,
+ "127": 221,
+ "202": 2744,
+ "203": 33500,
+ "204": 374,
+ "205": 52
+ },
+ "compatible": true,
+ "farm_dlbm": "011",
+ "forest_dlbm": "031",
+ "scheme": "legacy_three_digit_test_data"
+ },
+ "paper9": {
+ "algorithm_name": "paper9v2",
+ "algorithm_version": "2.2.3",
+ "commit": null,
+ "commit_date": null,
+ "error": null,
+ "importable": true,
+ "module_path": "/app/paper9-demo/src/farmland_mpc/__init__.py",
+ "package_version": "0.3.3",
+ "remote": null,
+ "repo_exists": true,
+ "repo_path": "/app/paper9-demo",
+ "source_matches_repo": true
+ },
+ "planning_ready": true,
+ "recommended_next_action": "run_plan",
+ "required_stages": [],
+ "reusable_stages": [
+ "prepare",
+ "sample",
+ "train"
+ ],
+ "stages": {
+ "prepare": {
+ "path": "/app/bishan-runs/prepared",
+ "ready": true
+ },
+ "sample": {
+ "path": "/app/bishan-runs/prepared/tool2",
+ "ready": true
+ },
+ "train": {
+ "onnx_member_count": 3,
+ "path": "/app/bishan-runs/prepared/ensemble_seed0",
+ "ready": true
+ }
+ },
+ "status": "ready",
+ "version": "2.1.0"
+ },
+ "passed": true,
+ "status": {
+ "capabilities": {
+ "baimu_area_floor": true,
+ "bounded_replan": true,
+ "county_env": true,
+ "cultivated_area_floor": true,
+ "hard_gate_audit": true,
+ "onnx_inference": true,
+ "pipeline_a_to_d": true,
+ "prepare_sample_train": true,
+ "restoration_env": true,
+ "tool1_prepare": true,
+ "tool2_sample": true,
+ "tool3_train": true,
+ "tool4_plan": true,
+ "verified_episode_memory": true
+ },
+ "defaults": {
+ "ensemble_dir": "/app/bishan-runs/prepared/ensemble_seed0",
+ "out_dir_policy": "per-user timestamped uploads directory",
+ "prepared_dir": "/app/bishan-runs/prepared"
+ },
+ "finals": {
+ "expected_algorithm_version": "2.2.3",
+ "expected_package_version": "0.3.3",
+ "ready": true,
+ "version_compatible": true
+ },
+ "onnx_member_count": 3,
+ "paper9": {
+ "algorithm_name": "paper9v2",
+ "algorithm_version": "2.2.3",
+ "commit": null,
+ "commit_date": null,
+ "error": null,
+ "importable": true,
+ "module_path": "/app/paper9-demo/src/farmland_mpc/__init__.py",
+ "package_version": "0.3.3",
+ "remote": null,
+ "repo_exists": true,
+ "repo_path": "/app/paper9-demo",
+ "source_matches_repo": true
+ },
+ "runtime": {
+ "proj_data_dir": "/app/.venv/lib/python3.12/site-packages/pyproj/proj_dir/share/proj"
+ },
+ "status": "ready",
+ "version": "2.1.0"
+ }
+ },
+ "passed": true,
+ "schema_version": "gemma4.finals_demo_verification.v1",
+ "scope": "exact_production_prompts_against_running_data_and_tools"
+}
diff --git a/data_agent/evals/paper9_finals/behavior_contract_cases.json b/data_agent/evals/paper9_finals/behavior_contract_cases.json
new file mode 100644
index 00000000..a5447654
--- /dev/null
+++ b/data_agent/evals/paper9_finals/behavior_contract_cases.json
@@ -0,0 +1,63 @@
+{
+ "schema_version": "paper9.behavior_contract_cases.v1",
+ "cases": [
+ {
+ "id": "verified_first_pass",
+ "expected_pass": true,
+ "events": [
+ {"tool": "world_model_v21_status", "response": {"finals": {"version_compatible": true}}},
+ {"tool": "paper9_inspect_resources", "response": {"planning_ready": true}},
+ {"tool": "paper9_recall_verified_episodes", "response": {"count": 0}},
+ {"tool": "world_model_v21_pipeline", "response": {"status": "ok"}},
+ {"tool": "paper9_audit_run", "response": {"attempt": 0, "hard_constraint_passed": true, "retryable": false}},
+ {"tool": "paper9_commit_verified_episode", "response": {"status": "committed"}}
+ ]
+ },
+ {
+ "id": "single_replan_then_verified",
+ "expected_pass": true,
+ "events": [
+ {"tool": "world_model_v21_status", "response": {"finals": {"version_compatible": true}}},
+ {"tool": "paper9_inspect_resources", "response": {"planning_ready": true}},
+ {"tool": "paper9_recall_verified_episodes", "response": {"count": 1}},
+ {"tool": "world_model_v21_pipeline", "response": {"status": "ok"}},
+ {"tool": "paper9_audit_run", "response": {"attempt": 0, "hard_constraint_passed": false, "retryable": true}},
+ {"tool": "world_model_v21_plan", "response": {"status": "ok"}},
+ {"tool": "paper9_audit_run", "response": {"attempt": 1, "hard_constraint_passed": true, "retryable": false}},
+ {"tool": "paper9_commit_verified_episode", "response": {"status": "committed"}}
+ ]
+ },
+ {
+ "id": "second_failure_escalates",
+ "expected_pass": true,
+ "events": [
+ {"tool": "world_model_v21_status", "response": {"finals": {"version_compatible": true}}},
+ {"tool": "paper9_inspect_resources", "response": {"planning_ready": true}},
+ {"tool": "paper9_recall_verified_episodes", "response": {"count": 0}},
+ {"tool": "world_model_v21_pipeline", "response": {"status": "ok"}},
+ {"tool": "paper9_audit_run", "response": {"attempt": 0, "hard_constraint_passed": false, "retryable": true}},
+ {"tool": "world_model_v21_plan", "response": {"status": "ok"}},
+ {"tool": "paper9_audit_run", "response": {"attempt": 1, "hard_constraint_passed": false, "retryable": false}}
+ ]
+ },
+ {
+ "id": "version_mismatch_stops",
+ "expected_pass": true,
+ "events": [
+ {"tool": "world_model_v21_status", "response": {"finals": {"version_compatible": false}}},
+ {"tool": "paper9_inspect_resources", "response": {"planning_ready": false}}
+ ]
+ },
+ {
+ "id": "premature_commit_is_rejected",
+ "expected_pass": false,
+ "events": [
+ {"tool": "world_model_v21_status", "response": {"finals": {"version_compatible": true}}},
+ {"tool": "paper9_inspect_resources", "response": {"planning_ready": true}},
+ {"tool": "paper9_recall_verified_episodes", "response": {"count": 0}},
+ {"tool": "world_model_v21_pipeline", "response": {"status": "ok"}},
+ {"tool": "paper9_commit_verified_episode", "response": {"status": "committed"}}
+ ]
+ }
+ ]
+}
diff --git a/data_agent/finals_preflight.py b/data_agent/finals_preflight.py
new file mode 100644
index 00000000..7a35366f
--- /dev/null
+++ b/data_agent/finals_preflight.py
@@ -0,0 +1,126 @@
+"""Read-only host preflight checks for the Gemma 4 finals demo."""
+
+from __future__ import annotations
+
+import ast
+import json
+import tomllib
+import urllib.request
+from pathlib import Path
+from typing import Any
+
+from .paper9_agent_governance import (
+ EXPECTED_ALGORITHM_VERSION,
+ EXPECTED_PACKAGE_VERSION,
+)
+
+
+def inspect_finals_host(
+ *,
+ paper9_repo: str | Path,
+ bishan_runs: str | Path,
+ model_tag: str,
+ ollama_tags: list[str],
+) -> dict[str, Any]:
+ """Inspect local resources without importing Paper9 or changing state."""
+
+ repo = Path(paper9_repo).expanduser().resolve()
+ runs = Path(bishan_runs).expanduser().resolve()
+ prepared = runs / "prepared"
+ ensemble = prepared / "ensemble_seed0"
+ package_version = _read_project_version(repo / "pyproject.toml")
+ algorithm_version = _read_python_constant(
+ repo / "src" / "paper9_mnr" / "version.py", "ALGORITHM_VERSION"
+ )
+ dltb = prepared / "dem_slope_analysis" / "output" / "DLTB_with_slope.shp"
+ onnx_members = sorted(ensemble.rglob("*.onnx")) if ensemble.is_dir() else []
+
+ checks = [
+ _check("paper9_repo", repo.is_dir(), str(repo)),
+ _check(
+ "paper9_package_version",
+ package_version == EXPECTED_PACKAGE_VERSION,
+ package_version,
+ expected=EXPECTED_PACKAGE_VERSION,
+ ),
+ _check(
+ "paper9_algorithm_version",
+ algorithm_version == EXPECTED_ALGORITHM_VERSION,
+ algorithm_version,
+ expected=EXPECTED_ALGORITHM_VERSION,
+ ),
+ _check("bishan_prepared", dltb.is_file(), str(dltb)),
+ _check(
+ "bishan_onnx_ensemble",
+ bool(onnx_members),
+ {"directory": str(ensemble), "member_count": len(onnx_members)},
+ ),
+ _check(
+ "ollama_model_tag",
+ model_tag in ollama_tags,
+ {"required": model_tag, "available": sorted(ollama_tags)},
+ ),
+ ]
+ failed = [check["name"] for check in checks if not check["passed"]]
+ return {
+ "schema_version": "gemma4.finals_preflight.v1",
+ "ready": not failed,
+ "failed_checks": failed,
+ "checks": checks,
+ }
+
+
+def fetch_ollama_tags(api_base: str, *, timeout: float = 5.0) -> list[str]:
+ """Return exact model tags from an Ollama server."""
+
+ url = f"{api_base.rstrip('/')}/api/tags"
+ with urllib.request.urlopen(url, timeout=timeout) as response: # noqa: S310
+ payload = json.load(response)
+ return [
+ str(model.get("name"))
+ for model in payload.get("models", [])
+ if model.get("name")
+ ]
+
+
+def _read_project_version(path: Path) -> str | None:
+ if not path.is_file():
+ return None
+ try:
+ with path.open("rb") as handle:
+ return str(tomllib.load(handle).get("project", {}).get("version") or "") or None
+ except (OSError, tomllib.TOMLDecodeError):
+ return None
+
+
+def _read_python_constant(path: Path, name: str) -> str | None:
+ if not path.is_file():
+ return None
+ try:
+ tree = ast.parse(path.read_text(encoding="utf-8"))
+ except (OSError, SyntaxError, UnicodeError):
+ return None
+ for node in tree.body:
+ if not isinstance(node, ast.Assign):
+ continue
+ if not any(isinstance(target, ast.Name) and target.id == name for target in node.targets):
+ continue
+ try:
+ value = ast.literal_eval(node.value)
+ except (ValueError, TypeError):
+ return None
+ return str(value)
+ return None
+
+
+def _check(
+ name: str,
+ passed: bool,
+ actual: Any,
+ *,
+ expected: Any | None = None,
+) -> dict[str, Any]:
+ result = {"name": name, "passed": bool(passed), "actual": actual}
+ if expected is not None:
+ result["expected"] = expected
+ return result
diff --git a/data_agent/model_gateway.py b/data_agent/model_gateway.py
index 92a0003a..1b7376bf 100644
--- a/data_agent/model_gateway.py
+++ b/data_agent/model_gateway.py
@@ -266,6 +266,8 @@ class ModelRegistry:
"reasoning", "analysis", "generation", "coding"],
"api_base": "http://localhost:11434",
"model_id": "ollama_chat/Gemma4:26b",
+ "extra_body": {"think": False},
+ "request_timeout": 600,
},
# Back-compat alias — older configs/env still reference the 31B name.
# Points at the same Gemma4 tag the user actually has pulled locally
diff --git a/data_agent/nl2sql_presentation.py b/data_agent/nl2sql_presentation.py
index 3a5d2325..e0c6128f 100644
--- a/data_agent/nl2sql_presentation.py
+++ b/data_agent/nl2sql_presentation.py
@@ -204,10 +204,188 @@ def build_bridge_building_map_update(
return None
+def build_longest_bridge_poi_map_update(
+ raw: str | dict[str, Any],
+ *,
+ question: str = "",
+ upload_dir: str | None = None,
+) -> dict[str, Any] | None:
+ """Build map layers for the locked longest-bridge/100m/AMap-POI query."""
+ payload = parse_nl2sql_payload(raw)
+ if not payload or payload.get("status") != "ok":
+ return None
+ sql = payload.get("sql") or ""
+ if not _is_longest_bridge_poi_query(question, sql):
+ return None
+
+ count_info = _extract_count_value(payload.get("execution") or {})
+ if not count_info or not isinstance(count_info[1], (int, float)):
+ return None
+ scalar_poi_count = int(count_info[1])
+
+ try:
+ from data_agent.database_tools import _inject_user_context
+ from data_agent.db_engine import get_engine
+ from data_agent.user_context import get_user_upload_dir
+
+ target_dir = upload_dir or get_user_upload_dir()
+ os.makedirs(target_dir, exist_ok=True)
+ engine = get_engine(readonly=True)
+ if engine is None:
+ return None
+
+ # One SQL statement gives every layer a single, consistent PostGIS snapshot.
+ with engine.connect() as conn:
+ _inject_user_context(conn)
+ rows = conn.execute(text(_LONGEST_BRIDGE_POI_MAP_SQL)).mappings().all()
+
+ poi_rows = [row for row in rows if row.get("feature_group") == "poi"]
+ bridge_rows = [row for row in rows if row.get("feature_group") == "bridge"]
+ buffer_rows = [row for row in rows if row.get("feature_group") == "buffer"]
+ if (
+ len(poi_rows) != scalar_poi_count
+ or len(bridge_rows) != 1
+ or len(buffer_rows) != 1
+ ):
+ logger.warning(
+ "Skipping longest-bridge map because scalar/features disagree: "
+ "scalar=%s poi=%s bridge=%s buffer=%s",
+ scalar_poi_count,
+ len(poi_rows),
+ len(bridge_rows),
+ len(buffer_rows),
+ )
+ return None
+
+ suffix = uuid.uuid4().hex[:8]
+ poi_name = f"nl2sql_longest_bridge_pois_{suffix}.geojson"
+ bridge_name = f"nl2sql_longest_bridge_{suffix}.geojson"
+ buffer_name = f"nl2sql_longest_bridge_100m_{suffix}.geojson"
+ _write_geojson(
+ os.path.join(target_dir, poi_name),
+ _rows_to_feature_collection(
+ poi_rows,
+ property_keys=(
+ "poi_id", "poi_name", "address", "poi_type",
+ "distance_m", "result_type",
+ ),
+ ),
+ )
+ _write_geojson(
+ os.path.join(target_dir, bridge_name),
+ _rows_to_feature_collection(
+ bridge_rows,
+ property_keys=(
+ "osm_id", "bridge_name", "fclass", "bridge",
+ "length_m", "result_type",
+ ),
+ ),
+ )
+ _write_geojson(
+ os.path.join(target_dir, buffer_name),
+ _rows_to_feature_collection(
+ buffer_rows,
+ property_keys=(
+ "osm_id", "bridge_name", "length_m", "radius_m", "result_type",
+ ),
+ ),
+ )
+
+ view_row = rows[0]
+ center = [float(view_row["center_lat"]), float(view_row["center_lng"])]
+ zoom = _estimate_zoom_from_extent(view_row)
+ bridge_row = bridge_rows[0]
+ return {
+ "schema": "map_update.v1",
+ "layers": [
+ {
+ "name": "最长桥梁 100 米范围",
+ "type": "polygon",
+ "geojson": buffer_name,
+ "style": {
+ "color": "#f59e0b",
+ "weight": 2,
+ "opacity": 0.9,
+ "fillColor": "#fbbf24",
+ "fillOpacity": 0.2,
+ },
+ },
+ {
+ "name": "道路网络中最长桥梁",
+ "type": "line",
+ "geojson": bridge_name,
+ "style": {
+ "color": "#dc2626",
+ "weight": 6,
+ "opacity": 1.0,
+ },
+ },
+ {
+ "name": f"100 米范围内高德 POI ({len(poi_rows)} 个)",
+ "type": "point",
+ "geojson": poi_name,
+ "style": {
+ "color": "#ffffff",
+ "weight": 2,
+ "opacity": 1.0,
+ "fillColor": "#2563eb",
+ "fillOpacity": 0.9,
+ "radius": 6,
+ },
+ },
+ ],
+ "center": center,
+ "zoom": zoom,
+ "summary": {
+ "query_type": "longest_bridge_poi_100m",
+ "scalar_poi_count": scalar_poi_count,
+ "poi_feature_count": len(poi_rows),
+ "bridge_feature_count": len(bridge_rows),
+ "buffer_feature_count": len(buffer_rows),
+ "distance_m": 100,
+ "bridge_osm_id": bridge_row.get("osm_id"),
+ "bridge_name": bridge_row.get("bridge_name"),
+ "bridge_length_m": _jsonable(bridge_row.get("length_m")),
+ "source_tables": ["cq_osm_roads_2021", "cq_amap_poi_2024"],
+ "geometry_snapshot": "single_postgis_statement",
+ },
+ }
+ except Exception as exc:
+ logger.warning("Failed to build longest-bridge POI map update: %s", exc)
+ return None
+
+
+def build_nl2sql_map_update(
+ raw: str | dict[str, Any],
+ *,
+ question: str = "",
+ upload_dir: str | None = None,
+) -> dict[str, Any] | None:
+ """Dispatch supported NL2SQL scalar results to companion map layers."""
+ map_update = build_longest_bridge_poi_map_update(
+ raw,
+ question=question,
+ upload_dir=upload_dir,
+ )
+ if map_update:
+ return map_update
+ return build_bridge_building_map_update(
+ raw,
+ question=question,
+ upload_dir=upload_dir,
+ )
+
+
def describe_map_update(map_update: dict[str, Any] | None) -> str:
if not map_update:
return ""
summary = map_update.get("summary") or {}
+ if summary.get("query_type") == "longest_bridge_poi_100m":
+ return (
+ f"已默认加载地图：高德 POI {summary.get('poi_feature_count')} 个、"
+ f"最长桥梁 1 条及其 {summary.get('distance_m')} 米范围；"
+ "三个图层来自同一 PostGIS 查询快照。"
+ )
golden_count = summary.get("golden_building_count")
feature_count = summary.get("building_feature_count")
road_count = summary.get("bridge_road_count")
@@ -258,8 +436,11 @@ def _friendly_count_label(label: str, *, question: str, sql: str) -> str:
):
return "建筑物轮廓数量"
if _is_length_metric_result(label, joined, count_metric=is_count):
- if any(token in joined for token in ("公里", "千米", "kilometer", "kilometre", "_km", " km")):
- return "道路总长度（公里）" if "道路" in question or "road" in joined else "总长度（公里）"
+ length_unit_tokens = ("公里", "千米", "kilometer", "kilometre", "_km", " km")
+ if any(token in joined for token in length_unit_tokens):
+ if "道路" in question or "road" in joined:
+ return "道路总长度（公里）"
+ return "总长度（公里）"
return "道路总长度" if "道路" in question or "road" in joined else "总长度"
if "road" in label.lower() or "道路" in question:
return "道路数量"
@@ -295,7 +476,34 @@ def _is_bridge_building_intersection_query(question: str, sql: str) -> bool:
return has_building and has_bridge and has_intersection and has_cq_roads
-def _rows_to_feature_collection(rows: list[Any], *, property_keys: tuple[str, ...]) -> dict[str, Any]:
+def _is_longest_bridge_poi_query(question: str, sql: str) -> bool:
+ normalized_question = re.sub(r"[\s。！？?!，,；;：:]+", "", question or "").lower()
+ normalized_question = re.sub(r"^@nl2sql", "", normalized_question)
+ if normalized_question != "统计距离道路网络中最长桥梁100米范围内的高德poi数量":
+ return False
+
+ sql_low = re.sub(r"\s+", " ", sql or "").lower()
+ required_sql_clues = (
+ "cq_osm_roads_2021",
+ "cq_amap_poi_2024",
+ "bridge",
+ "st_length",
+ "geography",
+ "order by",
+ "desc",
+ "limit 1",
+ "st_dwithin",
+ "100",
+ "count",
+ )
+ return all(clue in sql_low for clue in required_sql_clues)
+
+
+def _rows_to_feature_collection(
+ rows: list[Any],
+ *,
+ property_keys: tuple[str, ...],
+) -> dict[str, Any]:
features = []
for row in rows:
geom = row.get("geometry_json")
@@ -432,3 +640,86 @@ def _jsonable(value: Any) -> Any:
ST_YMax(Box3D(ST_Collect(geometry))) AS max_lat
FROM extent
"""
+
+
+_LONGEST_BRIDGE_POI_MAP_SQL = """
+WITH longest_bridge AS (
+ SELECT osm_id, name, fclass, bridge, geometry,
+ ST_Length(geometry::geography) AS length_m
+ FROM cq_osm_roads_2021
+ WHERE bridge = 'T'
+ ORDER BY ST_Length(geometry::geography) DESC, osm_id
+ LIMIT 1
+),
+matched_pois AS (
+ SELECT DISTINCT ON (p."ID")
+ p."ID" AS poi_id,
+ p."名称" AS poi_name,
+ p."地址" AS address,
+ p."类型" AS poi_type,
+ ST_Distance(p.geometry::geography, lb.geometry::geography) AS distance_m,
+ p.geometry
+ FROM cq_amap_poi_2024 AS p
+ CROSS JOIN longest_bridge AS lb
+ WHERE ST_DWithin(p.geometry::geography, lb.geometry::geography, 100)
+ ORDER BY p."ID"
+),
+bridge_buffer AS (
+ SELECT osm_id, name, length_m,
+ ST_Buffer(geometry::geography, 100)::geometry AS geometry
+ FROM longest_bridge
+),
+map_view AS (
+ SELECT
+ ST_Y(ST_Centroid(geometry)) AS center_lat,
+ ST_X(ST_Centroid(geometry)) AS center_lng,
+ ST_XMin(Box3D(geometry)) AS min_lng,
+ ST_YMin(Box3D(geometry)) AS min_lat,
+ ST_XMax(Box3D(geometry)) AS max_lng,
+ ST_YMax(Box3D(geometry)) AS max_lat
+ FROM bridge_buffer
+),
+features AS (
+ SELECT
+ 'poi'::text AS feature_group,
+ p.poi_id,
+ p.poi_name,
+ p.address,
+ p.poi_type,
+ ROUND(p.distance_m::numeric, 2) AS distance_m,
+ NULL::text AS osm_id,
+ NULL::text AS bridge_name,
+ NULL::text AS fclass,
+ NULL::text AS bridge,
+ NULL::numeric AS length_m,
+ NULL::integer AS radius_m,
+ 'matched_poi'::text AS result_type,
+ p.geometry
+ FROM matched_pois AS p
+ UNION ALL
+ SELECT
+ 'bridge', NULL::bigint, NULL, NULL, NULL, NULL,
+ lb.osm_id, lb.name, lb.fclass, lb.bridge,
+ ROUND(lb.length_m::numeric, 2), NULL,
+ 'longest_bridge', lb.geometry
+ FROM longest_bridge AS lb
+ UNION ALL
+ SELECT
+ 'buffer', NULL::bigint, NULL, NULL, NULL, NULL,
+ bb.osm_id, bb.name, NULL, NULL,
+ ROUND(bb.length_m::numeric, 2), 100,
+ 'search_buffer', bb.geometry
+ FROM bridge_buffer AS bb
+)
+SELECT
+ f.feature_group, f.poi_id, f.poi_name, f.address, f.poi_type,
+ f.distance_m, f.osm_id, f.bridge_name, f.fclass, f.bridge,
+ f.length_m, f.radius_m, f.result_type,
+ ST_AsGeoJSON(f.geometry)::text AS geometry_json,
+ v.center_lat, v.center_lng,
+ v.min_lng, v.min_lat, v.max_lng, v.max_lat
+FROM features AS f
+CROSS JOIN map_view AS v
+ORDER BY CASE f.feature_group WHEN 'buffer' THEN 1 WHEN 'bridge' THEN 2 ELSE 3 END,
+ f.poi_id NULLS FIRST
+"""
diff --git a/data_agent/nl2sql_semantic_rewrite.py b/data_agent/nl2sql_semantic_rewrite.py
index 88010c5c..04319f74 100644
--- a/data_agent/nl2sql_semantic_rewrite.py
+++ b/data_agent/nl2sql_semantic_rewrite.py
@@ -597,7 +597,11 @@ def _table_can_replace_referenced_table(sql: str, current: TableInfo, candidate:
if not col.is_geometry
for token in col.ref_tokens
}
- return bool(current_cols & candidate_tokens)
+ # Unqualified columns cannot be assigned to a table safely with a regex.
+ # Only replace when the candidate covers the current relation's complete
+ # non-spatial schema; a loose overlap can corrupt a valid CTE before the
+ # unknown-column guard runs (for example roads -> POI on "高德POI").
+ return bool(current_cols and current_cols.issubset(candidate_tokens))
def _qualifiers_for_referenced_table(sql: str, table: TableInfo) -> list[str]:
diff --git a/data_agent/paper9_agent_evaluation.py b/data_agent/paper9_agent_evaluation.py
new file mode 100644
index 00000000..a1b6cf69
--- /dev/null
+++ b/data_agent/paper9_agent_evaluation.py
@@ -0,0 +1,236 @@
+"""Offline behavioral-contract evaluator for the Paper9 ADK tool loop."""
+
+from __future__ import annotations
+
+import math
+from collections import Counter
+from collections.abc import Iterable, Mapping
+from typing import Any
+
+PLANNING_TOOLS = {"world_model_v21_plan", "world_model_v21_pipeline"}
+REQUIRED_OPENING = [
+ "world_model_v21_status",
+ "paper9_inspect_resources",
+]
+
+
+def evaluate_paper9_tool_trajectory(
+ events: Iterable[Mapping[str, Any]],
+) -> dict[str, Any]:
+ """Validate bounded plan/audit/replan/commit behavior from function events."""
+
+ trace = [dict(event) for event in events]
+ names = [str(event.get("tool") or "") for event in trace]
+ violations: list[str] = []
+
+ if names[:2] != REQUIRED_OPENING:
+ violations.append(
+ "Trajectory must open with world_model_v21_status -> "
+ "paper9_inspect_resources."
+ )
+ if not trace:
+ violations.append("Trajectory is empty.")
+ return _report(names, violations, terminal="invalid")
+
+ status_response = _response_for(trace, "world_model_v21_status")
+ inspect_response = _response_for(trace, "paper9_inspect_resources")
+ version_compatible = bool(
+ (status_response.get("finals") or {}).get("version_compatible")
+ )
+ planning_ready = bool(inspect_response.get("planning_ready"))
+ if not version_compatible or not planning_ready:
+ if any(name in PLANNING_TOOLS for name in names):
+ violations.append("Planning executed despite a failed version/resource gate.")
+ terminal = "preflight_stop"
+ return _report(names, violations, terminal=terminal)
+
+ if len(names) < 3 or names[2] != "paper9_recall_verified_episodes":
+ violations.append("Verified episodic recall must run before planning.")
+
+ planning_indices = [i for i, name in enumerate(names) if name in PLANNING_TOOLS]
+ if not planning_indices:
+ violations.append("No Paper9 planning tool was called.")
+ if len(planning_indices) > 2:
+ violations.append("More than one replan was attempted.")
+
+ last_audit: Mapping[str, Any] | None = None
+ terminal = "incomplete"
+ for plan_number, plan_index in enumerate(planning_indices):
+ audit_index = plan_index + 1
+ if audit_index >= len(trace) or names[audit_index] != "paper9_audit_run":
+ violations.append("Every planning call must be immediately followed by an audit.")
+ continue
+ audit = trace[audit_index].get("response") or {}
+ if not isinstance(audit, Mapping):
+ audit = {}
+ last_audit = audit
+ attempt = audit.get("attempt")
+ if attempt != plan_number:
+ violations.append(
+ f"Audit attempt must be {plan_number} after planning call {plan_number + 1}."
+ )
+
+ passed = bool(audit.get("hard_constraint_passed"))
+ next_index = audit_index + 1
+ next_name = names[next_index] if next_index < len(names) else None
+ if passed:
+ if next_name != "paper9_commit_verified_episode":
+ violations.append("A passed audit must be followed by verified-memory commit.")
+ elif next_index != len(names) - 1:
+ violations.append("No tool may run after verified-memory commit.")
+ terminal = "verified_commit"
+ break
+
+ if audit.get("retryable") and plan_number == 0:
+ if next_name not in PLANNING_TOOLS:
+ violations.append("A retryable first failure must take the single replan branch.")
+ terminal = "replan"
+ else:
+ if next_name is not None:
+ violations.append("A non-retryable audit failure must stop for human review.")
+ terminal = "human_review"
+
+ commit_indices = [
+ i for i, name in enumerate(names) if name == "paper9_commit_verified_episode"
+ ]
+ if commit_indices and not (
+ last_audit and bool(last_audit.get("hard_constraint_passed"))
+ ):
+ violations.append("An unverified or failed run was committed to memory.")
+ if len(commit_indices) > 1:
+ violations.append("Verified memory was committed more than once.")
+
+ return _report(names, violations, terminal=terminal)
+
+
+def _response_for(
+ trace: list[dict[str, Any]], tool_name: str
+) -> Mapping[str, Any]:
+ for event in trace:
+ if event.get("tool") == tool_name and isinstance(event.get("response"), Mapping):
+ return event["response"]
+ return {}
+
+
+def _report(
+ names: list[str], violations: list[str], *, terminal: str
+) -> dict[str, Any]:
+ return {
+ "passed": not violations,
+ "terminal": terminal,
+ "tool_count": len(names),
+ "tool_trace": names,
+ "violations": violations,
+ }
+
+
+def summarize_repeated_agent_runs(
+ runs: Iterable[Mapping[str, Any]],
+ *,
+ pass_rate_threshold: float = 0.8,
+) -> dict[str, Any]:
+ """Aggregate stochastic ADK runs without hiding per-scenario failures."""
+
+ rows = [dict(run) for run in runs]
+ scenarios = sorted({str(row.get("scenario") or "unknown") for row in rows})
+ by_scenario = {
+ scenario: _summarize_run_group(
+ [row for row in rows if str(row.get("scenario") or "unknown") == scenario]
+ )
+ for scenario in scenarios
+ }
+ overall = _summarize_run_group(rows)
+ failed_scenarios = [
+ scenario
+ for scenario, summary in by_scenario.items()
+ if summary["pass_rate"] < pass_rate_threshold
+ ]
+ return {
+ "overall": overall,
+ "by_scenario": by_scenario,
+ "release_gate": {
+ "pass_rate_threshold": pass_rate_threshold,
+ "passed": bool(rows) and not failed_scenarios,
+ "failed_scenarios": failed_scenarios,
+ },
+ }
+
+
+def _summarize_run_group(rows: list[dict[str, Any]]) -> dict[str, Any]:
+ total = len(rows)
+ passed = sum(bool(row.get("passed")) for row in rows)
+ pass_rate = passed / total if total else 0.0
+ latencies = [
+ float(row["latency_ms"])
+ for row in rows
+ if row.get("latency_ms") is not None
+ ]
+ tool_counts = [
+ int(row.get("tool_count", len(row.get("tool_trace") or []))) for row in rows
+ ]
+ traces = [tuple(str(item) for item in row.get("tool_trace") or []) for row in rows]
+ trace_counts = Counter(" -> ".join(trace) for trace in traces)
+ terminal_counts = Counter(str(row.get("terminal") or "unknown") for row in rows)
+ return {
+ "total": total,
+ "passed": passed,
+ "pass_rate": pass_rate,
+ "confidence_95_wilson": list(_wilson_interval(passed, total)),
+ "mean_latency_ms": _mean(latencies),
+ "p95_latency_ms": _percentile(latencies, 0.95),
+ "mean_tool_count": _mean(tool_counts),
+ "exact_trace_consistency": max(trace_counts.values(), default=0) / total
+ if total
+ else 0.0,
+ "pairwise_trace_consistency": _pairwise_trace_consistency(traces),
+ "unique_trace_count": len(trace_counts),
+ "trace_distribution": dict(trace_counts),
+ "terminal_distribution": dict(terminal_counts),
+ "error_count": sum(bool(row.get("error")) for row in rows),
+ }
+
+
+def _wilson_interval(successes: int, total: int, z: float = 1.96) -> tuple[float, float]:
+ if total <= 0:
+ return (0.0, 0.0)
+ proportion = successes / total
+ denominator = 1 + z**2 / total
+ center = (proportion + z**2 / (2 * total)) / denominator
+ margin = (
+ z
+ * math.sqrt(
+ proportion * (1 - proportion) / total + z**2 / (4 * total**2)
+ )
+ / denominator
+ )
+ return (max(0.0, center - margin), min(1.0, center + margin))
+
+
+def _mean(values: list[float] | list[int]) -> float | None:
+ return sum(values) / len(values) if values else None
+
+
+def _percentile(values: list[float], quantile: float) -> float | None:
+ if not values:
+ return None
+ ordered = sorted(values)
+ position = (len(ordered) - 1) * quantile
+ lower = math.floor(position)
+ upper = math.ceil(position)
+ if lower == upper:
+ return ordered[lower]
+ weight = position - lower
+ return ordered[lower] * (1 - weight) + ordered[upper] * weight
+
+
+def _pairwise_trace_consistency(traces: list[tuple[str, ...]]) -> float:
+ if len(traces) < 2:
+ return 1.0 if traces else 0.0
+ scores: list[float] = []
+ for left_index, left in enumerate(traces):
+ left_steps = {f"{index}:{name}" for index, name in enumerate(left)}
+ for right in traces[left_index + 1 :]:
+ right_steps = {f"{index}:{name}" for index, name in enumerate(right)}
+ union = left_steps | right_steps
+ scores.append(len(left_steps & right_steps) / len(union) if union else 1.0)
+ return sum(scores) / len(scores)
diff --git a/data_agent/paper9_agent_governance.py b/data_agent/paper9_agent_governance.py
new file mode 100644
index 00000000..ab03f752
--- /dev/null
+++ b/data_agent/paper9_agent_governance.py
@@ -0,0 +1,289 @@
+"""Governed audit and verified episodic memory for Paper9 agent runs.
+
+The module is deliberately independent from Google ADK. ADK tools call these
+deterministic functions so model decisions remain observable while acceptance
+of a spatial plan stays under code-enforced domain constraints.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import os
+import threading
+from collections.abc import Mapping
+from dataclasses import asdict, dataclass
+from datetime import UTC, datetime
+from pathlib import Path
+from typing import Any
+
+EXPECTED_PACKAGE_VERSION = "0.3.3"
+EXPECTED_ALGORITHM_VERSION = "2.2.3"
+AUDIT_SCHEMA_VERSION = "paper9.agent_audit.v1"
+EPISODE_SCHEMA_VERSION = "paper9.verified_episode.v1"
+
+_store_lock = threading.Lock()
+
+
+@dataclass(frozen=True)
+class Paper9AuditPolicy:
+ """Hard gates and bounded recovery policy for a Paper9 plan."""
+
+ cultivated_area_floor_delta_ha: float = 0.0
+ require_slope_improvement: bool = True
+ require_contiguity_improvement: bool = True
+ max_replans: int = 1
+
+
+def _float_or_none(value: Any) -> float | None:
+ try:
+ return float(value) if value is not None else None
+ except (TypeError, ValueError):
+ return None
+
+
+def _sha256(path: Path) -> str | None:
+ if not path.is_file():
+ return None
+ digest = hashlib.sha256()
+ with path.open("rb") as handle:
+ for chunk in iter(lambda: handle.read(1024 * 1024), b""):
+ digest.update(chunk)
+ return digest.hexdigest()
+
+
+def evaluate_paper9_summary(
+ summary: Mapping[str, Any],
+ *,
+ policy: Paper9AuditPolicy | None = None,
+ attempt: int = 0,
+) -> dict[str, Any]:
+ """Evaluate Paper9 output and return a machine-actionable recovery branch."""
+
+ policy = policy or Paper9AuditPolicy()
+ raw_records = summary.get("results") or summary.get("records") or []
+ records: list[dict[str, Any]] = []
+ failures: list[str] = []
+
+ if not isinstance(raw_records, list) or not raw_records:
+ failures.append("No MPC result records were produced.")
+ else:
+ for index, raw in enumerate(raw_records):
+ if not isinstance(raw, Mapping):
+ failures.append(f"Episode {index} is not a structured result record.")
+ continue
+ episode = raw.get("episode", index)
+ cultivated = _float_or_none(raw.get("cultivated_area_change_ha"))
+ slope = _float_or_none(raw.get("slope_change_pct"))
+ contiguity = _float_or_none(raw.get("cont_change"))
+ record = {
+ "episode": episode,
+ "cultivated_area_change_ha": cultivated,
+ "slope_change_pct": slope,
+ "cont_change": contiguity,
+ "baimu_count_change": _float_or_none(raw.get("baimu_count_change")),
+ "baimu_area_change_ha": _float_or_none(raw.get("baimu_area_change_ha")),
+ "total_reward": _float_or_none(raw.get("total_reward")),
+ "steps_run": raw.get("steps_run"),
+ }
+ records.append(record)
+
+ if cultivated is None:
+ failures.append(f"Episode {episode} is missing cultivated_area_change_ha.")
+ elif cultivated < policy.cultivated_area_floor_delta_ha:
+ failures.append(
+ f"Episode {episode} cultivated area delta {cultivated:.6f} ha is below "
+ f"the required {policy.cultivated_area_floor_delta_ha:.6f} ha."
+ )
+ if policy.require_slope_improvement:
+ if slope is None:
+ failures.append(f"Episode {episode} is missing slope_change_pct.")
+ elif slope >= 0:
+ failures.append(
+ f"Episode {episode} did not reduce mean cultivated-land slope."
+ )
+ if policy.require_contiguity_improvement:
+ if contiguity is None:
+ failures.append(f"Episode {episode} is missing cont_change.")
+ elif contiguity <= 0:
+ failures.append(
+ f"Episode {episode} did not improve cultivated-land contiguity."
+ )
+
+ passed = not failures
+ can_replan = bool(records) and attempt < policy.max_replans
+ if passed:
+ next_action = "commit_verified_episode"
+ recovery_hints: list[str] = []
+ elif can_replan:
+ next_action = "replan_once"
+ recovery_hints = [
+ "Keep cultivated_area_floor_delta_ha at or above 0.",
+ "Change only planning search parameters supported by the current ensemble.",
+ "Run the hard-gate audit again before presenting or committing the result.",
+ ]
+ else:
+ next_action = "stop_and_request_human_review"
+ recovery_hints = [
+ "Do not present the run as successful.",
+ "Preserve the failed audit and tool trace for review.",
+ ]
+
+ return {
+ "schema_version": AUDIT_SCHEMA_VERSION,
+ "hard_constraint_passed": passed,
+ "failure_reasons": failures,
+ "records": records,
+ "policy": asdict(policy),
+ "attempt": attempt,
+ "retryable": can_replan and not passed,
+ "next_action": next_action,
+ "recovery_hints": recovery_hints,
+ }
+
+
+def audit_paper9_run(
+ out_dir: str | Path,
+ *,
+ policy: Paper9AuditPolicy | None = None,
+ attempt: int = 0,
+ write: bool = True,
+) -> dict[str, Any]:
+ """Audit one Paper9 output directory and optionally persist the decision."""
+
+ root = Path(out_dir).expanduser().resolve()
+ if not root.is_dir():
+ raise ValueError(f"out_dir not found: {root}")
+
+ mpc_path = root / "mpc_summary.json"
+ upstream_audit_path = root / "audit_summary.json"
+ if mpc_path.is_file():
+ summary = json.loads(mpc_path.read_text(encoding="utf-8"))
+ source_path = mpc_path
+ elif upstream_audit_path.is_file():
+ upstream = json.loads(upstream_audit_path.read_text(encoding="utf-8"))
+ summary = {"results": (upstream.get("constraint_status") or {}).get("records", [])}
+ source_path = upstream_audit_path
+ else:
+ raise ValueError(f"No mpc_summary.json or audit_summary.json under {root}")
+
+ result = evaluate_paper9_summary(summary, policy=policy, attempt=attempt)
+ spatial_candidates = [
+ root / "optimized_dltb.shp",
+ root / "DLTB_optimized.shp",
+ root / "optimized_dltb.fgb",
+ root / "mpc_land_use.npy",
+ ]
+ artifacts = {
+ "summary": {
+ "path": str(source_path),
+ "exists": True,
+ "sha256": _sha256(source_path),
+ },
+ "optimized_spatial_result": {
+ "path": next((str(path) for path in spatial_candidates if path.is_file()), None),
+ "exists": any(path.is_file() for path in spatial_candidates),
+ },
+ }
+ result["out_dir"] = str(root)
+ result["artifacts"] = artifacts
+ result["all_expected_outputs_exist"] = artifacts["optimized_spatial_result"]["exists"]
+ if not result["all_expected_outputs_exist"]:
+ result["hard_constraint_passed"] = False
+ result["failure_reasons"].append("No optimized spatial result artifact was found.")
+ result["retryable"] = False
+ result["next_action"] = "stop_and_request_human_review"
+ result["recovery_hints"] = [
+ "Do not present the run as successful.",
+ "Repair output generation before rerunning the planning stage.",
+ ]
+
+ if write:
+ audit_path = root / "paper9_agent_audit.json"
+ audit_path.write_text(
+ json.dumps(result, ensure_ascii=False, indent=2, sort_keys=True) + "\n",
+ encoding="utf-8",
+ )
+ result["audit_path"] = str(audit_path)
+ return result
+
+
+class Paper9EpisodeStore:
+ """Append-only store containing only hard-gate-verified Paper9 episodes."""
+
+ def __init__(self, path: str | Path | None = None):
+ default = (
+ Path.home()
+ / ".gis-data-agent"
+ / "paper9"
+ / "verified_episodes.jsonl"
+ )
+ self.path = Path(path or os.environ.get("PAPER9_AGENT_MEMORY_PATH") or default)
+
+ def commit(
+ self,
+ *,
+ audit: Mapping[str, Any],
+ dataset: str,
+ goal: str,
+ plan_args: Mapping[str, Any] | None = None,
+ provenance: Mapping[str, Any] | None = None,
+ ) -> dict[str, Any]:
+ if not audit.get("hard_constraint_passed"):
+ raise ValueError("Only hard-gate-passed runs may enter verified memory.")
+ if not audit.get("all_expected_outputs_exist"):
+ raise ValueError("Verified memory requires complete spatial output artifacts.")
+
+ stable = {
+ "dataset": str(dataset or "unknown").strip().lower(),
+ "goal": str(goal or "").strip(),
+ "out_dir": audit.get("out_dir"),
+ "summary_sha256": ((audit.get("artifacts") or {}).get("summary") or {}).get(
+ "sha256"
+ ),
+ "plan_args": dict(plan_args or {}),
+ }
+ episode_id = hashlib.sha256(
+ json.dumps(stable, ensure_ascii=False, sort_keys=True).encode("utf-8")
+ ).hexdigest()[:20]
+ record = {
+ "schema_version": EPISODE_SCHEMA_VERSION,
+ "episode_id": episode_id,
+ "verified_at": datetime.now(UTC).isoformat(),
+ **stable,
+ "audit": {
+ "policy": audit.get("policy"),
+ "records": audit.get("records"),
+ "hard_constraint_passed": True,
+ },
+ "provenance": dict(provenance or {}),
+ }
+
+ self.path.parent.mkdir(parents=True, exist_ok=True)
+ with _store_lock:
+ existing = {item.get("episode_id") for item in self.recall(limit=10_000)}
+ if episode_id not in existing:
+ with self.path.open("a", encoding="utf-8") as handle:
+ handle.write(json.dumps(record, ensure_ascii=False, sort_keys=True) + "\n")
+ handle.flush()
+ os.fsync(handle.fileno())
+ record["already_existed"] = episode_id in existing
+ record["memory_path"] = str(self.path)
+ return record
+
+ def recall(self, *, dataset: str = "", limit: int = 3) -> list[dict[str, Any]]:
+ if not self.path.is_file():
+ return []
+ dataset_key = str(dataset or "").strip().lower()
+ records: list[dict[str, Any]] = []
+ for line in self.path.read_text(encoding="utf-8").splitlines():
+ try:
+ item = json.loads(line)
+ except json.JSONDecodeError:
+ continue
+ if item.get("schema_version") != EPISODE_SCHEMA_VERSION:
+ continue
+ if dataset_key and item.get("dataset") != dataset_key:
+ continue
+ records.append(item)
+ return records[-max(0, int(limit)) :][::-1]
diff --git a/data_agent/paper9_agent_prompt.py b/data_agent/paper9_agent_prompt.py
new file mode 100644
index 00000000..88cb9988
--- /dev/null
+++ b/data_agent/paper9_agent_prompt.py
@@ -0,0 +1,37 @@
+"""Single source of truth for the finals Paper9 agent behavior contract."""
+
+PAPER9_AGENT_INSTRUCTION = (
+ "你是由 Gemma 4 驱动、运行于 Google ADK 的 Paper9 受控自主规划代理。"
+ "只使用 WorldModelV21Toolset 暴露的 10 个工具，不得声称调用未发生的工具或结果。"
+ "每个新任务必须先调用 world_model_v21_status，再调用 paper9_inspect_resources，"
+ "检查 Paper9 包版本 0.3.3、算法版本 2.2.3、prepared_dir 和 ensemble_dir。"
+ "即使 status 已显示版本不兼容，也必须继续调用 paper9_inspect_resources 记录资源缺口，"
+ "然后停止。"
+ "版本不兼容或 planning_ready=false 时必须停止并明确报告缺口，不能降级为旧算法后称为成功。"
+ "资源就绪后调用 paper9_recall_verified_episodes；没有历史经验是正常情况。"
+ "如果用户要求完整 A->B->C->D 链路，调用 world_model_v21_pipeline；"
+ "如果用户只要求规划或演示，默认调用 world_model_v21_pipeline 并设置 reuse_existing=true，"
+ "首次 pipeline 必须设置 cultivated_area_floor_delta_ha='0'，将耕地面积不减少作为规划约束，"
+ "用实际状态决定复用 Tool 1/2/3 并执行 Tool 4；只有用户明确要求“只运行 Tool 4”"
+ "或“只调用 plan”时，才调用 world_model_v21_plan。每次规划完成后必须将返回的 out_dir"
+ "传给 paper9_audit_run，首次审计 attempt=0。"
+ "若审计 next_action=commit_verified_episode，调用 paper9_commit_verified_episode 后再总结。"
+ "若审计 next_action=replan_once，只允许再调用一次 world_model_v21_plan，保持"
+ "cultivated_area_floor_delta_ha>=0，仅调整受支持的搜索参数，再以 attempt=1 审计。"
+ "第二次仍失败或审计要求 stop_and_request_human_review 时立即停止、保留失败证据并请求人工复核。"
+ "严禁未审计成功就写入经验，严禁超过一次重规划，严禁把 warning 或失败描述为成功。"
+ "必须识别数据集名称：用户提到 dongxing 或东兴时，调用工具必须设置 dataset='dongxing'；"
+ "用户提到 Bishan 或璧山时，调用工具必须设置 dataset='bishan'。"
+ "只有用户没有提到数据集时，才使用默认 dataset='bishan'。"
+ "用户未提供 prepared_dir 或 ensemble_dir 时，对应参数保持空字符串，"
+ "让工具根据 dataset 选择演示路径。"
+ "默认使用 env_kind=county, "
+ "horizon=1, top_k=1, n_episodes=1, continuation=greedy, scoring=reward，"
+ "除非用户明确指定其他参数。最终用中文简要总结 package_version/algorithm_version/"
+ "mode/env_kind/steps_run/n_blocks/n_selected/total_reward/硬约束审计/经验提交状态/artifacts，"
+ "并按实际发生顺序列出工具调用轨迹。不要输出英文 Plan/Step、"
+ "思考过程、参数复述或重试纠错过程；如果工具重试后成功，只报告最终成功结果。"
+ "说明 horizon 是 MPC 前瞻步长，steps_run 是环境实际执行步数，两者不是同一个指标。"
+ "如果结果包含县域优化地图图层，明确说明右侧地图按 CHG_FLAG 展示优化变化："
+ "灰色为保持不变，红色为耕地 -> 林地，绿色为林地 -> 耕地。"
+)
diff --git a/data_agent/platform_truth.py b/data_agent/platform_truth.py
index c5ce4fe7..f91f855d 100644
--- a/data_agent/platform_truth.py
+++ b/data_agent/platform_truth.py
@@ -538,7 +538,7 @@ def _config(
# Fingerprint of literal environment reads in production Python modules. It is
# intentionally updated only with an explicit config-contract review.
ENV_ACCESS_BASELINE_FINGERPRINT = (
- "41949811ca1d12a9d8bdbd5e7ecb1ba528be7049af96742306d6f317ab0791b8"
+ "e092d287c0db895fe0c8b2f1bd04354ad6e59377efef25516b5c3c64ad3fce9a"
)
RUNTIME_PRIMITIVE_BASELINE_FINGERPRINT = (
"724d0c4751f92637e366602848b511c432afc9dcf10c723178f25daab9cf61e4"
diff --git a/data_agent/report_generator.py b/data_agent/report_generator.py
index 88515a4d..1115ba0a 100644
--- a/data_agent/report_generator.py
+++ b/data_agent/report_generator.py
@@ -586,6 +586,17 @@ def generate_pdf_report(
docx_path = output_path.rsplit('.', 1)[0] + '.docx'
generate_word_report(markdown_text, docx_path, title, author, logo_path, pipeline_type)
+ return convert_word_to_pdf(docx_path, output_path, remove_source=True)
+
+
+def convert_word_to_pdf(
+ docx_path: str,
+ output_path: str,
+ *,
+ remove_source: bool = False,
+) -> str:
+ """Convert an existing Word report to PDF using the configured office backend."""
+
output_abs = os.path.abspath(output_path)
out_dir = os.path.dirname(output_abs) or os.getcwd()
os.makedirs(out_dir, exist_ok=True)
@@ -633,10 +644,11 @@ def generate_pdf_report(
if os.path.abspath(generated_pdf) != output_abs and os.path.exists(generated_pdf):
os.replace(generated_pdf, output_abs)
if os.path.exists(output_abs):
- try:
- os.remove(docx_path)
- except OSError:
- pass
+ if remove_source:
+ try:
+ os.remove(docx_path)
+ except OSError:
+ pass
return output_abs
raise RuntimeError(f"LibreOffice did not create PDF: {output_abs}")
except Exception as e:
@@ -648,10 +660,11 @@ def generate_pdf_report(
convert(docx_path, output_abs)
if os.path.exists(output_abs):
- try:
- os.remove(docx_path)
- except OSError:
- pass
+ if remove_source:
+ try:
+ os.remove(docx_path)
+ except OSError:
+ pass
return output_abs
raise RuntimeError(f"docx2pdf did not create PDF: {output_abs}")
except Exception as e:
diff --git a/data_agent/test_finals_demo_verifier.py b/data_agent/test_finals_demo_verifier.py
new file mode 100644
index 00000000..8b667a22
--- /dev/null
+++ b/data_agent/test_finals_demo_verifier.py
@@ -0,0 +1,60 @@
+import json
+
+from scripts.verify_gemma4_finals_demo import _paper9_data_checks
+
+
+def test_paper9_checks_read_metrics_from_committed_run(tmp_path):
+ final_run = tmp_path / "final-run"
+ final_run.mkdir()
+ (final_run / "mpc_summary.json").write_text(
+ json.dumps(
+ {
+ "results": [
+ {
+ "steps_run": 100,
+ "swaps_completed": 412,
+ "cultivated_area_change_ha": 0.06,
+ "slope_change_pct": -0.62,
+ "cont_change": 0.02,
+ "baimu_area_change_ha": 32.5,
+ }
+ ],
+ "shapefile_output": {
+ "n_input": 101657,
+ "n_in_env": 53004,
+ "n_farm_to_forest": 412,
+ "n_forest_to_farm": 412,
+ "land_use_code_scheme": "legacy_three_digit_test_data",
+ },
+ }
+ ),
+ encoding="utf-8",
+ )
+ pipeline = {
+ "status": "ok",
+ "plan_result": {
+ "status": "ok",
+ "out_dir": str(tmp_path / "failed-first-run"),
+ "summary": {
+ "results": [{"cultivated_area_change_ha": -489.02}]
+ },
+ },
+ }
+ audit = {
+ "out_dir": str(final_run),
+ "hard_constraint_passed": True,
+ "all_expected_outputs_exist": True,
+ "artifacts": {"optimized_spatial_result": {"exists": True}},
+ }
+ commit = {
+ "status": "committed",
+ "episode": {"episode_id": "episode-1", "out_dir": str(final_run)},
+ }
+
+ checks, metrics = _paper9_data_checks(pipeline, audit, commit)
+
+ assert all(checks.values())
+ assert metrics["out_dir"] == str(final_run)
+ assert metrics["cultivated_area_change_ha"] == 0.06
+ assert metrics["swaps_completed"] == 412
+ assert metrics["episode_id"] == "episode-1"
diff --git a/data_agent/test_finals_deployment_contract.py b/data_agent/test_finals_deployment_contract.py
new file mode 100644
index 00000000..e4bc55ee
--- /dev/null
+++ b/data_agent/test_finals_deployment_contract.py
@@ -0,0 +1,70 @@
+import subprocess
+import sys
+from pathlib import Path
+
+from data_agent.model_gateway import ModelRegistry
+from data_agent.paper9_agent_prompt import PAPER9_AGENT_INSTRUCTION
+from scripts.check_gemma4_finals_preflight import (
+ _default_bishan_runs,
+ _default_repo,
+)
+
+ROOT = Path(__file__).resolve().parents[1]
+
+
+def test_finals_compose_uses_configurable_paper9_033_mount():
+ compose = (ROOT / "docker-compose.gemma4-demo.yml").read_text(encoding="utf-8")
+
+ assert "${PAPER9_HOST_REPO:-./vendor/paper9-mnr-offline-package}" in compose
+ assert "${PAPER9_BISHAN_RUNS_HOST:-./demo-data/bishan}" in compose
+ assert "${PAPER9_DONGXING_RUNS_HOST:-./demo-data/dongxing}" in compose
+ assert "/Users/" not in compose
+
+
+def test_finals_env_template_declares_every_host_mount():
+ template = (ROOT / ".env.finals.example").read_text(encoding="utf-8")
+
+ assert "PAPER9_HOST_REPO=" in template
+ assert "PAPER9_BISHAN_RUNS_HOST=" in template
+ assert "PAPER9_DONGXING_RUNS_HOST=" in template
+
+
+def test_gemma4_demo_model_disables_thinking_for_tool_loops():
+ info = ModelRegistry.get_model_info("gemma4-26b-ollama")
+
+ assert info["model_id"] == "ollama_chat/Gemma4:26b"
+ assert info["extra_body"] == {"think": False}
+ assert info["request_timeout"] == 600
+
+
+def test_paper9_prompt_requires_complete_preflight_even_on_version_failure():
+ assert "即使 status 已显示版本不兼容" in PAPER9_AGENT_INSTRUCTION
+ assert "paper9_inspect_resources" in PAPER9_AGENT_INSTRUCTION
+ assert "首次 pipeline 必须设置 cultivated_area_floor_delta_ha='0'" in PAPER9_AGENT_INSTRUCTION
+ assert "严禁超过一次重规划" in PAPER9_AGENT_INSTRUCTION
+
+
+def test_finals_preflight_script_imports_when_started_from_scripts_directory():
+ result = subprocess.run(
+ [sys.executable, "check_gemma4_finals_preflight.py", "--help"],
+ cwd=ROOT / "scripts",
+ capture_output=True,
+ text=True,
+ check=False,
+ )
+
+ assert result.returncode == 0, result.stderr
+ assert "--paper9-repo" in result.stdout
+
+
+def test_finals_preflight_defaults_to_runtime_mounts(monkeypatch):
+ monkeypatch.delenv("PAPER9_HOST_REPO", raising=False)
+ monkeypatch.delenv("PAPER9_BISHAN_RUNS_HOST", raising=False)
+ monkeypatch.setenv("PAPER9_FARMLAND_MPC_REPO", "/app/paper9-demo")
+ monkeypatch.setenv(
+ "PAPER9_FARMLAND_MPC_DEFAULT_PREPARED_DIR",
+ "/app/bishan-runs/prepared",
+ )
+
+ assert _default_repo() == Path("/app/paper9-demo")
+ assert _default_bishan_runs() == Path("/app/bishan-runs")
diff --git a/data_agent/test_finals_preflight.py b/data_agent/test_finals_preflight.py
new file mode 100644
index 00000000..3adb260d
--- /dev/null
+++ b/data_agent/test_finals_preflight.py
@@ -0,0 +1,59 @@
+from data_agent.finals_preflight import inspect_finals_host
+
+
+def _build_resources(tmp_path, *, package_version="0.3.3", algorithm_version="2.2.3"):
+ repo = tmp_path / "paper9"
+ version_file = repo / "src" / "paper9_mnr" / "version.py"
+ version_file.parent.mkdir(parents=True)
+ (repo / "pyproject.toml").write_text(
+ f'[project]\nname = "paper9"\nversion = "{package_version}"\n',
+ encoding="utf-8",
+ )
+ version_file.write_text(
+ f'ALGORITHM_VERSION = "{algorithm_version}"\n', encoding="utf-8"
+ )
+
+ runs = tmp_path / "bishan"
+ prepared = runs / "prepared"
+ dltb = prepared / "dem_slope_analysis" / "output" / "DLTB_with_slope.shp"
+ dltb.parent.mkdir(parents=True)
+ dltb.write_bytes(b"shp")
+ ensemble = prepared / "ensemble_seed0"
+ ensemble.mkdir()
+ (ensemble / "member_0.onnx").write_bytes(b"onnx")
+ return repo, runs
+
+
+def test_finals_host_preflight_passes_with_exact_versions_and_model(tmp_path):
+ repo, runs = _build_resources(tmp_path)
+
+ report = inspect_finals_host(
+ paper9_repo=repo,
+ bishan_runs=runs,
+ model_tag="Gemma4:26b",
+ ollama_tags=["Gemma4:26b", "Gemma4:12b"],
+ )
+
+ assert report["ready"] is True
+ assert report["failed_checks"] == []
+ ensemble = next(
+ check for check in report["checks"] if check["name"] == "bishan_onnx_ensemble"
+ )
+ assert ensemble["actual"]["member_count"] == 1
+
+
+def test_finals_host_preflight_fails_closed_on_version_or_model_mismatch(tmp_path):
+ repo, runs = _build_resources(tmp_path, package_version="0.2.1")
+
+ report = inspect_finals_host(
+ paper9_repo=repo,
+ bishan_runs=runs,
+ model_tag="Gemma4:26b",
+ ollama_tags=["Gemma4:12b"],
+ )
+
+ assert report["ready"] is False
+ assert report["failed_checks"] == [
+ "paper9_package_version",
+ "ollama_model_tag",
+ ]
diff --git a/data_agent/test_model_gateway.py b/data_agent/test_model_gateway.py
index 15a0314f..6664f39a 100644
--- a/data_agent/test_model_gateway.py
+++ b/data_agent/test_model_gateway.py
@@ -201,7 +201,10 @@ def test_create_deepseek_model_sets_openai_compat_env(self, mock_litellm):
})
self.assertEqual(os.environ["OPENAI_API_BASE"], "https://api.deepseek.com")
self.assertEqual(os.environ["OPENAI_API_KEY"], "test-key")
- mock_litellm.assert_called_once_with(model="openai/deepseek-v4-flash")
+ mock_litellm.assert_called_once_with(
+ model="openai/deepseek-v4-flash",
+ extra_body={"thinking": {"type": "disabled"}},
+ )
def test_create_deepseek_model_requires_dedicated_api_key(self):
from data_agent.model_gateway import _create_deepseek_model
@@ -276,7 +279,7 @@ def test_route_prefer_offline(self):
from data_agent.model_gateway import ModelRouter
router = ModelRouter()
result = router.route(prefer_offline=True)
- self.assertEqual(result, "gemma-3-4b")
+ self.assertEqual(result, "gemma4-26b-ollama")
def test_route_with_task_type(self):
from data_agent.model_gateway import ModelRouter
@@ -304,9 +307,15 @@ class TestAgentIntegration(unittest.TestCase):
@patch("data_agent.model_gateway.create_model")
def test_create_model_with_retry_delegates(self, mock_create):
- mock_create.return_value = MagicMock()
+ # agent.py constructs module-level ADK agents during import. Return a
+ # valid model identifier so current ADK type validation can complete.
+ mock_create.return_value = "gemini-2.5-flash"
from data_agent.agent import _create_model_with_retry
- _create_model_with_retry("gemini-2.5-flash")
+
+ mock_create.reset_mock()
+ result = _create_model_with_retry("gemini-2.5-flash")
+
+ self.assertEqual(result, "gemini-2.5-flash")
mock_create.assert_called_once_with("gemini-2.5-flash")
diff --git a/data_agent/test_nl2sql_presentation.py b/data_agent/test_nl2sql_presentation.py
index c5a7baea..7c392063 100644
--- a/data_agent/test_nl2sql_presentation.py
+++ b/data_agent/test_nl2sql_presentation.py
@@ -1,10 +1,14 @@
import json
from decimal import Decimal
+from unittest.mock import patch
from data_agent.nl2sql_presentation import (
- _is_bridge_building_intersection_query,
_estimate_zoom_from_extent,
+ _is_bridge_building_intersection_query,
+ _is_longest_bridge_poi_query,
_rows_to_feature_collection,
+ build_longest_bridge_poi_map_update,
+ build_nl2sql_map_update,
describe_map_update,
format_nl2sql_result_for_chat,
parse_nl2sql_payload,
@@ -37,6 +41,131 @@ def _bridge_count_payload() -> dict:
}
+def _longest_bridge_poi_payload(count: int = 35) -> dict:
+ return {
+ "status": "ok",
+ "sql": (
+ "WITH longest_bridge AS ("
+ "SELECT geometry FROM cq_osm_roads_2021 WHERE bridge = 'T' "
+ "ORDER BY ST_LENGTH(CAST(geometry AS GEOGRAPHY)) DESC LIMIT 1"
+ ') SELECT COUNT(DISTINCT p."ID") FROM cq_amap_poi_2024 AS p, '
+ "longest_bridge AS lb WHERE "
+ "ST_DWITHIN(CAST(p.geometry AS GEOGRAPHY), CAST(lb.geometry AS GEOGRAPHY), 100)"
+ ),
+ "execution": {
+ "status": "ok",
+ "rows": 1,
+ "columns": ["count"],
+ "data": [{"count": count}],
+ "message": "查询成功，返回 1 行",
+ },
+ "semantic": {"candidate_tables": ["cq_amap_poi_2024", "cq_osm_roads_2021"]},
+ }
+
+
+def _longest_bridge_map_rows(poi_count: int = 35) -> list[dict]:
+ view = {
+ "center_lat": Decimal("29.7021"),
+ "center_lng": Decimal("107.8132"),
+ "min_lng": Decimal("107.801"),
+ "min_lat": Decimal("29.695"),
+ "max_lng": Decimal("107.825"),
+ "max_lat": Decimal("29.709"),
+ }
+ rows = [
+ {
+ "feature_group": "poi",
+ "poi_id": idx,
+ "poi_name": f"POI {idx}",
+ "address": "测试地址",
+ "poi_type": "测试类型",
+ "distance_m": Decimal("42.50"),
+ "result_type": "matched_poi",
+ "geometry_json": json.dumps({"type": "Point", "coordinates": [107.81, 29.70]}),
+ **view,
+ }
+ for idx in range(1, poi_count + 1)
+ ]
+ rows.extend([
+ {
+ "feature_group": "bridge",
+ "osm_id": "708725252",
+ "bridge_name": "龙溪河大道",
+ "fclass": "primary",
+ "bridge": "T",
+ "length_m": Decimal("2167.57"),
+ "result_type": "longest_bridge",
+ "geometry_json": json.dumps({
+ "type": "LineString",
+ "coordinates": [[107.8, 29.7], [107.82, 29.7]],
+ }),
+ **view,
+ },
+ {
+ "feature_group": "buffer",
+ "osm_id": "708725252",
+ "bridge_name": "龙溪河大道",
+ "length_m": Decimal("2167.57"),
+ "radius_m": 100,
+ "result_type": "search_buffer",
+ "geometry_json": json.dumps({
+ "type": "Polygon",
+ "coordinates": [[
+ [107.8, 29.69],
+ [107.82, 29.69],
+ [107.82, 29.71],
+ [107.8, 29.69],
+ ]],
+ }),
+ **view,
+ },
+ ])
+ return rows
+
+
+class _FakeMappings:
+ def __init__(self, rows: list[dict]):
+ self._rows = rows
+
+ def all(self) -> list[dict]:
+ return self._rows
+
+
+class _FakeResult:
+ def __init__(self, rows: list[dict]):
+ self._rows = rows
+
+ def mappings(self) -> _FakeMappings:
+ return _FakeMappings(self._rows)
+
+
+class _FakeConnection:
+ def __init__(self, rows: list[dict], *, fail: bool = False):
+ self._rows = rows
+ self._fail = fail
+
+ def __enter__(self):
+ return self
+
+ def __exit__(self, *_args):
+ return None
+
+ def execute(self, statement):
+ assert "single, consistent PostGIS snapshot" not in str(statement)
+ if self._fail:
+ raise RuntimeError("database unavailable")
+ return _FakeResult(self._rows)
+
+
+class _FakeEngine:
+ def __init__(self, rows: list[dict], *, fail: bool = False):
+ self._rows = rows
+ self._fail = fail
+
+ def connect(self) -> _FakeConnection:
+ return _FakeConnection(self._rows, fail=self._fail)
+
+
def test_parse_nl2sql_payload_unwraps_function_response_result():
payload = _bridge_count_payload()
wrapped = {"result": json.dumps(payload, ensure_ascii=False)}
@@ -132,6 +261,132 @@ def test_bridge_building_query_detection_requires_all_spatial_clues():
)
+def test_longest_bridge_poi_query_detection_is_locked_to_prompt_and_sql():
+ payload = _longest_bridge_poi_payload()
+
+ assert _is_longest_bridge_poi_query(
+ "@NL2SQL 统计距离道路网络中最长桥梁 100 米范围内的高德 POI 数量。",
+ payload["sql"],
+ )
+ assert not _is_longest_bridge_poi_query(
+ "统计距离任意桥梁100米范围内的高德POI数量。",
+ payload["sql"],
+ )
+ assert not _is_longest_bridge_poi_query(
+ "统计距离道路网络中最长桥梁100米范围内的高德POI数量。",
+ "SELECT COUNT(*) FROM cq_amap_poi_2024",
+ )
+
+
+def test_longest_bridge_poi_map_has_three_snapshot_consistent_layers(tmp_path):
+ rows = _longest_bridge_map_rows()
+ with (
+ patch("data_agent.db_engine.get_engine", return_value=_FakeEngine(rows)),
+ patch("data_agent.database_tools._inject_user_context"),
+ ):
+ result = build_longest_bridge_poi_map_update(
+ _longest_bridge_poi_payload(),
+ question="统计距离道路网络中最长桥梁100米范围内的高德POI数量。",
+ upload_dir=str(tmp_path),
+ )
+
+ assert result is not None
+ assert [layer["type"] for layer in result["layers"]] == ["polygon", "line", "point"]
+ assert result["summary"]["scalar_poi_count"] == 35
+ assert result["summary"]["poi_feature_count"] == 35
+ assert result["summary"]["bridge_feature_count"] == 1
+ assert result["summary"]["buffer_feature_count"] == 1
+ assert result["summary"]["geometry_snapshot"] == "single_postgis_statement"
+ assert result["summary"]["bridge_osm_id"] == "708725252"
+
+ poi_layer = next(layer for layer in result["layers"] if layer["type"] == "point")
+ poi_geojson = json.loads((tmp_path / poi_layer["geojson"]).read_text(encoding="utf-8"))
+ assert len(poi_geojson["features"]) == 35
+ assert poi_geojson["features"][0]["properties"]["result_type"] == "matched_poi"
+ assert all((tmp_path / layer["geojson"]).is_file() for layer in result["layers"])
+
+ description = describe_map_update(result)
+ assert "高德 POI 35 个" in description
+ assert "最长桥梁 1 条" in description
+ assert "同一 PostGIS 查询快照" in description
+
+
+def test_longest_bridge_poi_map_skips_scalar_feature_count_mismatch(tmp_path):
+ engine = _FakeEngine(_longest_bridge_map_rows(34))
+ with (
+ patch("data_agent.db_engine.get_engine", return_value=engine),
+ patch("data_agent.database_tools._inject_user_context"),
+ ):
+ result = build_longest_bridge_poi_map_update(
+ _longest_bridge_poi_payload(count=35),
+ question="统计距离道路网络中最长桥梁100米范围内的高德POI数量。",
+ upload_dir=str(tmp_path),
+ )
+
+ assert result is None
+ assert list(tmp_path.iterdir()) == []
+
+
+def test_nl2sql_map_dispatcher_does_not_trigger_for_unrelated_query(tmp_path):
+ payload = {
+ "status": "ok",
+ "sql": "SELECT COUNT(*) FROM cq_amap_poi_2024",
+ "execution": {"data": [{"count": 1194351}]},
+ }
+ with patch("data_agent.db_engine.get_engine") as get_engine:
+ result = build_nl2sql_map_update(
+ payload,
+ question="统计高德POI总数。",
+ upload_dir=str(tmp_path),
+ )
+
+ assert result is None
+ get_engine.assert_not_called()
+
+
+def test_nl2sql_map_dispatcher_preserves_bridge_building_fallback(tmp_path):
+ expected = {"layers": [{"name": "existing bridge/building layer"}]}
+ with (
+ patch(
+ "data_agent.nl2sql_presentation.build_longest_bridge_poi_map_update",
+ return_value=None,
+ ) as longest_builder,
+ patch(
+ "data_agent.nl2sql_presentation.build_bridge_building_map_update",
+ return_value=expected,
+ ) as building_builder,
+ ):
+ result = build_nl2sql_map_update(
+ _bridge_count_payload(),
+ question="统计与桥梁相交的建筑物数量",
+ upload_dir=str(tmp_path),
+ )
+
+ assert result is expected
+ longest_builder.assert_called_once()
+ building_builder.assert_called_once()
+
+
+def test_longest_bridge_poi_map_database_failure_preserves_scalar_path(tmp_path):
+ with (
+ patch("data_agent.db_engine.get_engine", return_value=_FakeEngine([], fail=True)),
+ patch("data_agent.database_tools._inject_user_context"),
+ ):
+ result = build_longest_bridge_poi_map_update(
+ _longest_bridge_poi_payload(),
+ question="统计距离道路网络中最长桥梁100米范围内的高德POI数量。",
+ upload_dir=str(tmp_path),
+ )
+
+ assert result is None
+ chat_text = format_nl2sql_result_for_chat(
+ _longest_bridge_poi_payload(),
+ question="统计距离道路网络中最长桥梁100米范围内的高德POI数量。",
+ )
+ assert chat_text is not None
+ assert "**POI数量：35**" in chat_text
+
+
def test_rows_to_feature_collection_converts_decimal_properties():
rows = [
{
diff --git a/data_agent/test_nl2sql_semantic_rewrite.py b/data_agent/test_nl2sql_semantic_rewrite.py
index dc1861da..f511b729 100644
--- a/data_agent/test_nl2sql_semantic_rewrite.py
+++ b/data_agent/test_nl2sql_semantic_rewrite.py
@@ -2416,6 +2416,67 @@ def test_semantic_rewrite_cq_hard_14_dwithin_uses_geography_and_floor_quote():
assert "semantic_column_alias" in corrections
+def test_semantic_rewrite_keeps_longest_bridge_cte_on_roads_table():
+ from data_agent.nl2sql_semantic_rewrite import apply_semantic_sql_rewrites
+
+ context = {
+ "candidate_tables": [
+ {
+ "table_name": "cq_amap_poi_2024",
+ "table_aliases": ["高德POI", "POI", "兴趣点"],
+ "schema_complete": True,
+ "columns": [
+ {"column_name": "ID", "quoted_ref": '"ID"', "needs_quoting": True},
+ {"column_name": "名称", "quoted_ref": '"名称"', "needs_quoting": True},
+ {"column_name": "geometry", "is_geometry": True},
+ ],
+ },
+ {
+ "table_name": "cq_osm_roads_2021",
+ "table_aliases": ["路网", "道路网 2021"],
+ "schema_complete": True,
+ "columns": [
+ {"column_name": "name"},
+ {"column_name": "fclass"},
+ {"column_name": "bridge"},
+ {"column_name": "geometry", "is_geometry": True},
+ ],
+ },
+ {
+ "table_name": "cq_osm_roads",
+ "table_aliases": ["路网", "道路数据"],
+ "schema_complete": True,
+ "columns": [
+ {"column_name": "name"},
+ {"column_name": "fclass"},
+ {"column_name": "bridge"},
+ {"column_name": "geometry", "is_geometry": True},
+ ],
+ },
+ ]
+ }
+ sql = (
+ "WITH longest_bridge AS ("
+ "SELECT geometry FROM cq_osm_roads_2021 WHERE bridge = 'T' "
+ "ORDER BY ST_Length(geometry::geography) DESC LIMIT 1"
+ ") "
+ 'SELECT COUNT(DISTINCT p."ID") FROM cq_amap_poi_2024 AS p, '
+ "longest_bridge AS lb WHERE "
+ "ST_DWithin(p.geometry::geography, lb.geometry::geography, 100)"
+ )
+
+ rewritten, corrections = apply_semantic_sql_rewrites(
+ "统计距离道路网络中最长桥梁100米范围内的高德POI数量。",
+ sql,
+ context,
+ )
+
+ assert "FROM cq_osm_roads_2021" in rewritten
+ assert "FROM cq_amap_poi_2024 WHERE bridge" not in rewritten
+ assert rewritten != "SELECT 1"
+ assert "semantic_unknown_column_refusal" not in corrections
+
+
def test_semantic_rewrite_cq_hard_25_grouped_road_length_uses_geography_km():
from data_agent.nl2sql_semantic_rewrite import apply_semantic_sql_rewrites
diff --git a/data_agent/test_paper9_agent_evaluation.py b/data_agent/test_paper9_agent_evaluation.py
new file mode 100644
index 00000000..6a3d0a77
--- /dev/null
+++ b/data_agent/test_paper9_agent_evaluation.py
@@ -0,0 +1,191 @@
+import pytest
+
+from data_agent.paper9_agent_evaluation import (
+ evaluate_paper9_tool_trajectory,
+ summarize_repeated_agent_runs,
+)
+
+
+def _opening():
+ return [
+ {
+ "tool": "world_model_v21_status",
+ "response": {"finals": {"version_compatible": True}},
+ },
+ {"tool": "paper9_inspect_resources", "response": {"planning_ready": True}},
+ {"tool": "paper9_recall_verified_episodes", "response": {"count": 0}},
+ ]
+
+
+def test_success_trajectory_passes_contract():
+ events = _opening() + [
+ {"tool": "world_model_v21_pipeline", "response": {"status": "ok"}},
+ {
+ "tool": "paper9_audit_run",
+ "response": {
+ "attempt": 0,
+ "hard_constraint_passed": True,
+ "retryable": False,
+ },
+ },
+ {"tool": "paper9_commit_verified_episode", "response": {"status": "committed"}},
+ ]
+
+ result = evaluate_paper9_tool_trajectory(events)
+
+ assert result["passed"] is True
+ assert result["terminal"] == "verified_commit"
+
+
+def test_single_replan_then_success_passes_contract():
+ events = _opening() + [
+ {"tool": "world_model_v21_pipeline", "response": {"status": "ok"}},
+ {
+ "tool": "paper9_audit_run",
+ "response": {
+ "attempt": 0,
+ "hard_constraint_passed": False,
+ "retryable": True,
+ },
+ },
+ {"tool": "world_model_v21_plan", "response": {"status": "ok"}},
+ {
+ "tool": "paper9_audit_run",
+ "response": {
+ "attempt": 1,
+ "hard_constraint_passed": True,
+ "retryable": False,
+ },
+ },
+ {"tool": "paper9_commit_verified_episode", "response": {"status": "committed"}},
+ ]
+
+ result = evaluate_paper9_tool_trajectory(events)
+
+ assert result["passed"] is True
+ assert result["tool_count"] == 8
+
+
+def test_second_failure_stops_without_commit():
+ events = _opening() + [
+ {"tool": "world_model_v21_pipeline", "response": {"status": "ok"}},
+ {
+ "tool": "paper9_audit_run",
+ "response": {
+ "attempt": 0,
+ "hard_constraint_passed": False,
+ "retryable": True,
+ },
+ },
+ {"tool": "world_model_v21_plan", "response": {"status": "ok"}},
+ {
+ "tool": "paper9_audit_run",
+ "response": {
+ "attempt": 1,
+ "hard_constraint_passed": False,
+ "retryable": False,
+ },
+ },
+ ]
+
+ result = evaluate_paper9_tool_trajectory(events)
+
+ assert result["passed"] is True
+ assert result["terminal"] == "human_review"
+
+
+def test_premature_commit_fails_contract():
+ events = _opening() + [
+ {"tool": "world_model_v21_pipeline", "response": {"status": "ok"}},
+ {"tool": "paper9_commit_verified_episode", "response": {"status": "committed"}},
+ ]
+
+ result = evaluate_paper9_tool_trajectory(events)
+
+ assert result["passed"] is False
+ assert any("followed by an audit" in item for item in result["violations"])
+ assert any("unverified" in item for item in result["violations"])
+
+
+def test_version_mismatch_stops_before_planning():
+ events = [
+ {
+ "tool": "world_model_v21_status",
+ "response": {"finals": {"version_compatible": False}},
+ },
+ {"tool": "paper9_inspect_resources", "response": {"planning_ready": False}},
+ ]
+
+ result = evaluate_paper9_tool_trajectory(events)
+
+ assert result["passed"] is True
+ assert result["terminal"] == "preflight_stop"
+
+
+def test_repeated_run_summary_reports_statistics_by_scenario():
+ runs = [
+ {
+ "scenario": "success",
+ "passed": True,
+ "terminal": "verified_commit",
+ "latency_ms": 100,
+ "tool_trace": ["status", "inspect", "plan", "audit", "commit"],
+ },
+ {
+ "scenario": "success",
+ "passed": False,
+ "terminal": "incomplete",
+ "latency_ms": 300,
+ "tool_trace": ["status", "inspect"],
+ "error": "missing plan",
+ },
+ {
+ "scenario": "blocked",
+ "passed": True,
+ "terminal": "preflight_stop",
+ "latency_ms": 50,
+ "tool_trace": ["status", "inspect"],
+ },
+ ]
+
+ report = summarize_repeated_agent_runs(runs, pass_rate_threshold=0.8)
+
+ assert report["overall"]["pass_rate"] == pytest.approx(2 / 3)
+ assert report["by_scenario"]["success"]["mean_latency_ms"] == 200
+ assert report["by_scenario"]["success"]["p95_latency_ms"] == pytest.approx(290)
+ assert report["by_scenario"]["blocked"]["exact_trace_consistency"] == 1
+ assert report["release_gate"] == {
+ "pass_rate_threshold": 0.8,
+ "passed": False,
+ "failed_scenarios": ["success"],
+ }
+
+
+def test_repeated_run_summary_uses_wilson_interval_for_small_samples():
+ report = summarize_repeated_agent_runs(
+ [
+ {
+ "scenario": "success",
+ "passed": True,
+ "tool_trace": ["status"],
+ }
+ for _ in range(10)
+ ]
+ )
+
+ lower, upper = report["overall"]["confidence_95_wilson"]
+ assert lower == pytest.approx(0.72246, rel=1e-4)
+ assert upper == pytest.approx(1.0)
+ assert report["release_gate"]["passed"] is True
+
+
+def test_trace_consistency_is_order_sensitive():
+ report = summarize_repeated_agent_runs(
+ [
+ {"scenario": "x", "passed": True, "tool_trace": ["a", "b"]},
+ {"scenario": "x", "passed": True, "tool_trace": ["b", "a"]},
+ ]
+ )
+
+ assert report["overall"]["exact_trace_consistency"] == 0.5
+ assert report["overall"]["pairwise_trace_consistency"] == 0.0
diff --git a/data_agent/test_paper9_agent_governance.py b/data_agent/test_paper9_agent_governance.py
new file mode 100644
index 00000000..279af557
--- /dev/null
+++ b/data_agent/test_paper9_agent_governance.py
@@ -0,0 +1,121 @@
+import json
+
+import pytest
+
+from data_agent.paper9_agent_governance import (
+ Paper9AuditPolicy,
+ Paper9EpisodeStore,
+ audit_paper9_run,
+ evaluate_paper9_summary,
+)
+
+
+def _summary(*, cultivated=4.3, slope=-0.8, contiguity=0.02):
+ return {
+ "results": [
+ {
+ "episode": 0,
+ "cultivated_area_change_ha": cultivated,
+ "slope_change_pct": slope,
+ "cont_change": contiguity,
+ "baimu_area_change_ha": 34.6,
+ "total_reward": 12.5,
+ "steps_run": 50,
+ }
+ ]
+ }
+
+
+def test_hard_gate_passes_and_routes_to_verified_commit():
+ result = evaluate_paper9_summary(_summary())
+
+ assert result["hard_constraint_passed"] is True
+ assert result["next_action"] == "commit_verified_episode"
+ assert result["retryable"] is False
+
+
+def test_first_hard_gate_failure_allows_one_bounded_replan():
+ result = evaluate_paper9_summary(_summary(cultivated=-1.0), attempt=0)
+
+ assert result["hard_constraint_passed"] is False
+ assert result["next_action"] == "replan_once"
+ assert result["retryable"] is True
+ assert "below the required" in result["failure_reasons"][0]
+
+
+def test_second_hard_gate_failure_stops_for_human_review():
+ policy = Paper9AuditPolicy(max_replans=1)
+ result = evaluate_paper9_summary(
+ _summary(slope=0.1), policy=policy, attempt=1
+ )
+
+ assert result["hard_constraint_passed"] is False
+ assert result["next_action"] == "stop_and_request_human_review"
+ assert result["retryable"] is False
+
+
+def test_run_audit_requires_summary_and_spatial_result(tmp_path):
+ (tmp_path / "mpc_summary.json").write_text(
+ json.dumps(_summary()), encoding="utf-8"
+ )
+ (tmp_path / "optimized_dltb.fgb").write_bytes(b"fgb")
+
+ result = audit_paper9_run(tmp_path)
+
+ assert result["hard_constraint_passed"] is True
+ assert result["all_expected_outputs_exist"] is True
+ assert result["artifacts"]["summary"]["sha256"]
+ assert (tmp_path / "paper9_agent_audit.json").is_file()
+
+
+def test_run_audit_fails_closed_when_spatial_result_is_missing(tmp_path):
+ (tmp_path / "mpc_summary.json").write_text(
+ json.dumps(_summary()), encoding="utf-8"
+ )
+
+ result = audit_paper9_run(tmp_path, write=False)
+
+ assert result["hard_constraint_passed"] is False
+ assert result["next_action"] == "stop_and_request_human_review"
+ assert "No optimized spatial result" in result["failure_reasons"][-1]
+
+
+def test_verified_episode_store_rejects_failed_runs(tmp_path):
+ store = Paper9EpisodeStore(tmp_path / "episodes.jsonl")
+ failed = evaluate_paper9_summary(_summary(slope=0.0))
+ failed["all_expected_outputs_exist"] = True
+
+ with pytest.raises(ValueError, match="hard-gate-passed"):
+ store.commit(audit=failed, dataset="bishan", goal="optimize")
+
+
+def test_verified_episode_store_is_idempotent_and_recallable(tmp_path):
+ run_dir = tmp_path / "run"
+ run_dir.mkdir()
+ (run_dir / "mpc_summary.json").write_text(
+ json.dumps(_summary()), encoding="utf-8"
+ )
+ (run_dir / "optimized_dltb.fgb").write_bytes(b"fgb")
+ audit = audit_paper9_run(run_dir)
+ store = Paper9EpisodeStore(tmp_path / "episodes.jsonl")
+
+ first = store.commit(
+ audit=audit,
+ dataset="bishan",
+ goal="improve layout",
+ plan_args={"horizon": 1, "top_k": 1},
+ provenance={"algorithm_version": "2.2.3"},
+ )
+ second = store.commit(
+ audit=audit,
+ dataset="bishan",
+ goal="improve layout",
+ plan_args={"horizon": 1, "top_k": 1},
+ provenance={"algorithm_version": "2.2.3"},
+ )
+
+ assert first["already_existed"] is False
+ assert second["already_existed"] is True
+ recalled = store.recall(dataset="bishan", limit=3)
+ assert [item["episode_id"] for item in recalled] == [first["episode_id"]]
+ assert recalled[0]["audit"]["hard_constraint_passed"] is True
diff --git a/data_agent/test_tool_filter.py b/data_agent/test_tool_filter.py
index 844bca33..3d9d9f7d 100644
--- a/data_agent/test_tool_filter.py
+++ b/data_agent/test_tool_filter.py
@@ -143,9 +143,10 @@ def test_parses_tools_field(self, mock_client):
mock_client.models.generate_content.return_value = mock_resp
from data_agent.app import classify_intent
- intent, reason, tokens, cats, lang = classify_intent("创建缓冲区")
+ intent, reason, tokens, cats, lang, execution_mode = classify_intent("创建缓冲区")
self.assertEqual(intent, "GENERAL")
self.assertEqual(cats, {"spatial_processing"})
+ self.assertEqual(execution_mode, "agentic")
@patch("data_agent.intent_router._router_client")
def test_multiple_tools(self, mock_client):
@@ -157,9 +158,12 @@ def test_multiple_tools(self, mock_client):
mock_client.models.generate_content.return_value = mock_resp
from data_agent.app import classify_intent
- intent, reason, tokens, cats, lang = classify_intent("搜索POI并生成热力图")
+ intent, reason, tokens, cats, lang, execution_mode = classify_intent(
+ "搜索POI并生成热力图"
+ )
self.assertEqual(intent, "GENERAL")
self.assertEqual(cats, {"poi_location", "spatial_processing"})
+ self.assertEqual(execution_mode, "agentic")
@patch("data_agent.intent_router._router_client")
def test_tools_all_returns_empty(self, mock_client):
@@ -171,9 +175,10 @@ def test_tools_all_returns_empty(self, mock_client):
mock_client.models.generate_content.return_value = mock_resp
from data_agent.app import classify_intent
- intent, reason, tokens, cats, lang = classify_intent("你好")
+ intent, reason, tokens, cats, lang, execution_mode = classify_intent("你好")
self.assertEqual(intent, "AMBIGUOUS")
self.assertEqual(cats, set())
+ self.assertEqual(execution_mode, "agentic")
@patch("data_agent.intent_router._router_client")
def test_no_tools_field_returns_empty(self, mock_client):
@@ -185,19 +190,21 @@ def test_no_tools_field_returns_empty(self, mock_client):
mock_client.models.generate_content.return_value = mock_resp
from data_agent.app import classify_intent
- intent, reason, tokens, cats, lang = classify_intent("查看地图")
+ intent, reason, tokens, cats, lang, execution_mode = classify_intent("查看地图")
self.assertEqual(intent, "GENERAL")
self.assertEqual(cats, set())
+ self.assertEqual(execution_mode, "agentic")
@patch("data_agent.intent_router._router_client")
def test_error_returns_empty_cats(self, mock_client):
- """API error returns 4-tuple with empty set."""
+ """API error returns the six-field fallback with empty categories."""
mock_client.models.generate_content.side_effect = Exception("API down")
from data_agent.app import classify_intent
- intent, reason, tokens, cats, lang = classify_intent("test")
+ intent, reason, tokens, cats, lang, execution_mode = classify_intent("test")
self.assertEqual(intent, "GENERAL")
self.assertEqual(cats, set())
+ self.assertEqual(execution_mode, "agentic")
@patch("data_agent.intent_router._router_client")
def test_governance_with_tools(self, mock_client):
@@ -209,9 +216,10 @@ def test_governance_with_tools(self, mock_client):
mock_client.models.generate_content.return_value = mock_resp
from data_agent.app import classify_intent
- intent, reason, tokens, cats, lang = classify_intent("检查拓扑")
+ intent, reason, tokens, cats, lang, execution_mode = classify_intent("检查拓扑")
self.assertEqual(intent, "GOVERNANCE")
self.assertEqual(cats, {"quality_audit"})
+ self.assertEqual(execution_mode, "agentic")
if __name__ == "__main__":
diff --git a/data_agent/test_world_model_v21.py b/data_agent/test_world_model_v21.py
index 593adb02..c2022419 100644
--- a/data_agent/test_world_model_v21.py
+++ b/data_agent/test_world_model_v21.py
@@ -138,6 +138,68 @@ def fake_run(**kwargs):
assert result["summary"]["steps_run"] == 50
+def test_county_plan_uses_output_codes_matching_detected_input_scheme(
+ tmp_path, monkeypatch
+):
+ prepared = tmp_path / "prepared"
+ input_dir = prepared / "dem_slope_analysis" / "output"
+ input_dir.mkdir(parents=True)
+ (input_dir / "DLTB_with_slope.shp").write_bytes(b"shp")
+ ensemble = tmp_path / "ensemble"
+ ensemble.mkdir()
+ (ensemble / "ensemble_member0.onnx").write_bytes(b"onnx")
+ calls = {}
+
+ def fake_run(**kwargs):
+ calls.update(kwargs)
+ out_dir = Path(kwargs["out_dir"])
+ summary = {
+ "config": {"n_blocks": 1, "n_parcels": 2, "max_steps": 1},
+ "results": [
+ {
+ "episode": 0,
+ "cultivated_area_change_ha": 0.0,
+ "slope_change_pct": -0.1,
+ "cont_change": 0.1,
+ "steps_run": 1,
+ }
+ ],
+ }
+ (out_dir / "mpc_summary.json").write_text(
+ json.dumps(summary), encoding="utf-8"
+ )
+ Path(kwargs["output_fc"]).write_bytes(b"shp")
+ return summary
+
+ svc = WorldModelV21Service(repo_path=tmp_path)
+ monkeypatch.setattr(svc, "_load_paper9_plan_run", lambda: fake_run)
+ monkeypatch.setattr(
+ svc,
+ "_detect_land_use_code_contract",
+ lambda path: {
+ "compatible": True,
+ "scheme": "legacy_three_digit_test_data",
+ "farm_dlbm": "011",
+ "forest_dlbm": "031",
+ "code_counts": {"011": 1, "031": 1},
+ },
+ )
+ monkeypatch.setattr(svc, "_convert_optimized_shp_to_fgb", lambda *args: None)
+
+ result = svc.run_plan(
+ {
+ "prepared_dir": str(prepared),
+ "ensemble_dir": str(ensemble),
+ "env_kind": "county",
+ },
+ user_id="pytest",
+ )
+
+ assert calls["farm_dlbm"] == "011"
+ assert calls["forest_dlbm"] == "031"
+ assert result["land_use_code_contract"]["compatible"] is True
+
+
def test_run_prepare_calls_paper9_prepare_and_returns_artifacts(tmp_path, monkeypatch):
dltb = tmp_path / "DLTB.shp"
dem = tmp_path / "dem.tif"
diff --git a/data_agent/test_world_model_v21_presentation.py b/data_agent/test_world_model_v21_presentation.py
index 8500bd41..46c0780d 100644
--- a/data_agent/test_world_model_v21_presentation.py
+++ b/data_agent/test_world_model_v21_presentation.py
@@ -30,6 +30,14 @@ def test_parse_world_model_v21_tool_response_keeps_pipeline_wrapper():
assert parse_world_model_v21_tool_response({"result": json.dumps(payload)}) == payload
+def test_parse_structured_tool_response_unwraps_adk_result():
+ from data_agent.world_model_v21_presentation import parse_structured_tool_response
+
+ payload = {"hard_constraint_passed": True, "next_action": "commit_verified_episode"}
+
+ assert parse_structured_tool_response({"result": json.dumps(payload)}) == payload
+
+
def test_format_world_model_v21_result_uses_tool_n_selected_not_top_k():
from data_agent.world_model_v21_presentation import format_world_model_v21_result_for_chat
@@ -60,9 +68,9 @@ def test_format_world_model_v21_result_uses_tool_n_selected_not_top_k():
)
lines = set(text.splitlines())
- assert "- Top K: 5" in text
- assert "- N Selected: 50" in lines
- assert "- N Selected: 5" not in lines
+ assert "- 每步候选行动数：5" in lines
+ assert "- 选中决策单元：50" in lines
+ assert "- 选中决策单元：5" not in lines
assert "restoration_mpc_units.geojson" in text
@@ -101,13 +109,12 @@ def test_format_world_model_v21_county_result_uses_swaps_and_land_use_legend():
)
lines = set(text.splitlines())
- assert "- Swaps Completed: 427" in lines
- assert not any(line.startswith("- N Selected:") for line in lines)
- assert "- Slope Change: -1.7531%" in lines
- assert "CHG_FLAG" in text
- assert "灰色代表保持不变" in text
- assert "红色代表耕地 -> 林地" in text
- assert "绿色代表林地 -> 耕地" in text
+ assert "- 完成双向置换：427 对" in lines
+ assert not any(line.startswith("- 选中决策单元：") for line in lines)
+ assert "- 平均坡度变化：-1.7531%" in lines
+ assert "灰色为保持不变" in text
+ assert "红色为耕地 → 林地" in text
+ assert "绿色为林地 → 耕地" in text
def test_format_world_model_v21_pipeline_result_shows_abcd_steps():
@@ -152,19 +159,141 @@ def test_format_world_model_v21_pipeline_result_shows_abcd_steps():
tool_args={"horizon": "1", "top_k": "1"},
)
- assert "- Mode: pipeline_a_to_d; plan=tool4_mpc" in text
- assert "- Prepared Dir: /app/dongxing-runs/prepared" in text
- assert "- Ensemble Dir: /app/dongxing-runs/prepared/ensemble_seed0" in text
- assert "- Horizon: 1（MPC 前瞻步长）" in text
- assert "- Top K: 1（每步候选动作数）" in text
- assert "- N Parcels: 53004" in text
- assert "- A / Tool 1 Prepare: skipped_reused" in text
- assert "- B / Tool 2 Sample: skipped_reused" in text
- assert "- C / Tool 3 Train: skipped_reused" in text
- assert "- D / Tool 4 Plan: ok" in text
- assert "world_model_v21_status -> world_model_v21_pipeline" in text
+ assert "- MPC 前瞻步长：1" in text
+ assert "- 每步候选行动数：1" in text
+ assert "- 图斑数：53004" in text
+ assert "- A / 数据准备：已复用（未重复执行）" in text
+ assert "- B / 样本生成：已复用（未重复执行）" in text
+ assert "- C / 状态转移模型训练：已复用（未重复执行）" in text
+ assert "- D / MPC 规划执行：完成" in text
+ assert text.startswith("### 规划结果尚未完成审计")
+ assert "未捕获完整函数调用轨迹，请查看运行日志。" in text
+ assert "/app/dongxing-runs" not in text
progress = format_world_model_v21_progress_for_chat(result, "@WorldModelV21 (直接调用)")
- assert "**@WorldModelV21 (直接调用)** A/B/C/D 4 阶段完成" in progress
- assert "✓ A / Tool 1 Prepare: skipped_reused" in progress
- assert "✓ D / Tool 4 Plan: ok" in progress
+ assert "**县域耕地规划 Agent** · Gemma 4 + Google ADK 受控自主闭环" in progress
+ assert "✓ A / 数据准备：已复用（未重复执行）" in progress
+ assert "✓ D / MPC 规划执行：完成" in progress
+
+
+def test_format_world_model_v21_result_shows_verified_agent_loop():
+ from data_agent.world_model_v21_presentation import (
+ format_world_model_v21_progress_for_chat,
+ format_world_model_v21_result_for_chat,
+ )
+
+ result = {
+ "status": "ok",
+ "version": "2.1.0",
+ "mode": "tool4_mpc",
+ "env_kind": "county",
+ "summary": {
+ "cultivated_area_change_ha": 0.2116,
+ "slope_change_pct": -0.8154,
+ "cont_change": 0.0284,
+ },
+ "artifacts": {},
+ }
+ status = {
+ "paper9": {"package_version": "0.3.3", "algorithm_version": "2.2.3"},
+ "finals": {"version_compatible": True},
+ }
+ audit = {
+ "hard_constraint_passed": True,
+ "next_action": "commit_verified_episode",
+ "audit_path": "/run/paper9_agent_audit.json",
+ }
+ commit = {
+ "status": "committed",
+ "episode": {"episode_id": "episode-1"},
+ }
+ trace = [
+ {"tool_name": "world_model_v21_status", "duration_s": 0.004},
+ {"tool_name": "paper9_inspect_resources", "duration_s": 0.2},
+ {"tool_name": "paper9_recall_verified_episodes", "duration_s": 0.1},
+ {"tool_name": "world_model_v21_plan", "duration_s": 88.6},
+ {"tool_name": "paper9_audit_run", "duration_s": 1.3},
+ {"tool_name": "paper9_commit_verified_episode", "duration_s": 0.1},
+ ]
+
+ text = format_world_model_v21_result_for_chat(
+ result,
+ status_result=status,
+ audit_result=audit,
+ commit_result=commit,
+ tool_trace=trace,
+ total_duration_s=96.8,
+ )
+ progress = format_world_model_v21_progress_for_chat(
+ result,
+ audit_result=audit,
+ commit_result=commit,
+ tool_trace=trace,
+ total_duration_s=96.8,
+ )
+
+ assert text.startswith("### 县域耕地受控规划完成")
+ assert "**Gemma 4 + Google ADK** · 6 次原生函数调用" in text
+ assert "总用时 96.8 秒" in text
+ assert "MPC 规划 88.6 秒" in text
+ assert "审计与经验提交 1.4 秒" in text
+ assert "`world_model_v21_status` · 0.004 秒" in text
+ assert "版本：适配器 2.1.0 · 引擎包 0.3.3 · 算法 2.2.3" in text
+ assert "- 耕地面积变化：0.2116 ha" in text
+ assert "- 硬约束校验：通过" in text
+ assert "- 经验编号：episode-1" in text
+ assert "**观察版本与运行状态** → 版本兼容" in text
+ assert "**根据审计结果决定是否写入经验** → 已写入已验证经验库" in text
+ assert "Paper9 Package:" not in text
+ assert "Prepared Dir:" not in text
+ assert "原生函数调用：6 次" in progress
+ assert "总用时：96.8 秒" in progress
+ assert "硬约束审计：通过" in progress
+ assert "已验证经验库：已写入" in progress
+
+
+def test_format_world_model_v21_result_fails_closed_after_bad_audit():
+ from data_agent.world_model_v21_presentation import format_world_model_v21_result_for_chat
+
+ text = format_world_model_v21_result_for_chat(
+ {
+ "status": "ok",
+ "version": "2.1.0",
+ "mode": "tool4_mpc",
+ "env_kind": "county",
+ "summary": {},
+ "artifacts": {},
+ },
+ audit_result={
+ "hard_constraint_passed": False,
+ "next_action": "stop_and_request_human_review",
+ "failure_reasons": ["cultivated area below floor"],
+ },
+ )
+
+ assert text.startswith("### 县域耕地规划已停止")
+ assert "- 硬约束校验：未通过" in text
+ assert "cultivated area below floor" in text
+
+
+def test_format_world_model_v21_result_does_not_claim_completion_before_commit():
+ from data_agent.world_model_v21_presentation import format_world_model_v21_result_for_chat
+
+ text = format_world_model_v21_result_for_chat(
+ {
+ "status": "ok",
+ "version": "2.1.0",
+ "mode": "tool4_mpc",
+ "env_kind": "county",
+ "summary": {},
+ "artifacts": {},
+ },
+ audit_result={
+ "hard_constraint_passed": True,
+ "next_action": "commit_verified_episode",
+ },
+ tool_trace=["world_model_v21_status", "paper9_audit_run"],
+ )
+
+ assert text.startswith("### 规划已通过审计，尚未写入经验")
+ assert "- 已验证经验库：未写入" in text
diff --git a/data_agent/test_world_model_v21_report.py b/data_agent/test_world_model_v21_report.py
new file mode 100644
index 00000000..9719e67e
--- /dev/null
+++ b/data_agent/test_world_model_v21_report.py
@@ -0,0 +1,120 @@
+import zipfile
+from pathlib import Path
+
+import geopandas as gpd
+from docx import Document
+from shapely.geometry import box
+
+from data_agent.world_model_v21_report import generate_world_model_v21_word_report
+
+
+def _report_context(run_dir: Path) -> dict:
+ return {
+ "result": {
+ "mode": "pipeline_a_to_d",
+ "version": "2.1.0",
+ "steps": [
+ {"step": "prepare", "status": "skipped_reused"},
+ {"step": "sample", "status": "skipped_reused"},
+ {"step": "train", "status": "skipped_reused"},
+ {"step": "plan", "status": "ok"},
+ ],
+ "plan_result": {
+ "status": "ok",
+ "version": "2.1.0",
+ "env_kind": "county",
+ "out_dir": str(run_dir),
+ "summary": {
+ "steps_run": 100,
+ "swaps_completed": 2,
+ "n_blocks": 3,
+ "n_parcels": 3,
+ "cultivated_area_change_ha": 1.25,
+ "slope_change_pct": -0.61,
+ "cont_change": 0.02,
+ "baimu_area_change_ha": 4.5,
+ "total_reward": 9.8,
+ },
+ "artifacts": {
+ "summary_json": "mpc_summary.json",
+ "map_layer": "optimized_dltb.shp",
+ },
+ },
+ },
+ "tool_args": {"dataset": "bishan", "horizon": "1", "top_k": "1"},
+ "status_result": {"paper9": {"package_version": "0.3.3", "algorithm_version": "2.2.3"}},
+ "audit_result": {
+ "hard_constraint_passed": True,
+ "all_expected_outputs_exist": True,
+ "out_dir": str(run_dir),
+ "policy": {"cultivated_area_floor_delta_ha": 0.0},
+ "artifacts": {"summary": {"sha256": "a" * 64}},
+ },
+ "commit_result": {
+ "status": "committed",
+ "episode": {"episode_id": "episode-verified-001"},
+ },
+ "tool_trace": [
+ {"tool_name": "world_model_v21_status", "duration_s": 0.01},
+ {"tool_name": "paper9_inspect_resources", "duration_s": 0.02},
+ {"tool_name": "paper9_recall_verified_episodes", "duration_s": 0.03},
+ {"tool_name": "world_model_v21_pipeline", "duration_s": 12.3},
+ {"tool_name": "paper9_audit_run", "duration_s": 0.12},
+ {"tool_name": "paper9_commit_verified_episode", "duration_s": 0.04},
+ ],
+ "total_duration_s": 13.1,
+ }
+
+
+def test_world_model_v21_report_embeds_real_map_and_visual_evidence(tmp_path):
+ run_dir = tmp_path / "run"
+ run_dir.mkdir()
+ gdf = gpd.GeoDataFrame(
+ {"CHG_FLAG": [0, 1, 2]},
+ geometry=[box(0, 0, 1, 1), box(1, 0, 2, 1), box(0, 1, 1, 2)],
+ crs="EPSG:4326",
+ )
+ gdf.to_file(run_dir / "optimized_dltb.shp")
+ (run_dir / "mpc_summary.json").write_text(
+ """{
+ "config": {"horizon": 1, "top_k": 1, "n_blocks": 3, "n_parcels": 3},
+ "results": [{
+ "steps_run": 100,
+ "swaps_completed": 2,
+ "cultivated_area_change_ha": 1.25,
+ "slope_change_pct": -0.61,
+ "cont_change": 0.02,
+ "baimu_area_change_ha": 4.5,
+ "total_reward": 9.8
+ }],
+ "shapefile_output": {
+ "n_input": 3,
+ "n_in_env": 3,
+ "n_farm_to_forest": 1,
+ "n_forest_to_farm": 1,
+ "n_unchanged": 1
+ }
+ }""",
+ encoding="utf-8",
+ )
+
+ output = tmp_path / "county_report.docx"
+ report_path = generate_world_model_v21_word_report(
+ _report_context(run_dir), str(output), author="Finals Tester"
+ )
+
+ document = Document(report_path)
+ visible_text = "\n".join(
+ [paragraph.text for paragraph in document.paragraphs]
+ + [cell.text for table in document.tables for row in table.rows for cell in row.cells]
+ )
+ assert "县域耕地空间规划与硬约束审计报告" in visible_text
+ assert "Gemma 4 + Google ADK 受控自主闭环" in visible_text
+ assert "硬约束审计：通过" in visible_text
+ assert "已验证经验库" in visible_text
+ assert "episode-verified-001" in visible_text
+ assert "/tmp/" not in visible_text
+
+ with zipfile.ZipFile(report_path) as archive:
+ media = [name for name in archive.namelist() if name.startswith("word/media/")]
+ assert len(media) >= 3
diff --git a/data_agent/test_world_model_v21_tools.py b/data_agent/test_world_model_v21_tools.py
index ae3cb833..38721916 100644
--- a/data_agent/test_world_model_v21_tools.py
+++ b/data_agent/test_world_model_v21_tools.py
@@ -14,6 +14,39 @@ def status(self):
"paper9": {"repo_path": "paper9", "importable": True},
}
+ def inspect_resources(self, *, dataset, prepared_dir, ensemble_dir):
+ self.payload = {
+ "dataset": dataset,
+ "prepared_dir": prepared_dir,
+ "ensemble_dir": ensemble_dir,
+ }
+ return {"status": "ready", "planning_ready": True, **self.payload}
+
+ def audit_run(self, *, out_dir, attempt, cultivated_area_floor_delta_ha):
+ self.payload = {
+ "out_dir": out_dir,
+ "attempt": attempt,
+ "cultivated_area_floor_delta_ha": cultivated_area_floor_delta_ha,
+ }
+ return {
+ "hard_constraint_passed": True,
+ "next_action": "commit_verified_episode",
+ **self.payload,
+ }
+
+ def recall_verified_episodes(self, *, dataset, limit):
+ self.payload = {"dataset": dataset, "limit": limit}
+ return {"status": "ok", "count": 0, "episodes": []}
+
+ def commit_verified_episode(self, *, out_dir, dataset, goal, plan_args):
+ self.payload = {
+ "out_dir": out_dir,
+ "dataset": dataset,
+ "goal": goal,
+ "plan_args": plan_args,
+ }
+ return {"status": "committed", "episode": {"episode_id": "demo"}}
+
def run_plan(self, payload, user_id):
self.payload = payload
self.user_id = user_id
@@ -84,6 +117,37 @@ def test_world_model_v21_status_tool_returns_service_status(monkeypatch):
assert result["paper9"]["repo_path"] == "paper9"
+def test_paper9_governance_tools_call_service_with_structured_values(monkeypatch):
+ from data_agent.toolsets import world_model_v21_tools as tools
+
+ fake = FakeWorldModelV21Service()
+ monkeypatch.setattr(tools, "get_world_model_v21_service", lambda: fake)
+
+ inspected = json.loads(tools.paper9_inspect_resources(dataset="东兴"))
+ assert inspected["planning_ready"] is True
+ assert fake.payload["dataset"] == "dongxing"
+ assert fake.payload["prepared_dir"] == "/app/dongxing-runs/prepared"
+
+ recalled = json.loads(tools.paper9_recall_verified_episodes("东兴", "5"))
+ assert recalled["count"] == 0
+ assert fake.payload == {"dataset": "dongxing", "limit": 5}
+
+ audited = json.loads(tools.paper9_audit_run("/tmp/run", "1", "0"))
+ assert audited["hard_constraint_passed"] is True
+ assert fake.payload["attempt"] == 1
+
+ committed = json.loads(
+ tools.paper9_commit_verified_episode(
+ "/tmp/run",
+ "东兴",
+ "优化耕地布局",
+ '{"horizon": 1, "top_k": 1}',
+ )
+ )
+ assert committed["status"] == "committed"
+ assert fake.payload["plan_args"] == {"horizon": 1, "top_k": 1}
+
+
def test_world_model_v21_plan_tool_normalizes_payload_and_returns_map_update(monkeypatch):
from data_agent.toolsets import world_model_v21_tools as tools
from data_agent.user_context import current_user_id
@@ -191,6 +255,46 @@ def test_world_model_v21_pipeline_uses_demo_defaults_and_env_paths(monkeypatch):
assert fake.payload["horizon"] == 1
assert fake.payload["top_k"] == 1
assert fake.payload["continuation"] == "greedy"
+ assert fake.payload["cultivated_area_floor_delta_ha"] == 0.0
+ assert fake.payload["baimu_area_floor_delta_ha"] is None
+
+
+def test_world_model_v21_pipeline_forwards_planning_constraints(monkeypatch):
+ from data_agent.toolsets import world_model_v21_tools as tools
+
+ fake = FakeWorldModelV21Service()
+ monkeypatch.setattr(tools, "get_world_model_v21_service", lambda: fake)
+
+ result = json.loads(
+ tools._world_model_v21_pipeline_sync(
+ cultivated_area_floor_delta_ha="1.25",
+ baimu_area_floor_delta_ha="2.5",
+ gamma_conn="3.5",
+ delta_conn="4.5",
+ )
+ )
+
+ assert result["status"] == "ok"
+ assert fake.payload["cultivated_area_floor_delta_ha"] == 1.25
+ assert fake.payload["baimu_area_floor_delta_ha"] == 2.5
+ assert fake.payload["gamma_conn"] == 3.5
+ assert fake.payload["delta_conn"] == 4.5
+
+
+def test_world_model_v21_pipeline_cannot_lower_cultivated_area_floor(monkeypatch):
+ from data_agent.toolsets import world_model_v21_tools as tools
+
+ fake = FakeWorldModelV21Service()
+ monkeypatch.setattr(tools, "get_world_model_v21_service", lambda: fake)
+
+ result = json.loads(
+ tools._world_model_v21_pipeline_sync(
+ cultivated_area_floor_delta_ha="-100",
+ )
+ )
+
+ assert result["status"] == "ok"
+ assert fake.payload["cultivated_area_floor_delta_ha"] == 0.0
def test_world_model_v21_pipeline_dataset_dongxing_overrides_bishan_env(monkeypatch):
@@ -289,6 +393,11 @@ def test_world_model_v21_toolset_lists_all_v21_tools():
assert "world_model_v21_train" in names
assert "world_model_v21_plan" in names
assert "world_model_v21_pipeline" in names
+ assert "paper9_inspect_resources" in names
+ assert "paper9_recall_verified_episodes" in names
+ assert "paper9_audit_run" in names
+ assert "paper9_commit_verified_episode" in names
+ assert len(names) == 10
def test_world_model_category_includes_v21_tools():
@@ -300,6 +409,10 @@ def test_world_model_category_includes_v21_tools():
assert "world_model_v21_train" in TOOL_CATEGORIES["world_model"]
assert "world_model_v21_plan" in TOOL_CATEGORIES["world_model"]
assert "world_model_v21_pipeline" in TOOL_CATEGORIES["world_model"]
+ assert "paper9_inspect_resources" in TOOL_CATEGORIES["world_model"]
+ assert "paper9_recall_verified_episodes" in TOOL_CATEGORIES["world_model"]
+ assert "paper9_audit_run" in TOOL_CATEGORIES["world_model"]
+ assert "paper9_commit_verified_episode" in TOOL_CATEGORIES["world_model"]
def test_world_model_category_includes_twm_tools():
@@ -354,6 +467,12 @@ def test_world_model_v21_agent_instruction_uses_fast_defaults():
assert "dataset='bishan'" in instruction
assert "CHG_FLAG" in instruction
assert "红色为耕地 -> 林地" in instruction
+ assert "paper9_inspect_resources" in instruction
+ assert "paper9_audit_run" in instruction
+ assert "paper9_commit_verified_episode" in instruction
+ assert "只允许再调用一次" in instruction
+ assert "算法版本 2.2.3" in instruction
+ assert "首次 pipeline 必须设置 cultivated_area_floor_delta_ha='0'" in instruction
def test_territory_world_model_agent_is_directly_mentionable():
diff --git a/data_agent/tool_filter.py b/data_agent/tool_filter.py
index c57ef586..1430bd3c 100644
--- a/data_agent/tool_filter.py
+++ b/data_agent/tool_filter.py
@@ -111,6 +111,8 @@
"world_model_v21_status", "world_model_v21_prepare",
"world_model_v21_sample", "world_model_v21_train",
"world_model_v21_plan", "world_model_v21_pipeline",
+ "paper9_inspect_resources", "paper9_recall_verified_episodes",
+ "paper9_audit_run", "paper9_commit_verified_episode",
"twm_status", "twm_status_detail", "twm_create_project",
"twm_list_projects", "twm_bind_layer", "twm_list_layer_bindings",
"twm_build_state", "twm_build_state_async", "twm_evaluate_rules",
diff --git a/data_agent/toolsets/world_model_v21_tools.py b/data_agent/toolsets/world_model_v21_tools.py
index 171a823e..1046f0ca 100644
--- a/data_agent/toolsets/world_model_v21_tools.py
+++ b/data_agent/toolsets/world_model_v21_tools.py
@@ -97,6 +97,123 @@ def world_model_v21_status() -> str:
return _json({"error": str(exc)})
+def paper9_inspect_resources(
+ dataset: str = "",
+ prepared_dir: str = "",
+ ensemble_dir: str = "",
+) -> str:
+ """Inspect Paper9 version binding and reusable stages before execution.
+
+ Use after world_model_v21_status and before planning. Set dataset to
+ "bishan" or "dongxing" to resolve the corresponding demo paths. This tool
+ never runs training or planning; it returns stages that can be safely
+ reused and any version or artifact gaps that must be repaired first.
+ """
+
+ dataset_key = _dataset_key(dataset)
+ resolved_prepared = _dataset_path(
+ prepared_dir,
+ "PAPER9_FARMLAND_MPC_DEFAULT_PREPARED_DIR",
+ dataset_key,
+ "prepared_dir",
+ )
+ resolved_ensemble = _dataset_path(
+ ensemble_dir,
+ "PAPER9_FARMLAND_MPC_DEFAULT_ENSEMBLE_DIR",
+ dataset_key,
+ "ensemble_dir",
+ )
+ try:
+ return _json(
+ get_world_model_v21_service().inspect_resources(
+ dataset=dataset_key,
+ prepared_dir=resolved_prepared,
+ ensemble_dir=resolved_ensemble,
+ )
+ )
+ except Exception as exc:
+ return _json(
+ {
+ "error": str(exc),
+ "dataset": dataset_key or None,
+ "prepared_dir": resolved_prepared,
+ "ensemble_dir": resolved_ensemble,
+ }
+ )
+
+
+def paper9_audit_run(
+ out_dir: str,
+ attempt: str = "0",
+ cultivated_area_floor_delta_ha: str = "0",
+) -> str:
+ """Audit a Paper9 run with deterministic hard gates after every plan.
+
+ Pass the out_dir returned by world_model_v21_plan or pipeline. The result
+ explicitly selects one branch: commit_verified_episode, replan_once, or
+ stop_and_request_human_review. Never commit or present a run before this
+ tool reports hard_constraint_passed=true.
+ """
+
+ try:
+ result = get_world_model_v21_service().audit_run(
+ out_dir=str(out_dir or "").strip(),
+ attempt=_int_arg(attempt, 0),
+ cultivated_area_floor_delta_ha=(
+ _optional_float_arg(cultivated_area_floor_delta_ha) or 0.0
+ ),
+ )
+ return _json(result)
+ except Exception as exc:
+ return _json({"error": str(exc), "out_dir": out_dir})
+
+
+def paper9_recall_verified_episodes(dataset: str = "", limit: str = "3") -> str:
+ """Recall prior hard-gate-passed Paper9 episodes for the named dataset.
+
+ Use before planning when prior experience may help choose supported search
+ parameters. Returned records are verified evidence, not unreviewed chat
+ history. An empty result is valid and should not block a new plan.
+ """
+
+ try:
+ result = get_world_model_v21_service().recall_verified_episodes(
+ dataset=_dataset_key(dataset),
+ limit=max(0, min(_int_arg(limit, 3), 20)),
+ )
+ return _json(result)
+ except Exception as exc:
+ return _json({"error": str(exc), "dataset": _dataset_key(dataset) or None})
+
+
+def paper9_commit_verified_episode(
+ out_dir: str,
+ dataset: str = "",
+ goal: str = "",
+ plan_args_json: str = "{}",
+) -> str:
+ """Commit an audited successful Paper9 run to verified episodic memory.
+
+ Call only when paper9_audit_run returned next_action=commit_verified_episode.
+ plan_args_json must be a JSON object containing the actual planning search
+ parameters. Failed, unaudited, or incomplete runs are rejected by code.
+ """
+
+ try:
+ plan_args = json.loads(plan_args_json or "{}")
+ if not isinstance(plan_args, dict):
+ raise ValueError("plan_args_json must decode to a JSON object")
+ result = get_world_model_v21_service().commit_verified_episode(
+ out_dir=str(out_dir or "").strip(),
+ dataset=_dataset_key(dataset) or str(dataset or "unknown").strip().lower(),
+ goal=str(goal or "").strip(),
+ plan_args=plan_args,
+ )
+ return _json(result)
+ except Exception as exc:
+ return _json({"error": str(exc), "out_dir": out_dir})
+
+
def _world_model_v21_plan_sync(
prepared_dir: str = "",
ensemble_dir: str = "",
@@ -314,6 +431,10 @@ def _world_model_v21_pipeline_sync(
n_members: str = "3",
epochs: str = "30",
out_subdir: str = "tool3",
+ cultivated_area_floor_delta_ha: str = "0",
+ baimu_area_floor_delta_ha: str = "",
+ gamma_conn: str = "",
+ delta_conn: str = "",
) -> str:
"""Run/reuse the full World Model v2.1 A->B->C->D pipeline.
@@ -356,6 +477,15 @@ def _world_model_v21_pipeline_sync(
"n_members": _int_arg(n_members, 3),
"epochs": _int_arg(epochs, 30),
"out_subdir": str(out_subdir or "tool3").strip() or "tool3",
+ "cultivated_area_floor_delta_ha": max(
+ _optional_float_arg(cultivated_area_floor_delta_ha) or 0.0,
+ 0.0,
+ ),
+ "baimu_area_floor_delta_ha": _optional_float_arg(
+ baimu_area_floor_delta_ha
+ ),
+ "gamma_conn": _optional_float_arg(gamma_conn),
+ "delta_conn": _optional_float_arg(delta_conn),
}
try:
from ..user_context import current_user_id
@@ -534,6 +664,10 @@ async def world_model_v21_pipeline(
n_members: str = "3",
epochs: str = "30",
out_subdir: str = "tool3",
+ cultivated_area_floor_delta_ha: str = "0",
+ baimu_area_floor_delta_ha: str = "",
+ gamma_conn: str = "",
+ delta_conn: str = "",
) -> str:
"""Run/reuse Paper9 Tools 1-4 as one long-running ADK tool.
@@ -566,6 +700,10 @@ async def world_model_v21_pipeline(
n_members,
epochs,
out_subdir,
+ cultivated_area_floor_delta_ha,
+ baimu_area_floor_delta_ha,
+ gamma_conn,
+ delta_conn,
)
@@ -581,7 +719,13 @@ async def world_model_v21_pipeline(
world_model_v21_pipeline.__qualname__ = "world_model_v21_pipeline"
-_SYNC_FUNCS = [world_model_v21_status]
+_SYNC_FUNCS = [
+ world_model_v21_status,
+ paper9_inspect_resources,
+ paper9_recall_verified_episodes,
+ paper9_audit_run,
+ paper9_commit_verified_episode,
+]
_LONG_RUNNING_FUNCS = [
world_model_v21_prepare,
world_model_v21_sample,
diff --git a/data_agent/world_model_v21.py b/data_agent/world_model_v21.py
index 4778dfc5..a17e81de 100644
--- a/data_agent/world_model_v21.py
+++ b/data_agent/world_model_v21.py
@@ -18,6 +18,14 @@
from pathlib import Path
from typing import Any
+from .paper9_agent_governance import (
+ EXPECTED_ALGORITHM_VERSION,
+ EXPECTED_PACKAGE_VERSION,
+ Paper9AuditPolicy,
+ Paper9EpisodeStore,
+ audit_paper9_run,
+)
+
VERSION = "2.1.0"
DEFAULT_REPO = Path(r"D:\test\_publish\arcgis-farmland-mpc")
@@ -77,6 +85,12 @@ def status(self) -> dict[str, Any]:
onnx_count = len(self.find_onnx_members(defaults["ensemble_dir"]))
ready = repo_exists and import_info["importable"]
+ version_compatible = bool(
+ ready
+ and import_info.get("package_version") == EXPECTED_PACKAGE_VERSION
+ and import_info.get("algorithm_version") == EXPECTED_ALGORITHM_VERSION
+ and import_info.get("source_matches_repo", True)
+ )
return {
"status": "ready" if ready else "unavailable",
"version": VERSION,
@@ -101,6 +115,15 @@ def status(self) -> dict[str, Any]:
"restoration_env": True,
"cultivated_area_floor": True,
"baimu_area_floor": True,
+ "hard_gate_audit": True,
+ "bounded_replan": True,
+ "verified_episode_memory": True,
+ },
+ "finals": {
+ "expected_package_version": EXPECTED_PACKAGE_VERSION,
+ "expected_algorithm_version": EXPECTED_ALGORITHM_VERSION,
+ "version_compatible": version_compatible,
+ "ready": version_compatible,
},
"runtime": {
"proj_data_dir": proj_data_dir,
@@ -108,6 +131,134 @@ def status(self) -> dict[str, Any]:
"onnx_member_count": onnx_count,
}
+ def inspect_resources(
+ self,
+ *,
+ dataset: str,
+ prepared_dir: str,
+ ensemble_dir: str,
+ ) -> dict[str, Any]:
+ """Inspect version binding and reusable artifacts without executing MPC."""
+
+ status = self.status()
+ prepared = Path(prepared_dir).expanduser() if prepared_dir else None
+ ensemble = Path(ensemble_dir).expanduser() if ensemble_dir else None
+ stages = {
+ "prepare": {
+ "ready": bool(prepared and self._prepared_ready(prepared)),
+ "path": str(prepared) if prepared else None,
+ },
+ "sample": {
+ "ready": bool(prepared and self._sample_ready(prepared)),
+ "path": str(prepared / "tool2") if prepared else None,
+ },
+ "train": {
+ "ready": bool(ensemble and self.find_onnx_members(ensemble)),
+ "path": str(ensemble) if ensemble else None,
+ "onnx_member_count": len(self.find_onnx_members(ensemble))
+ if ensemble
+ else 0,
+ },
+ }
+ reusable = [name for name, detail in stages.items() if detail["ready"]]
+ required = [name for name, detail in stages.items() if not detail["ready"]]
+ planning_ready = bool(
+ status.get("finals", {}).get("version_compatible")
+ and stages["prepare"]["ready"]
+ and stages["train"]["ready"]
+ )
+ land_use_contract = None
+ if prepared:
+ input_dltb = (
+ prepared / "dem_slope_analysis" / "output" / "DLTB_with_slope.shp"
+ )
+ if input_dltb.is_file():
+ try:
+ land_use_contract = self._detect_land_use_code_contract(input_dltb)
+ except Exception as exc:
+ land_use_contract = {"compatible": False, "error": str(exc)}
+ planning_ready = False
+ return {
+ "status": "ready" if planning_ready else "needs_action",
+ "dataset": dataset or None,
+ "version": status.get("version"),
+ "paper9": status.get("paper9"),
+ "finals": status.get("finals"),
+ "stages": stages,
+ "reusable_stages": reusable,
+ "required_stages": required,
+ "land_use_code_contract": land_use_contract,
+ "planning_ready": planning_ready,
+ "recommended_next_action": "run_plan"
+ if planning_ready
+ else "repair_version_or_missing_stages",
+ }
+
+ def audit_run(
+ self,
+ *,
+ out_dir: str,
+ attempt: int = 0,
+ cultivated_area_floor_delta_ha: float = 0.0,
+ ) -> dict[str, Any]:
+ """Apply deterministic hard gates and return a bounded recovery branch."""
+
+ return audit_paper9_run(
+ out_dir,
+ policy=Paper9AuditPolicy(
+ cultivated_area_floor_delta_ha=cultivated_area_floor_delta_ha,
+ max_replans=1,
+ ),
+ attempt=attempt,
+ )
+
+ def recall_verified_episodes(
+ self, *, dataset: str = "", limit: int = 3
+ ) -> dict[str, Any]:
+ """Recall only episodes that previously passed all deterministic gates."""
+
+ episodes = Paper9EpisodeStore().recall(dataset=dataset, limit=limit)
+ return {
+ "status": "ok",
+ "dataset": dataset or None,
+ "count": len(episodes),
+ "episodes": episodes,
+ }
+
+ def commit_verified_episode(
+ self,
+ *,
+ out_dir: str,
+ dataset: str,
+ goal: str,
+ plan_args: dict[str, Any] | None = None,
+ ) -> dict[str, Any]:
+ """Commit an audited run; failed or incomplete runs are rejected."""
+
+ root = Path(out_dir).expanduser().resolve()
+ audit_path = root / "paper9_agent_audit.json"
+ if not audit_path.is_file():
+ raise WorldModelV21ValidationError(
+ f"paper9_agent_audit.json not found under {root}; audit the run first"
+ )
+ audit = json.loads(audit_path.read_text(encoding="utf-8"))
+ status = self.status()
+ paper9 = status.get("paper9") or {}
+ record = Paper9EpisodeStore().commit(
+ audit=audit,
+ dataset=dataset,
+ goal=goal,
+ plan_args=plan_args,
+ provenance={
+ "adapter_version": VERSION,
+ "package_version": paper9.get("package_version"),
+ "algorithm_name": paper9.get("algorithm_name"),
+ "algorithm_version": paper9.get("algorithm_version"),
+ "paper9_commit": paper9.get("commit"),
+ },
+ )
+ return {"status": "committed", "episode": record}
+
def run_prepare(self, payload: dict[str, Any], user_id: str) -> dict[str, Any]:
"""Run Paper9 Tool 1 data preparation and return normalized JSON."""
self._ensure_proj_data_dir()
@@ -418,6 +569,11 @@ def run_plan(self, payload: dict[str, Any], user_id: str) -> dict[str, Any]:
if cfg["env_kind"] == "county" and input_dltb_fc.exists()
else None
)
+ land_use_contract = (
+ self._detect_land_use_code_contract(input_dltb_fc)
+ if output_fc_arg
+ else None
+ )
try:
summary = plan_run(
@@ -435,6 +591,8 @@ def run_plan(self, payload: dict[str, Any], user_id: str) -> dict[str, Any]:
env_kind=cfg["env_kind"],
output_fc=output_fc_arg,
input_dltb_fc=str(input_dltb_fc) if output_fc_arg else None,
+ farm_dlbm=(land_use_contract or {}).get("farm_dlbm", "0101"),
+ forest_dlbm=(land_use_contract or {}).get("forest_dlbm", "0301"),
cultivated_area_floor_delta_ha=cfg[
"cultivated_area_floor_delta_ha"
],
@@ -480,23 +638,85 @@ def run_plan(self, payload: dict[str, Any], user_id: str) -> dict[str, Any]:
"map_config": self._build_map_config(map_layer) if map_layer else None,
"map_update_queued": False,
"warnings": warnings,
+ "land_use_code_contract": land_use_contract,
+ }
+
+ def _detect_land_use_code_contract(self, input_dltb_fc: Path) -> dict[str, Any]:
+ """Choose output codes that match the input scheme before MPC execution."""
+
+ info = self._import_paper9()
+ if not info["importable"]:
+ raise WorldModelV21UnavailableError(info["error"] or "Paper9 import failed")
+ try:
+ import geopandas as gpd
+ from farmland_mpc.landuse import (
+ CURRENT_LAND_USE_SCHEME,
+ LEGACY_LAND_USE_SCHEME,
+ analyse_land_use_codes,
+ )
+
+ dltb = gpd.read_file(input_dltb_fc, columns=["DLBM"])
+ report = analyse_land_use_codes(
+ dltb["DLBM"], require_farmland=True, require_forest=True
+ )
+ except Exception as exc:
+ raise WorldModelV21ValidationError(
+ f"Unable to validate DLBM code scheme for {input_dltb_fc}: {exc}"
+ ) from exc
+
+ if report.scheme == CURRENT_LAND_USE_SCHEME:
+ farm_dlbm, forest_dlbm = "0101", "0301"
+ elif report.scheme == LEGACY_LAND_USE_SCHEME:
+ farm_dlbm, forest_dlbm = "011", "031"
+ else:
+ raise WorldModelV21ValidationError(
+ f"Unsupported DLBM code scheme: {report.scheme}"
+ )
+ return {
+ "compatible": True,
+ "scheme": report.scheme,
+ "farm_dlbm": farm_dlbm,
+ "forest_dlbm": forest_dlbm,
+ "code_counts": report.code_counts,
}
def _import_paper9(self) -> dict[str, Any]:
- repo = str(self.repo_path)
+ source_root = self.repo_path / "src" if (self.repo_path / "src").is_dir() else self.repo_path
+ repo = str(source_root)
if repo not in sys.path:
sys.path.insert(0, repo)
try:
package = importlib.import_module("farmland_mpc")
+ module_path = Path(getattr(package, "__file__", "") or "").resolve()
+ try:
+ source_matches_repo = module_path.is_relative_to(source_root.resolve())
+ except ValueError:
+ source_matches_repo = False
+ algorithm_name = None
+ algorithm_version = None
+ try:
+ version_module = importlib.import_module("paper9_mnr.version")
+ algorithm_name = getattr(version_module, "ALGORITHM_NAME", None)
+ algorithm_version = getattr(version_module, "ALGORITHM_VERSION", None)
+ except Exception:
+ pass
return {
"importable": True,
"package_version": getattr(package, "__version__", None),
+ "algorithm_name": algorithm_name,
+ "algorithm_version": algorithm_version,
+ "module_path": str(module_path),
+ "source_matches_repo": source_matches_repo,
"error": None,
}
except Exception as exc:
return {
"importable": False,
"package_version": None,
+ "algorithm_name": None,
+ "algorithm_version": None,
+ "module_path": None,
+ "source_matches_repo": False,
"error": str(exc),
}
@@ -617,6 +837,8 @@ def _normalize_summary(self, summary: dict[str, Any]) -> dict[str, Any]:
"baimu_area_change_ha": first.get(
"baimu_area_change_ha", aggregate.get("baimu_ha_mean")
),
+ "baimu_count_change": first.get("baimu_count_change"),
+ "cultivated_area_change_ha": first.get("cultivated_area_change_ha"),
"n_episodes": config.get("n_episodes"),
"n_blocks": config.get("n_blocks"),
"n_parcels": config.get("n_parcels"),
diff --git a/data_agent/world_model_v21_presentation.py b/data_agent/world_model_v21_presentation.py
index 2b6353dc..28c342a5 100644
--- a/data_agent/world_model_v21_presentation.py
+++ b/data_agent/world_model_v21_presentation.py
@@ -1,4 +1,4 @@
-"""Presentation helpers for World Model v2.1 chat responses."""
+"""Presentation helpers for the county farmland planning agent."""
from __future__ import annotations
@@ -6,6 +6,9 @@
import os
from typing import Any
+PUBLIC_AGENT_NAME = "县域耕地规划 Agent"
+PUBLIC_ENGINE_NAME = "县域耕地空间优化引擎"
+
def parse_world_model_v21_tool_response(value: Any) -> dict[str, Any] | None:
"""Return the planning result dict from common ADK tool response shapes."""
@@ -33,6 +36,25 @@ def parse_world_model_v21_tool_response(value: Any) -> dict[str, Any] | None:
return None
+def parse_structured_tool_response(value: Any) -> dict[str, Any] | None:
+ """Return a JSON object from common ADK function-response wrappers."""
+ if value is None:
+ return None
+ if isinstance(value, str):
+ try:
+ return parse_structured_tool_response(json.loads(value))
+ except (json.JSONDecodeError, TypeError):
+ return None
+ if not isinstance(value, dict):
+ return None
+ for key in ("result", "output", "response", "content"):
+ if key in value:
+ nested = parse_structured_tool_response(value[key])
+ if nested is not None:
+ return nested
+ return value
+
+
def _fmt_number(value: Any, digits: int = 2) -> str:
if value is None:
return "-"
@@ -45,53 +67,191 @@ def _fmt_number(value: Any, digits: int = 2) -> str:
return f"{number:.{digits}f}"
+def _fmt_duration(value: Any) -> str:
+ try:
+ seconds = float(value)
+ except (TypeError, ValueError):
+ return ""
+ if seconds < 0:
+ return ""
+ if seconds < 0.1:
+ return f"{seconds:.3f} 秒"
+ return f"{seconds:.1f} 秒"
+
+
def _artifact_names(artifacts: dict[str, Any]) -> str:
names: list[str] = []
for key in ("summary_json", "land_use_npy", "optimized_shp", "map_layer"):
value = artifacts.get(key)
- if not value:
+ if value:
+ names.append(os.path.basename(str(value)))
+ return "、".join(names) if names else "-"
+
+
+def _step_status(value: Any) -> str:
+ status = str(value or "ok")
+ labels = {
+ "skipped_reused": "已复用（未重复执行）",
+ "ok": "完成",
+ "ready": "就绪",
+ "committed": "已写入",
+ "passed": "通过",
+ "failed": "失败",
+ "error": "失败",
+ }
+ return labels.get(status, status)
+
+
+def _normalize_trace(tool_trace: list[Any] | None) -> list[dict[str, Any]]:
+ normalized: list[dict[str, Any]] = []
+ for entry in tool_trace or []:
+ if isinstance(entry, str):
+ normalized.append({"tool_name": entry})
+ continue
+ if not isinstance(entry, dict):
+ continue
+ tool_name = entry.get("tool_name") or entry.get("name")
+ if not tool_name:
+ continue
+ normalized.append({
+ "tool_name": str(tool_name),
+ "duration_s": entry.get("duration_s", entry.get("duration")),
+ "status": entry.get("status"),
+ "is_error": bool(entry.get("is_error", False)),
+ })
+ return normalized
+
+
+def _trace_duration(trace: list[dict[str, Any]], names: set[str]) -> float | None:
+ values: list[float] = []
+ for entry in trace:
+ if entry.get("tool_name") not in names:
continue
- names.append(os.path.basename(str(value)))
- return ", ".join(names) if names else "-"
+ try:
+ values.append(float(entry.get("duration_s")))
+ except (TypeError, ValueError):
+ continue
+ return sum(values) if values else None
+
+
+def _has_reused_pipeline_stages(result: dict[str, Any]) -> bool:
+ return any(
+ isinstance(step, dict) and step.get("status") == "skipped_reused"
+ for step in result.get("steps") or []
+ )
+
+
+def _tool_trace_lines(
+ trace: list[dict[str, Any]],
+ *,
+ result: dict[str, Any],
+ status_result: dict[str, Any] | None,
+ audit_result: dict[str, Any] | None,
+ commit_result: dict[str, Any] | None,
+) -> list[str]:
+ finals_status = (status_result or {}).get("finals") or {}
+ version_compatible = finals_status.get("version_compatible")
+ reused = _has_reused_pipeline_stages(result)
+ audit_passed = bool(audit_result and audit_result.get("hard_constraint_passed"))
+ committed = bool(commit_result and commit_result.get("status") == "committed")
+
+ descriptions = {
+ "world_model_v21_status": (
+ "观察版本与运行状态",
+ "版本兼容" if version_compatible is True else "状态检查完成",
+ ),
+ "paper9_inspect_resources": (
+ "检查规划资源",
+ "数据与模型资源可复用" if reused else "资源检查完成",
+ ),
+ "paper9_recall_verified_episodes": (
+ "召回已验证经验",
+ "读取同作用域经验作为规划上下文",
+ ),
+ "world_model_v21_prepare": ("准备空间状态", "数据准备完成"),
+ "world_model_v21_sample": ("生成转移样本", "样本生成完成"),
+ "world_model_v21_train": ("训练状态转移模型", "模型训练完成"),
+ "world_model_v21_pipeline": (
+ "根据资源状态选择快速 MPC 路径",
+ "复用准备与训练产物，执行空间规划" if reused else "执行 A/B/C/D 规划链",
+ ),
+ "world_model_v21_plan": ("选择 MPC 空间规划", "完成候选行动搜索"),
+ "paper9_audit_run": (
+ "检查面积、坡度、连片度与空间产物",
+ "硬约束审计通过" if audit_passed else "硬约束审计未通过",
+ ),
+ "paper9_commit_verified_episode": (
+ "根据审计结果决定是否写入经验",
+ "已写入已验证经验库" if committed else "未写入经验库",
+ ),
+ }
+
+ lines: list[str] = []
+ for index, entry in enumerate(trace, start=1):
+ name = str(entry.get("tool_name"))
+ action, outcome = descriptions.get(name, ("执行领域函数", "调用完成"))
+ if entry.get("is_error"):
+ outcome = "调用失败"
+ duration = _fmt_duration(entry.get("duration_s"))
+ duration_text = f" · {duration}" if duration else ""
+ lines.append(
+ f"{index}. **{action}** → {outcome} · `{name}`{duration_text}"
+ )
+ return lines
def format_world_model_v21_progress_for_chat(
result: dict[str, Any],
pipeline_label: str = "@WorldModelV21",
+ audit_result: dict[str, Any] | None = None,
+ commit_result: dict[str, Any] | None = None,
+ tool_trace: list[Any] | None = None,
+ total_duration_s: float | None = None,
) -> str:
- """Build a deterministic progress summary for WorldModelV21 direct runs."""
+ """Build a concise Chinese progress summary for direct planning runs."""
+ del pipeline_label
step_labels = {
- "prepare": "A / Tool 1 Prepare",
- "sample": "B / Tool 2 Sample",
- "train": "C / Tool 3 Train",
- "plan": "D / Tool 4 Plan",
+ "prepare": "A / 数据准备",
+ "sample": "B / 样本生成",
+ "train": "C / 状态转移模型训练",
+ "plan": "D / MPC 规划执行",
}
+ lines = [f"**{PUBLIC_AGENT_NAME}** · Gemma 4 + Google ADK 受控自主闭环", ""]
if result.get("mode") == "pipeline_a_to_d":
- steps = [s for s in result.get("steps") or [] if isinstance(s, dict)]
- lines = [f"**{pipeline_label}** A/B/C/D 4 阶段完成", ""]
- if steps:
- for step in steps:
- key = str(step.get("step") or "-")
- status = step.get("status") or step.get("mode") or "ok"
- lines.append(f"✓ {step_labels.get(key, key)}: {status}")
- else:
- for label in step_labels.values():
- lines.append(f"✓ {label}")
- return "\n".join(lines)
+ for step in result.get("steps") or []:
+ if not isinstance(step, dict):
+ continue
+ key = str(step.get("step") or "-")
+ status = step.get("status") or step.get("mode") or "ok"
+ lines.append(f"✓ {step_labels.get(key, key)}：{_step_status(status)}")
+ else:
+ lines.append("✓ MPC 规划执行：完成")
- return "\n".join([
- f"**{pipeline_label}** Tool Calling 完成",
- "",
- "✓ world_model_v21_status",
- "✓ D / Tool 4 Plan",
- ])
+ if audit_result:
+ passed = bool(audit_result.get("hard_constraint_passed"))
+ lines.append(f"✓ 硬约束审计：{'通过' if passed else '未通过'}")
+ if commit_result:
+ lines.append(f"✓ 已验证经验库：{_step_status(commit_result.get('status'))}")
+
+ trace = _normalize_trace(tool_trace)
+ if trace:
+ lines.append(f"原生函数调用：{len(trace)} 次")
+ duration = _fmt_duration(total_duration_s)
+ if duration:
+ lines.append(f"总用时：{duration}")
+ return "\n".join(lines)
def format_world_model_v21_result_for_chat(
result: dict[str, Any],
tool_args: dict[str, Any] | None = None,
+ status_result: dict[str, Any] | None = None,
+ audit_result: dict[str, Any] | None = None,
+ commit_result: dict[str, Any] | None = None,
+ tool_trace: list[Any] | None = None,
+ total_duration_s: float | None = None,
) -> str:
- """Build a deterministic Chinese summary for the WorldModelV21 direct agent."""
+ """Build an evidence-backed Chinese summary for the county planning agent."""
is_pipeline = (
result.get("mode") == "pipeline_a_to_d"
and isinstance(result.get("plan_result"), dict)
@@ -103,84 +263,154 @@ def format_world_model_v21_result_for_chat(
summary = plan_result.get("summary") or {}
artifacts = plan_result.get("artifacts") or {}
args = tool_args or {}
- map_update = plan_result.get("map_update") or plan_result.get("map_config") or {}
- layers = map_update.get("layers") if isinstance(map_update, dict) else []
- layer_name = layers[0].get("name") if layers else "World Model v2.1 optimized"
-
- horizon = args.get("horizon") or summary.get("horizon") or "1"
- top_k = args.get("top_k") or summary.get("top_k") or "1"
- env_kind = plan_result.get("env_kind", "-")
- metric_lines = [
- f"- Steps Run: {_fmt_number(summary.get('steps_run'))}（环境实际执行步数）",
- f"- Horizon: {horizon}（MPC 前瞻步长）",
- f"- Top K: {top_k}（每步候选动作数）",
- f"- N Blocks: {_fmt_number(summary.get('n_blocks'))}",
- ]
+ trace = _normalize_trace(tool_trace)
+ audit_passed = bool(audit_result and audit_result.get("hard_constraint_passed"))
+ memory_committed = bool(commit_result and commit_result.get("status") == "committed")
+
+ if audit_result is None:
+ title = "### 规划结果尚未完成审计"
+ headline = "该结果不得作为成功方案，也不会写入已验证经验库。"
+ elif not audit_passed:
+ title = "### 县域耕地规划已停止"
+ headline = "规划未通过硬约束审计，系统已按受控策略停止。"
+ elif not memory_committed:
+ title = "### 规划已通过审计，尚未写入经验"
+ headline = "只有经验提交成功后，才算完成本次受控自主闭环。"
+ else:
+ title = "### 县域耕地受控规划完成"
+ headline = "硬约束审计通过后，已写入已验证经验库。"
+
+ lines = [title, "", headline]
+ if trace:
+ lines.extend(["", f"**Gemma 4 + Google ADK** · {len(trace)} 次原生函数调用"])
+
+ planning_duration = _trace_duration(
+ trace,
+ {"world_model_v21_pipeline", "world_model_v21_plan"},
+ )
+ governance_duration = _trace_duration(
+ trace,
+ {"paper9_audit_run", "paper9_commit_verified_episode"},
+ )
+ duration_parts: list[str] = []
+ if _fmt_duration(total_duration_s):
+ duration_parts.append(f"总用时 {_fmt_duration(total_duration_s)}")
+ if _fmt_duration(planning_duration):
+ duration_parts.append(f"MPC 规划 {_fmt_duration(planning_duration)}")
+ if _fmt_duration(governance_duration):
+ duration_parts.append(f"审计与经验提交 {_fmt_duration(governance_duration)}")
+ if duration_parts:
+ lines.extend(["", "**运行耗时**：" + " · ".join(duration_parts)])
+
+ lines.extend(["", "#### 受控自主决策轨迹"])
+ trace_lines = _tool_trace_lines(
+ trace,
+ result=result,
+ status_result=status_result,
+ audit_result=audit_result,
+ commit_result=commit_result,
+ )
+ lines.extend(trace_lines or ["未捕获完整函数调用轨迹，请查看运行日志。"])
+
+ status_engine = (status_result or {}).get("paper9") or {}
+ dataset = str(args.get("dataset") or "-")
+ dataset_label = {"bishan": "璧山（bishan）", "dongxing": "东兴（dongxing）"}.get(
+ dataset,
+ dataset,
+ )
+ lines.extend([
+ "",
+ "#### 规划结果",
+ f"- 数据集：{dataset_label}",
+ f"- 规划引擎：{PUBLIC_ENGINE_NAME}（学习型状态转移模型集成 + MPC）",
+ (
+ "- 版本："
+ f"适配器 {plan_result.get('version', result.get('version', '-'))} · "
+ f"引擎包 {status_engine.get('package_version', '-')} · "
+ f"算法 {status_engine.get('algorithm_version', '-')}"
+ ),
+ f"- 环境执行步数：{_fmt_number(summary.get('steps_run'))}",
+ f"- MPC 前瞻步长：{args.get('horizon') or summary.get('horizon') or '1'}",
+ f"- 每步候选行动数：{args.get('top_k') or summary.get('top_k') or '1'}",
+ f"- 空间块数：{_fmt_number(summary.get('n_blocks'))}",
+ ])
if summary.get("n_parcels") is not None:
- metric_lines.append(f"- N Parcels: {_fmt_number(summary.get('n_parcels'))}")
+ lines.append(f"- 图斑数：{_fmt_number(summary.get('n_parcels'))}")
if summary.get("n_selected") is not None:
- metric_lines.append(f"- N Selected: {_fmt_number(summary.get('n_selected'))}")
+ lines.append(f"- 选中决策单元：{_fmt_number(summary.get('n_selected'))}")
if summary.get("swaps_completed") is not None:
- metric_lines.append(f"- Swaps Completed: {_fmt_number(summary.get('swaps_completed'))}")
+ lines.append(f"- 完成双向置换：{_fmt_number(summary.get('swaps_completed'))} 对")
+ if summary.get("cultivated_area_change_ha") is not None:
+ lines.append(
+ "- 耕地面积变化："
+ f"{_fmt_number(summary.get('cultivated_area_change_ha'), 4)} ha"
+ )
if summary.get("slope_change_pct") is not None:
- metric_lines.append(f"- Slope Change: {_fmt_number(summary.get('slope_change_pct'), 4)}%")
+ lines.append(f"- 平均坡度变化：{_fmt_number(summary.get('slope_change_pct'), 4)}%")
if summary.get("cont_change") is not None:
- metric_lines.append(f"- Contiguity Change: {_fmt_number(summary.get('cont_change'), 4)}")
+ lines.append(f"- 连片度变化：{_fmt_number(summary.get('cont_change'), 4)}")
if summary.get("baimu_area_change_ha") is not None:
- metric_lines.append(f"- Baimu Area Change: {_fmt_number(summary.get('baimu_area_change_ha'), 2)} ha")
- metric_lines.extend([
- f"- Total Reward: {_fmt_number(summary.get('total_reward'))}",
- f"- Artifacts: {_artifact_names(artifacts)}",
- ])
-
- if env_kind == "county":
- map_description = (
- f"右侧地图展示 `{layer_name}` 图层，按优化变化字段 `CHG_FLAG` 分类显示；"
- "灰色代表保持不变，红色代表耕地 -> 林地，绿色代表林地 -> 耕地。"
- )
- else:
- map_description = (
- f"右侧地图展示 `{layer_name}` 图层，绿色代表 MPC selected（选中的决策单元），"
- "灰色代表 Not selected（未选中的单元）。"
+ lines.append(
+ "- 百亩方面积变化："
+ f"{_fmt_number(summary.get('baimu_area_change_ha'), 2)} ha"
)
+ if summary.get("total_reward") is not None:
+ lines.append(f"- 总奖励：{_fmt_number(summary.get('total_reward'))}")
+ lines.append(f"- 交付成果：{_artifact_names(artifacts)}")
if is_pipeline:
step_labels = {
- "prepare": "A / Tool 1 Prepare",
- "sample": "B / Tool 2 Sample",
- "train": "C / Tool 3 Train",
- "plan": "D / Tool 4 Plan",
+ "prepare": "A / 数据准备",
+ "sample": "B / 样本生成",
+ "train": "C / 状态转移模型训练",
+ "plan": "D / MPC 规划执行",
}
- pipeline_lines = ["", "A/B/C/D 阶段："]
+ lines.extend(["", "#### 算法阶段"])
for step in result.get("steps") or []:
if not isinstance(step, dict):
continue
key = str(step.get("step") or "-")
status = step.get("status") or step.get("mode") or "ok"
- pipeline_lines.append(f"- {step_labels.get(key, key)}: {status}")
- tool_trace = "world_model_v21_status -> world_model_v21_pipeline"
- mode = f"{result.get('mode', '-')}; plan={plan_result.get('mode', '-')}"
+ lines.append(f"- {step_labels.get(key, key)}：{_step_status(status)}")
+
+ lines.extend(["", "#### 治理结果"])
+ if audit_result:
+ lines.append(f"- 硬约束校验：{'通过' if audit_passed else '未通过'}")
+ lines.append(
+ "- 下一动作："
+ + (
+ "写入已验证经验"
+ if audit_result.get("next_action") == "commit_verified_episode"
+ else "停止并转人工复核"
+ )
+ )
+ audit_path = audit_result.get("audit_path")
+ if audit_path:
+ lines.append(f"- 审计证据：{os.path.basename(str(audit_path))}")
+ if audit_result.get("failure_reasons"):
+ lines.append("- 未通过原因：" + "；".join(audit_result["failure_reasons"]))
+ else:
+ lines.append("- 硬约束校验：未捕获")
+ if commit_result:
+ episode = commit_result.get("episode") or {}
+ lines.extend([
+ f"- 已验证经验库：{_step_status(commit_result.get('status'))}",
+ f"- 经验编号：{episode.get('episode_id', '-')}",
+ ])
else:
- pipeline_lines = []
- tool_trace = "world_model_v21_status -> world_model_v21_plan"
- mode = result.get("mode", "-")
+ lines.append("- 已验证经验库：未写入")
- return "\n".join([
- "已成功完成。",
- "",
- "状态摘要：",
- f"- Status: {plan_result.get('status', result.get('status', '-'))}",
- f"- Version: {plan_result.get('version', result.get('version', '-'))}",
- f"- Mode: {mode}",
- f"- Env Kind: {env_kind}",
- f"- Prepared Dir: {plan_result.get('prepared_dir') or result.get('prepared_dir') or '-'}",
- f"- Ensemble Dir: {plan_result.get('ensemble_dir') or result.get('ensemble_dir') or '-'}",
- *metric_lines,
- *pipeline_lines,
- "",
- "地图说明：",
- map_description,
- "",
- "工具调用轨迹：",
- tool_trace,
- ])
+ map_update = plan_result.get("map_update") or plan_result.get("map_config") or {}
+ layers = map_update.get("layers") if isinstance(map_update, dict) else []
+ if layers:
+ if plan_result.get("env_kind") == "county":
+ map_description = (
+ "右侧地图已加载县域耕地优化结果：灰色为保持不变，"
+ "红色为耕地 → 林地，绿色为林地 → 耕地。"
+ )
+ else:
+ map_description = (
+ "右侧地图已加载 MPC 决策结果：绿色为选中单元，灰色为未选中单元。"
+ )
+ lines.extend(["", "#### 地图", map_description])
+ return "\n".join(lines)
diff --git a/data_agent/world_model_v21_report.py b/data_agent/world_model_v21_report.py
new file mode 100644
index 00000000..fae3daec
--- /dev/null
+++ b/data_agent/world_model_v21_report.py
@@ -0,0 +1,994 @@
+"""Evidence-backed visual report for county farmland planning runs."""
+
+from __future__ import annotations
+
+import json
+import math
+import tempfile
+import textwrap
+from datetime import datetime
+from pathlib import Path
+from typing import Any
+
+from docx import Document
+from docx.enum.table import WD_CELL_VERTICAL_ALIGNMENT, WD_TABLE_ALIGNMENT
+from docx.enum.text import WD_ALIGN_PARAGRAPH
+from docx.oxml import OxmlElement
+from docx.oxml.ns import qn
+from docx.shared import Cm, Pt, RGBColor
+
+from .report_generator import (
+ _set_cell_background,
+ _setup_page,
+ _setup_styles,
+ convert_word_to_pdf,
+)
+
+PUBLIC_AGENT_NAME = "县域耕地规划 Agent"
+PUBLIC_ENGINE_NAME = "县域耕地空间优化引擎"
+REPORT_TITLE = "县域耕地空间规划与硬约束审计报告"
+
+NAVY = "17324D"
+BLUE = "2563EB"
+GREEN = "15803D"
+LIGHT_GREEN = "EAF6EE"
+RED = "B91C1C"
+LIGHT_RED = "FCEBEC"
+GRAY = "64748B"
+LIGHT_GRAY = "F1F5F9"
+WHITE = "FFFFFF"
+
+
+def _plan_result(context: dict[str, Any]) -> tuple[dict[str, Any], dict[str, Any]]:
+ result = context.get("result") or {}
+ if not isinstance(result, dict):
+ result = {}
+ if result.get("mode") == "pipeline_a_to_d" and isinstance(result.get("plan_result"), dict):
+ return result, result["plan_result"]
+ return result, result
+
+
+def _load_json(path: Path | None) -> dict[str, Any]:
+ if not path or not path.is_file():
+ return {}
+ try:
+ value = json.loads(path.read_text(encoding="utf-8"))
+ except (OSError, json.JSONDecodeError):
+ return {}
+ return value if isinstance(value, dict) else {}
+
+
+def _run_dir(plan: dict[str, Any], audit: dict[str, Any]) -> Path | None:
+ for value in (plan.get("out_dir"), audit.get("out_dir")):
+ if value:
+ path = Path(str(value)).expanduser()
+ if path.is_dir():
+ return path
+ return None
+
+
+def _artifact_path(
+ run_dir: Path | None,
+ plan: dict[str, Any],
+ *names: str,
+) -> Path | None:
+ artifacts = plan.get("artifacts") or {}
+ candidates: list[Path] = []
+ if run_dir:
+ for name in names:
+ candidates.append(run_dir / name)
+ for key in ("map_layer", "optimized_shp", "summary_json"):
+ value = artifacts.get(key)
+ if not value:
+ continue
+ path = Path(str(value)).expanduser()
+ candidates.append(path)
+ if run_dir:
+ candidates.append(run_dir / path.name)
+ for candidate in candidates:
+ if candidate.is_file() and candidate.name in names:
+ return candidate
+ return None
+
+
+def _as_float(value: Any) -> float | None:
+ try:
+ number = float(value)
+ except (TypeError, ValueError):
+ return None
+ return number if math.isfinite(number) else None
+
+
+def _fmt(value: Any, digits: int = 2, signed: bool = False) -> str:
+ number = _as_float(value)
+ if number is None:
+ return "-"
+ prefix = "+" if signed and number > 0 else ""
+ if number.is_integer():
+ return f"{prefix}{int(number):,}"
+ return f"{prefix}{number:,.{digits}f}"
+
+
+def _duration(value: Any) -> str:
+ number = _as_float(value)
+ if number is None or number < 0:
+ return "-"
+ if number < 0.1:
+ return f"{number:.3f} 秒"
+ return f"{number:.1f} 秒"
+
+
+def _dataset_label(value: Any) -> str:
+ text = str(value or "-")
+ return {
+ "bishan": "璧山（bishan）",
+ "dongxing": "东兴（dongxing）",
+ }.get(text, text)
+
+
+def _configure_plot_fonts() -> None:
+ import matplotlib.pyplot as plt
+
+ try:
+ from .utils import _configure_fonts
+
+ _configure_fonts()
+ except Exception:
+ pass
+ plt.rcParams["axes.unicode_minus"] = False
+
+
+def _metric_values(normalized: dict[str, Any], raw_summary: dict[str, Any]) -> dict[str, Any]:
+ raw_results = raw_summary.get("results") or []
+ raw_first = raw_results[0] if raw_results and isinstance(raw_results[0], dict) else {}
+ config = raw_summary.get("config") or {}
+ output = raw_summary.get("shapefile_output") or {}
+ return {
+ "cultivated_area_change_ha": normalized.get(
+ "cultivated_area_change_ha", raw_first.get("cultivated_area_change_ha")
+ ),
+ "slope_change_pct": normalized.get("slope_change_pct", raw_first.get("slope_change_pct")),
+ "cont_change": normalized.get("cont_change", raw_first.get("cont_change")),
+ "baimu_area_change_ha": normalized.get(
+ "baimu_area_change_ha", raw_first.get("baimu_area_change_ha")
+ ),
+ "total_reward": normalized.get("total_reward", raw_first.get("total_reward")),
+ "steps_run": normalized.get("steps_run", raw_first.get("steps_run")),
+ "swaps_completed": normalized.get("swaps_completed", raw_first.get("swaps_completed")),
+ "n_blocks": normalized.get("n_blocks", config.get("n_blocks")),
+ "n_parcels": normalized.get("n_parcels", config.get("n_parcels")),
+ "n_input": output.get("n_input"),
+ "n_farm_to_forest": output.get("n_farm_to_forest"),
+ "n_forest_to_farm": output.get("n_forest_to_farm"),
+ "n_unchanged": output.get("n_unchanged"),
+ "horizon": config.get("horizon"),
+ "top_k": config.get("top_k"),
+ }
+
+
+def _draw_kpi_dashboard(metrics: dict[str, Any], output_path: Path) -> None:
+ import matplotlib
+
+ matplotlib.use("Agg")
+ import matplotlib.pyplot as plt
+ from matplotlib.patches import FancyBboxPatch
+
+ _configure_plot_fonts()
+ fig = plt.figure(figsize=(12, 5.5), facecolor="#F8FAFC")
+ fig.text(0.04, 0.93, "规划成效概览", fontsize=19, weight="bold", color="#17324D")
+ fig.text(
+ 0.04,
+ 0.875,
+ "所有数值均来自本次 MPC 汇总文件，正负号表示相对规划前的变化",
+ fontsize=10,
+ color="#64748B",
+ )
+
+ cards = [
+ (
+ "耕地面积",
+ f"{_fmt(metrics.get('cultivated_area_change_ha'), 4, True)} ha",
+ "满足面积不减少门槛",
+ "#EAF6EE",
+ "#15803D",
+ ),
+ (
+ "平均坡度",
+ f"{_fmt(metrics.get('slope_change_pct'), 4, True)}%",
+ "负值表示坡度降低",
+ "#EAF6EE",
+ "#15803D",
+ ),
+ (
+ "空间连片度",
+ _fmt(metrics.get("cont_change"), 4, True),
+ "正值表示连片度提升",
+ "#EAF6EE",
+ "#15803D",
+ ),
+ (
+ "百亩方面积",
+ f"{_fmt(metrics.get('baimu_area_change_ha'), 2, True)} ha",
+ "集中连片规模增量",
+ "#EAF6EE",
+ "#15803D",
+ ),
+ ]
+ card_w = 0.215
+ gap = 0.02
+ for index, (label, value, note, fill, color) in enumerate(cards):
+ x = 0.04 + index * (card_w + gap)
+ box = FancyBboxPatch(
+ (x, 0.47),
+ card_w,
+ 0.31,
+ boxstyle="round,pad=0.012,rounding_size=0.015",
+ transform=fig.transFigure,
+ linewidth=1,
+ edgecolor="#D7E2EA",
+ facecolor=fill,
+ )
+ fig.patches.append(box)
+ fig.text(x + 0.018, 0.715, label, fontsize=11, color="#475569")
+ fig.text(x + 0.018, 0.615, value, fontsize=21, weight="bold", color=color)
+ fig.text(x + 0.018, 0.515, note, fontsize=9, color="#64748B")
+
+ scale = [
+ ("空间图斑", _fmt(metrics.get("n_parcels"), 0)),
+ ("空间块", _fmt(metrics.get("n_blocks"), 0)),
+ ("双向置换", f"{_fmt(metrics.get('swaps_completed'), 0)} 对"),
+ ("环境步数", _fmt(metrics.get("steps_run"), 0)),
+ ("总奖励", _fmt(metrics.get("total_reward"), 2)),
+ ]
+ for index, (label, value) in enumerate(scale):
+ x = 0.04 + index * 0.188
+ fig.text(x, 0.31, value, fontsize=15, weight="bold", color="#17324D")
+ fig.text(x, 0.245, label, fontsize=9.5, color="#64748B")
+ fig.lines.append(
+ plt.Line2D([0.04, 0.96], [0.39, 0.39], transform=fig.transFigure, color="#D7E2EA")
+ )
+ fig.text(
+ 0.04,
+ 0.09,
+ "决策方法：学习型状态转移模型集成 + 模型预测控制（MPC）",
+ fontsize=10.5,
+ color="#334155",
+ )
+ fig.savefig(output_path, dpi=190, bbox_inches="tight", facecolor=fig.get_facecolor())
+ plt.close(fig)
+
+
+def _flag_code(value: Any) -> int:
+ try:
+ return int(float(value))
+ except (TypeError, ValueError):
+ return -1
+
+
+def _draw_planning_map(
+ layer_path: Path | None,
+ metrics: dict[str, Any],
+ output_path: Path,
+) -> bool:
+ if not layer_path:
+ return False
+ try:
+ import geopandas as gpd
+ import matplotlib
+
+ matplotlib.use("Agg")
+ import matplotlib.pyplot as plt
+ import numpy as np
+ from matplotlib.colors import BoundaryNorm, ListedColormap
+ from matplotlib.patches import Patch
+ from rasterio.features import rasterize
+ from rasterio.transform import from_bounds
+
+ _configure_plot_fonts()
+ try:
+ gdf = gpd.read_file(layer_path, columns=["CHG_FLAG"])
+ except (TypeError, ValueError):
+ gdf = gpd.read_file(layer_path)
+ if gdf.empty or "geometry" not in gdf:
+ return False
+ gdf = gdf[gdf.geometry.notna() & ~gdf.geometry.is_empty].copy()
+ if gdf.empty:
+ return False
+ flags = gdf.get("CHG_FLAG")
+ if flags is None:
+ flags = gdf.index.to_series().map(lambda _: -1)
+ else:
+ flags = flags.map(_flag_code)
+
+ fig = plt.figure(figsize=(12, 7.2), facecolor="#F8FAFC")
+ ax = fig.add_axes([0.05, 0.08, 0.62, 0.78])
+ ax.set_facecolor("#F8FAFC")
+ min_x, min_y, max_x, max_y = gdf.total_bounds
+ width = 1400
+ data_ratio = (max_y - min_y) / max(max_x - min_x, 1e-12)
+ height = max(900, min(1800, int(width * data_ratio)))
+ transform = from_bounds(min_x, min_y, max_x, max_y, width, height)
+ category_grid = rasterize(
+ (
+ (geometry, code + 1 if code in {0, 1, 2} else 1)
+ for geometry, code in zip(gdf.geometry, flags, strict=False)
+ ),
+ out_shape=(height, width),
+ transform=transform,
+ fill=0,
+ all_touched=True,
+ dtype=np.uint8,
+ )
+ color_map = ListedColormap(["#F8FAFC", "#DDE4EA", "#DC2626", "#16A34A"])
+ norm = BoundaryNorm([-0.5, 0.5, 1.5, 2.5, 3.5], color_map.N)
+ ax.imshow(
+ category_grid,
+ extent=(min_x, max_x, min_y, max_y),
+ origin="upper",
+ interpolation="nearest",
+ cmap=color_map,
+ norm=norm,
+ aspect="equal",
+ )
+
+ ax.set_axis_off()
+ fig.text(
+ 0.04,
+ 0.94,
+ "县域耕地空间布局优化结果",
+ fontsize=18,
+ weight="bold",
+ color="#17324D",
+ )
+ fig.text(
+ 0.04,
+ 0.89,
+ "真实优化图层按变化标记分类渲染",
+ fontsize=10,
+ color="#64748B",
+ )
+ legend = [
+ Patch(facecolor="#DDE4EA", edgecolor="#C4CED8", label="保持不变"),
+ Patch(facecolor="#DC2626", edgecolor="#991B1B", label="耕地 -> 林地"),
+ Patch(facecolor="#16A34A", edgecolor="#166534", label="林地 -> 耕地"),
+ ]
+ fig.legend(
+ handles=legend,
+ loc="upper left",
+ bbox_to_anchor=(0.70, 0.76),
+ frameon=True,
+ framealpha=0.96,
+ facecolor="white",
+ edgecolor="#D7E2EA",
+ fontsize=10,
+ )
+ fig.text(
+ 0.70,
+ 0.48,
+ "变化统计",
+ fontsize=12,
+ weight="bold",
+ color="#17324D",
+ )
+ fig.text(
+ 0.70,
+ 0.40,
+ f"{_fmt(metrics.get('n_farm_to_forest'), 0)} 个",
+ fontsize=18,
+ weight="bold",
+ color="#B91C1C",
+ )
+ fig.text(0.70, 0.355, "耕地 -> 林地", fontsize=9.5, color="#64748B")
+ fig.text(
+ 0.84,
+ 0.40,
+ f"{_fmt(metrics.get('n_forest_to_farm'), 0)} 个",
+ fontsize=18,
+ weight="bold",
+ color="#15803D",
+ )
+ fig.text(0.84, 0.355, "林地 -> 耕地", fontsize=9.5, color="#64748B")
+ fig.text(
+ 0.70,
+ 0.24,
+ f"{_fmt(metrics.get('n_parcels'), 0)} 个",
+ fontsize=18,
+ weight="bold",
+ color="#17324D",
+ )
+ fig.text(0.70, 0.195, "规划环境图斑", fontsize=9.5, color="#64748B")
+ fig.text(
+ 0.84,
+ 0.24,
+ f"{_fmt(metrics.get('swaps_completed'), 0)} 对",
+ fontsize=18,
+ weight="bold",
+ color="#17324D",
+ )
+ fig.text(0.84, 0.195, "完成双向置换", fontsize=9.5, color="#64748B")
+ fig.savefig(output_path, dpi=210, facecolor=fig.get_facecolor())
+ plt.close(fig)
+ return True
+ except Exception:
+ return False
+
+
+TRACE_LABELS = {
+ "world_model_v21_status": "检查版本与运行状态",
+ "paper9_inspect_resources": "检查数据与模型资源",
+ "paper9_recall_verified_episodes": "召回已验证经验",
+ "world_model_v21_pipeline": "选择并执行快速 MPC 路径",
+ "world_model_v21_plan": "执行 MPC 空间规划",
+ "paper9_audit_run": "执行硬约束审计",
+ "paper9_commit_verified_episode": "写入已验证经验库",
+}
+
+
+def _normalized_trace(context: dict[str, Any]) -> list[dict[str, Any]]:
+ result: list[dict[str, Any]] = []
+ for entry in context.get("tool_trace") or []:
+ if isinstance(entry, str):
+ result.append({"tool_name": entry})
+ elif isinstance(entry, dict) and (entry.get("tool_name") or entry.get("name")):
+ result.append(
+ {
+ "tool_name": str(entry.get("tool_name") or entry.get("name")),
+ "duration_s": entry.get("duration_s", entry.get("duration")),
+ }
+ )
+ return result
+
+
+def _draw_agent_trace(trace: list[dict[str, Any]], output_path: Path) -> bool:
+ if not trace:
+ return False
+ import matplotlib
+
+ matplotlib.use("Agg")
+ import matplotlib.pyplot as plt
+
+ _configure_plot_fonts()
+ count = len(trace)
+ fig, ax = plt.subplots(figsize=(12, 4.6), facecolor="#F8FAFC")
+ ax.set_xlim(-0.5, count - 0.5)
+ ax.set_ylim(-1.05, 1.0)
+ ax.plot(range(count), [0] * count, color="#9FB2C3", linewidth=3, zorder=1)
+ colors = ["#2563EB", "#0F766E", "#7C3AED", "#D97706", "#DC2626", "#15803D"]
+ for index, entry in enumerate(trace):
+ color = colors[index % len(colors)]
+ ax.scatter(index, 0, s=420, color=color, edgecolor="white", linewidth=2, zorder=2)
+ ax.text(
+ index,
+ 0,
+ str(index + 1),
+ ha="center",
+ va="center",
+ color="white",
+ weight="bold",
+ zorder=3,
+ )
+ direction = 1 if index % 2 == 0 else -1
+ name = str(entry.get("tool_name") or "-")
+ wrapped_name = textwrap.fill(name, width=22, break_long_words=True)
+ action = TRACE_LABELS.get(name, "执行领域函数")
+ y_action = 0.42 * direction
+ y_name = 0.62 * direction
+ va = "bottom" if direction > 0 else "top"
+ ax.text(
+ index,
+ y_action,
+ action,
+ ha="center",
+ va=va,
+ fontsize=9.4,
+ weight="bold",
+ color="#17324D",
+ )
+ ax.text(
+ index,
+ y_name,
+ wrapped_name,
+ ha="center",
+ va=va,
+ fontsize=7.2,
+ color="#64748B",
+ )
+ duration_y = -0.12 if direction > 0 else 0.12
+ ax.text(
+ index,
+ duration_y,
+ _duration(entry.get("duration_s")),
+ ha="center",
+ va="center",
+ fontsize=8,
+ color="#334155",
+ )
+ ax.set_title(
+ "Gemma 4 + Google ADK 原生函数调用轨迹",
+ loc="left",
+ fontsize=17,
+ weight="bold",
+ color="#17324D",
+ )
+ ax.text(
+ 0,
+ 0.91,
+ "模型负责观察、选择下一工具与受控闭环；确定性代码负责规划计算和硬约束裁决",
+ transform=ax.transAxes,
+ fontsize=9.5,
+ color="#64748B",
+ )
+ ax.set_axis_off()
+ fig.tight_layout()
+ fig.savefig(output_path, dpi=190, bbox_inches="tight", facecolor=fig.get_facecolor())
+ plt.close(fig)
+ return True
+
+
+def _set_cell_text(
+ cell,
+ text: Any,
+ *,
+ bold: bool = False,
+ color: str = "334155",
+ size: float = 9.5,
+ align=WD_ALIGN_PARAGRAPH.LEFT,
+) -> None:
+ cell.text = ""
+ cell.vertical_alignment = WD_CELL_VERTICAL_ALIGNMENT.CENTER
+ paragraph = cell.paragraphs[0]
+ paragraph.alignment = align
+ paragraph.paragraph_format.space_after = Pt(0)
+ run = paragraph.add_run(str(text))
+ run.bold = bold
+ run.font.size = Pt(size)
+ run.font.color.rgb = RGBColor.from_string(color)
+ run.font.name = "Microsoft YaHei"
+ run._element.rPr.get_or_add_rFonts().set(qn("w:eastAsia"), "Microsoft YaHei")
+
+
+def _set_table_borders(table, color: str = "D7E2EA", size: str = "4") -> None:
+ table_properties = table._tbl.tblPr
+ borders = table_properties.first_child_found_in("w:tblBorders")
+ if borders is None:
+ borders = OxmlElement("w:tblBorders")
+ table_properties.append(borders)
+ for edge in ("top", "left", "bottom", "right", "insideH", "insideV"):
+ tag = "w:" + edge
+ element = borders.find(qn(tag))
+ if element is None:
+ element = OxmlElement(tag)
+ borders.append(element)
+ element.set(qn("w:val"), "single")
+ element.set(qn("w:sz"), size)
+ element.set(qn("w:color"), color)
+
+
+def _table(doc: Document, headers: list[str], rows: list[list[Any]], widths=None):
+ table = doc.add_table(rows=1, cols=len(headers))
+ table.alignment = WD_TABLE_ALIGNMENT.CENTER
+ table.autofit = False
+ _set_table_borders(table)
+ for index, header in enumerate(headers):
+ cell = table.rows[0].cells[index]
+ _set_cell_background(cell, NAVY)
+ _set_cell_text(cell, header, bold=True, color=WHITE, size=9)
+ if widths:
+ cell.width = Cm(widths[index])
+ for row_index, values in enumerate(rows):
+ cells = table.add_row().cells
+ for index, value in enumerate(values):
+ if row_index % 2:
+ _set_cell_background(cells[index], "F8FAFC")
+ _set_cell_text(cells[index], value, size=8.8)
+ if widths:
+ cells[index].width = Cm(widths[index])
+ doc.add_paragraph().paragraph_format.space_after = Pt(0)
+ return table
+
+
+def _heading(doc: Document, text: str, level: int = 1) -> None:
+ paragraph = doc.add_heading(text, level=level)
+ paragraph.paragraph_format.space_before = Pt(4)
+ paragraph.paragraph_format.space_after = Pt(8)
+
+
+def _paragraph(doc: Document, text: str, *, color: str = "334155", size: float = 9.5):
+ paragraph = doc.add_paragraph()
+ paragraph.paragraph_format.space_after = Pt(5)
+ paragraph.paragraph_format.line_spacing = 1.15
+ run = paragraph.add_run(text)
+ run.font.size = Pt(size)
+ run.font.color.rgb = RGBColor.from_string(color)
+ return paragraph
+
+
+def _add_cover(
+ doc: Document,
+ *,
+ dataset: str,
+ audit_passed: bool,
+ committed: bool,
+ author: str,
+ episode_id: str,
+) -> None:
+ doc.add_paragraph().paragraph_format.space_after = Pt(18)
+ table = doc.add_table(rows=1, cols=1)
+ table.alignment = WD_TABLE_ALIGNMENT.CENTER
+ cell = table.cell(0, 0)
+ _set_cell_background(cell, NAVY)
+ cell.vertical_alignment = WD_CELL_VERTICAL_ALIGNMENT.CENTER
+ cell.margin_top = Cm(1.2)
+ cell.margin_bottom = Cm(1.2)
+ p = cell.paragraphs[0]
+ p.alignment = WD_ALIGN_PARAGRAPH.CENTER
+ p.paragraph_format.space_before = Pt(34)
+ p.paragraph_format.space_after = Pt(18)
+ run = p.add_run(REPORT_TITLE)
+ run.bold = True
+ run.font.size = Pt(25)
+ run.font.color.rgb = RGBColor.from_string(WHITE)
+ p = cell.add_paragraph(PUBLIC_AGENT_NAME)
+ p.alignment = WD_ALIGN_PARAGRAPH.CENTER
+ p.runs[0].font.size = Pt(13)
+ p.runs[0].font.color.rgb = RGBColor.from_string("BFD7EA")
+ p.paragraph_format.space_after = Pt(36)
+
+ status_ok = audit_passed and committed
+ status_text = (
+ "审计通过，已写入已验证经验库"
+ if status_ok
+ else ("审计通过，尚未写入经验库" if audit_passed else "审计未通过，结果未提交")
+ )
+ banner = doc.add_table(rows=1, cols=1)
+ banner.alignment = WD_TABLE_ALIGNMENT.CENTER
+ status_cell = banner.cell(0, 0)
+ _set_cell_background(status_cell, LIGHT_GREEN if status_ok else LIGHT_RED)
+ _set_cell_text(
+ status_cell,
+ status_text,
+ bold=True,
+ color=GREEN if status_ok else RED,
+ size=12,
+ align=WD_ALIGN_PARAGRAPH.CENTER,
+ )
+ doc.add_paragraph().paragraph_format.space_after = Pt(8)
+
+ meta_rows = [
+ ["规划范围", dataset, "报告生成", datetime.now().strftime("%Y-%m-%d %H:%M")],
+ ["生成主体", author or "GIS Data Agent", "经验编号", episode_id or "-"],
+ ]
+ _table(doc, ["项目", "内容", "项目", "内容"], meta_rows, [2.4, 4.7, 2.4, 4.7])
+ _paragraph(
+ doc,
+ "本报告由运行产物自动生成。图件、指标、函数耗时和审计结论均绑定本次执行记录，可用于方案复核与演示留档。",
+ color=GRAY,
+ size=9,
+ )
+ doc.add_page_break()
+
+
+def _add_picture(doc: Document, path: Path, width_cm: float, caption: str) -> None:
+ paragraph = doc.add_paragraph()
+ paragraph.alignment = WD_ALIGN_PARAGRAPH.CENTER
+ paragraph.paragraph_format.space_after = Pt(3)
+ paragraph.add_run().add_picture(str(path), width=Cm(width_cm))
+ caption_p = doc.add_paragraph()
+ caption_p.alignment = WD_ALIGN_PARAGRAPH.CENTER
+ caption_p.paragraph_format.space_after = Pt(7)
+ caption_run = caption_p.add_run(caption)
+ caption_run.font.size = Pt(8.5)
+ caption_run.font.color.rgb = RGBColor.from_string(GRAY)
+
+
+def _audit_rows(audit: dict[str, Any], metrics: dict[str, Any]) -> list[list[str]]:
+ policy = audit.get("policy") or {}
+ cultivated = _as_float(metrics.get("cultivated_area_change_ha"))
+ cultivated_floor = _as_float(policy.get("cultivated_area_floor_delta_ha"))
+ slope = _as_float(metrics.get("slope_change_pct"))
+ contiguity = _as_float(metrics.get("cont_change"))
+ outputs = bool(audit.get("all_expected_outputs_exist"))
+
+ def verdict(condition: bool) -> str:
+ return "通过" if condition else "未通过"
+
+ return [
+ [
+ "耕地面积不减少",
+ f">= {_fmt(cultivated_floor, 4)} ha",
+ f"{_fmt(cultivated, 4, True)} ha",
+ verdict(
+ cultivated is not None
+ and cultivated_floor is not None
+ and cultivated >= cultivated_floor
+ ),
+ ],
+ [
+ "平均坡度改善",
+ "变化值 < 0",
+ f"{_fmt(slope, 4, True)}%",
+ verdict(slope is not None and slope < 0),
+ ],
+ [
+ "空间连片度改善",
+ "变化值 > 0",
+ _fmt(contiguity, 4, True),
+ verdict(contiguity is not None and contiguity > 0),
+ ],
+ ["成果文件完整", "摘要 + 空间结果", "完整" if outputs else "缺失", verdict(outputs)],
+ ]
+
+
+def _build_document(
+ context: dict[str, Any],
+ *,
+ author: str,
+ asset_dir: Path,
+) -> Document:
+ result, plan = _plan_result(context)
+ audit = context.get("audit_result") or {}
+ commit = context.get("commit_result") or {}
+ status = context.get("status_result") or {}
+ args = context.get("tool_args") or {}
+ trace = _normalized_trace(context)
+ run_dir = _run_dir(plan, audit)
+ summary_path = _artifact_path(run_dir, plan, "mpc_summary.json")
+ raw_summary = _load_json(summary_path)
+ normalized = plan.get("summary") or {}
+ metrics = _metric_values(normalized, raw_summary)
+ layer_path = _artifact_path(
+ run_dir,
+ plan,
+ "optimized_dltb.fgb",
+ "optimized_dltb.shp",
+ "DLTB_optimized.shp",
+ )
+
+ audit_passed = bool(audit.get("hard_constraint_passed"))
+ committed = commit.get("status") == "committed"
+ episode = commit.get("episode") or {}
+ episode_id = str(episode.get("episode_id") or "-")
+ dataset = _dataset_label(args.get("dataset") or context.get("dataset"))
+
+ kpi_path = asset_dir / "planning_kpis.png"
+ map_path = asset_dir / "planning_map.png"
+ trace_path = asset_dir / "agent_trace.png"
+ _draw_kpi_dashboard(metrics, kpi_path)
+ map_ready = _draw_planning_map(layer_path, metrics, map_path)
+ trace_ready = _draw_agent_trace(trace, trace_path)
+
+ doc = Document()
+ _setup_styles(doc)
+ _setup_page(doc)
+ doc.core_properties.title = REPORT_TITLE
+ doc.core_properties.author = author or "GIS Data Agent"
+ doc.core_properties.subject = "县域耕地空间规划、硬约束审计与已验证经验"
+
+ _add_cover(
+ doc,
+ dataset=dataset,
+ audit_passed=audit_passed,
+ committed=committed,
+ author=author,
+ episode_id=episode_id,
+ )
+
+ _heading(doc, "1. 执行摘要")
+ outcome = (
+ "规划方案通过硬约束校验，并在审计通过后写入已验证经验库。"
+ if audit_passed and committed
+ else "规划结果未完成审计与经验提交闭环，不应作为已验证方案使用。"
+ )
+ _paragraph(doc, outcome, color=GREEN if audit_passed and committed else RED, size=10.5)
+ _add_picture(doc, kpi_path, 14.5, "图 1 本次运行关键规划指标")
+ planning_seconds = sum(
+ _as_float(entry.get("duration_s")) or 0.0
+ for entry in trace
+ if entry.get("tool_name") in {"world_model_v21_pipeline", "world_model_v21_plan"}
+ )
+ governance_seconds = sum(
+ _as_float(entry.get("duration_s")) or 0.0
+ for entry in trace
+ if entry.get("tool_name") in {"paper9_audit_run", "paper9_commit_verified_episode"}
+ )
+ overview_rows = [
+ [
+ "数据集",
+ dataset,
+ "环境",
+ "县域" if plan.get("env_kind") == "county" else str(plan.get("env_kind") or "-"),
+ ],
+ ["规划引擎", PUBLIC_ENGINE_NAME, "算法", "学习型状态转移模型集成 + MPC"],
+ [
+ "MPC 前瞻步长",
+ _fmt(args.get("horizon") or metrics.get("horizon"), 0),
+ "每步候选行动数",
+ _fmt(args.get("top_k") or metrics.get("top_k"), 0),
+ ],
+ [
+ "总用时",
+ _duration(context.get("total_duration_s")),
+ "MPC 规划用时",
+ _duration(planning_seconds),
+ ],
+ ["原生函数调用", f"{len(trace)} 次", "审计与经验提交", _duration(governance_seconds)],
+ ]
+ _table(doc, ["项目", "数值", "项目", "数值"], overview_rows, [2.5, 4.5, 2.5, 4.5])
+ doc.add_page_break()
+
+ _heading(doc, "2. 空间规划结果")
+ _paragraph(
+ doc,
+ "图件直接读取本次运行的优化后空间图层。灰色为保持不变，红色为耕地转林地，绿色为林地转耕地。双向置换在县域尺度上保持耕地总量门槛，同时优化坡度与连片度。",
+ )
+ if map_ready:
+ _add_picture(doc, map_path, 14.5, "图 2 县域耕地空间布局优化变化图")
+ else:
+ warning = doc.add_table(rows=1, cols=1).cell(0, 0)
+ _set_cell_background(warning, LIGHT_RED)
+ _set_cell_text(
+ warning,
+ "空间图层未能渲染，报告保留指标和审计证据，请复核原始交付物。",
+ color=RED,
+ bold=True,
+ )
+ transition_rows = [
+ [
+ "输入记录",
+ _fmt(metrics.get("n_input"), 0),
+ "进入规划环境的图斑",
+ _fmt(metrics.get("n_parcels"), 0),
+ ],
+ [
+ "耕地 -> 林地",
+ _fmt(metrics.get("n_farm_to_forest"), 0),
+ "林地 -> 耕地",
+ _fmt(metrics.get("n_forest_to_farm"), 0),
+ ],
+ [
+ "保持不变",
+ _fmt(metrics.get("n_unchanged"), 0),
+ "完成双向置换",
+ f"{_fmt(metrics.get('swaps_completed'), 0)} 对",
+ ],
+ ]
+ _table(doc, ["统计项", "数量", "统计项", "数量"], transition_rows, [3.1, 3.9, 3.1, 3.9])
+ doc.add_page_break()
+
+ _heading(doc, "3. Gemma 4 + Google ADK 受控自主闭环")
+ _paragraph(
+ doc,
+ "Gemma 4 根据版本、资源、历史经验与审计反馈选择下一工具；"
+ "Google ADK 执行结构化原生函数调用；确定性规划引擎完成空间搜索；"
+ "硬约束校验决定是否允许经验写入。",
+ )
+ if trace_ready:
+ _add_picture(doc, trace_path, 14.5, "图 3 本次运行的原生函数调用顺序与真实耗时")
+ trace_rows = []
+ for index, entry in enumerate(trace, start=1):
+ name = str(entry.get("tool_name") or "-")
+ trace_rows.append(
+ [
+ index,
+ TRACE_LABELS.get(name, "执行领域函数"),
+ name,
+ _duration(entry.get("duration_s")),
+ ]
+ )
+ if trace_rows:
+ _table(doc, ["序号", "观察/决策", "原生函数", "用时"], trace_rows, [1.2, 4.4, 6.2, 2.2])
+
+ stage_labels = {
+ "prepare": "A / 数据准备",
+ "sample": "B / 样本生成",
+ "train": "C / 状态转移模型训练",
+ "plan": "D / MPC 规划执行",
+ }
+ stage_status = {"skipped_reused": "已复用", "ok": "完成", "ready": "就绪"}
+ stage_rows = []
+ for step in result.get("steps") or []:
+ if not isinstance(step, dict):
+ continue
+ key = str(step.get("step") or "-")
+ value = str(step.get("status") or step.get("mode") or "-")
+ stage_rows.append([stage_labels.get(key, key), stage_status.get(value, value)])
+ if stage_rows:
+ _heading(doc, "算法执行阶段", level=2)
+ _table(doc, ["阶段", "本次状态"], stage_rows, [9.5, 4.5])
+ doc.add_page_break()
+
+ _heading(doc, "4. 硬约束审计与经验治理")
+ audit_banner = doc.add_table(rows=1, cols=1).cell(0, 0)
+ _set_cell_background(audit_banner, LIGHT_GREEN if audit_passed else LIGHT_RED)
+ _set_cell_text(
+ audit_banner,
+ f"硬约束审计：{'通过' if audit_passed else '未通过'}",
+ color=GREEN if audit_passed else RED,
+ bold=True,
+ size=12,
+ align=WD_ALIGN_PARAGRAPH.CENTER,
+ )
+ doc.add_paragraph().paragraph_format.space_after = Pt(5)
+ _table(
+ doc,
+ ["审计项", "约束", "实际结果", "结论"],
+ _audit_rows(audit, metrics),
+ [4.3, 3.3, 3.8, 2.6],
+ )
+
+ memory_text = "已写入" if committed else "未写入"
+ memory_rows = [
+ ["已验证经验库", memory_text],
+ ["经验编号", episode_id],
+ ["写入规则", "仅当硬约束审计通过时允许写入"],
+ ]
+ _heading(doc, "经验写入", level=2)
+ _table(doc, ["项目", "结果"], memory_rows, [4.1, 9.9])
+
+ engine = status.get("paper9") or {}
+ summary_artifact = (audit.get("artifacts") or {}).get("summary") or {}
+ checksum = str(summary_artifact.get("sha256") or "-")
+ deliverable_rows = [
+ [
+ "规划汇总",
+ "JSON",
+ "关键指标与运行参数",
+ checksum[:16] + ("..." if len(checksum) > 16 else ""),
+ ],
+ [
+ "优化空间结果",
+ "FlatGeobuf / Shapefile",
+ "地图复核与 GIS 交付",
+ "已生成" if layer_path else "缺失",
+ ],
+ ["硬约束审计记录", "JSON", "校验结论与失败原因", "已生成" if audit else "缺失"],
+ ["已验证经验", "Append-only 记录", "后续任务受控召回", memory_text],
+ ]
+ _heading(doc, "可追溯交付", level=2)
+ _table(doc, ["交付物", "格式", "用途", "状态/摘要哈希"], deliverable_rows, [3.4, 3.2, 4.2, 3.2])
+ _paragraph(
+ doc,
+ (
+ "版本信息：适配器 "
+ f"{plan.get('version', result.get('version', '-'))}；"
+ f"引擎包 {engine.get('package_version', '-')}；"
+ f"算法 {engine.get('algorithm_version', '-')}。"
+ ),
+ color=GRAY,
+ size=8.5,
+ )
+ _paragraph(
+ doc,
+ "边界说明：本报告记录计算与工程审计结果，不替代法定规划审批、权属确认或生产数据验收。",
+ color=GRAY,
+ size=8.5,
+ )
+ return doc
+
+
+def generate_world_model_v21_word_report(
+ context: dict[str, Any],
+ output_path: str,
+ *,
+ author: str = "",
+) -> str:
+ """Generate a visual DOCX from one structured county-planning run."""
+ output = Path(output_path).expanduser().resolve()
+ output.parent.mkdir(parents=True, exist_ok=True)
+ with tempfile.TemporaryDirectory(prefix="wm_v21_report_") as tmp:
+ doc = _build_document(context, author=author, asset_dir=Path(tmp))
+ doc.save(output)
+ return str(output)
+
+
+def generate_world_model_v21_pdf_report(
+ context: dict[str, Any],
+ output_path: str,
+ *,
+ author: str = "",
+) -> str:
+ """Generate a visual PDF from one structured county-planning run."""
+ output = Path(output_path).expanduser().resolve()
+ docx_path = output.with_suffix(".docx")
+ generate_world_model_v21_word_report(context, str(docx_path), author=author)
+ return convert_word_to_pdf(str(docx_path), str(output), remove_source=True)
diff --git a/docker-compose.gemma4-demo.yml b/docker-compose.gemma4-demo.yml
index 9559b415..b6e03275 100644
--- a/docker-compose.gemma4-demo.yml
+++ b/docker-compose.gemma4-demo.yml
@@ -97,9 +97,9 @@ services:
DYNAMIC_PLANNER: "true"
volumes:
- gemma4_demo_uploads:/app/data_agent/uploads
- - /Users/zhouning/arcgis-farmland-mpc:/app/paper9-demo:ro
- - /Users/zhouning/farmland_mpc_runs/bishan:/app/bishan-runs:ro
- - /Users/zhouning/arcgis-farmland-mpc/runs/dongxing:/app/dongxing-runs:ro
+ - ${PAPER9_HOST_REPO:-./vendor/paper9-mnr-offline-package}:/app/paper9-demo:ro
+ - ${PAPER9_BISHAN_RUNS_HOST:-./demo-data/bishan}:/app/bishan-runs:ro
+ - ${PAPER9_DONGXING_RUNS_HOST:-./demo-data/dongxing}:/app/dongxing-runs:ro
networks:
- gemma4-demo-net
diff --git a/docs/finals/GIS_Data_Agent_Gemma4_Finals_CN.pdf b/docs/finals/GIS_Data_Agent_Gemma4_Finals_CN.pdf
new file mode 100644
index 00000000..5853d925
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diff --git a/docs/finals/GIS_Data_Agent_Gemma4_Finals_CN.pptx b/docs/finals/GIS_Data_Agent_Gemma4_Finals_CN.pptx
new file mode 100644
index 00000000..228def31
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diff --git a/docs/finals/GIS_Data_Agent_Gemma4_Finals_Technical_Handbook_CN.docx b/docs/finals/GIS_Data_Agent_Gemma4_Finals_Technical_Handbook_CN.docx
new file mode 100644
index 00000000..f5ce4752
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diff --git a/docs/finals/GIS_Data_Agent_Gemma4_Finals_Technical_Handbook_CN.md b/docs/finals/GIS_Data_Agent_Gemma4_Finals_Technical_Handbook_CN.md
new file mode 100644
index 00000000..d9a107f0
--- /dev/null
+++ b/docs/finals/GIS_Data_Agent_Gemma4_Finals_Technical_Handbook_CN.md
@@ -0,0 +1,1028 @@
+---
+title: "GIS Data Agent 决赛技术答辩手册"
+subtitle: "Gemma 4 + Google ADK + GeoSQL + GWM/MPC"
+author: "周宁"
+date: "2026-07-30"
+lang: zh-CN
+---
+
+> 文档定位：本手册是决赛现场的技术查询底稿，不替代 5 分钟 PPT 与演示脚本。回答评委时先给结论，再给一个可核验的实现或数字，最后主动说明边界。所有运行数字以 2026-07-30 决赛证据快照为准。
+
+# 1. 现场使用方法
+
+## 1.1 先记住这段 30 秒总答案
+
+GIS Data Agent 面向两类真实地理空间任务：高频的自然语言空间问数，以及低频但高价值的县域耕地空间布局优化。Gemma 4 26B 负责理解目标、生成 GeoSQL、选择工具和根据反馈决定下一步；Google ADK 承载 Agent 生命周期、原生函数调用和调用事件；PostGIS、GIS 与县域耕地空间优化引擎负责确定性计算；独立治理代码负责版本兼容性、硬约束校验和经验写入权限。系统的核心不是让大模型猜空间结果，而是让它在可审计边界内组织真实计算。
+
+县域优化部分同时展示了 `LLM + GWM` 的方向：图斑与空间块构成状态，空间块内的耕地与林地等量置换构成行动，学习型状态转移模型集成预测行动后果，MPC 使用预测搜索方案。当前实现应称为“领域化地理空间世界模型原型”，不是完整通用地理空间世界模型。
+
+## 1.2 高频追问快速索引
+
+| 评委问题 | 先回答什么 | 深入章节 | 建议搜索词 |
+|---|---|---:|---|
+| Gemma 4 到底做了什么？ | 决策与控制，不直接计算 GIS/MPC 数值 | 3、4 | `Gemma 4 职责` |
+| ADK 是主链还是装饰？ | 主运行时，承载 10 个结构化工具和函数事件 | 4 | `10 个工具` |
+| 多步规划在哪里？ | 任务级 2/6/8 工具分支；数值级 MPC 是另一层规划 | 4、6 | `两层规划` |
+| 为什么选 26B？ | 113/125，接近 31B，但完整基准快约 37.6% | 3 | `CQ-125` |
+| 和普通 NL2SQL 有什么区别？ | 专门处理 SRID、米制距离、空间谓词、去重与真实执行 | 5 | `GeoSQL` |
+| 结果为什么是 35？ | 同一 PostGIS 快照、锁定 SQL、三层地图相互校验 | 5.7 | `最长桥梁` |
+| Paper9 是什么？ | 县域优化引擎内部研发代号，主叙事不用它做产品名 | 6.1 | `内部代号` |
+| 世界模型体现在哪里？ | 学习型状态转移模型集成；MPC 是消费者，不是模型本身 | 6.2 | `GWM` |
+| 自主性体现在哪里？ | 观察、选择、审计后恢复或停止，并有代码强制边界 | 7 | `受控自主` |
+| Memory 如何避免污染？ | 只提交通过校验且产物完整的 episode，幂等追加写入 | 7.4 | `verified memory` |
+| 失败怎么办？ | 版本不兼容停止；审计失败最多重规划一次；再失败转人工 | 7.3 | `恢复分支` |
+| 30/30 证明了什么？ | 证明受控场景中的 Gemma 4 + ADK 编排行为，不是 30 次算法重跑 | 9.2 | `30/30` |
+| 自然资源部测试的是什么？ | 底层引擎历史版本的目标内网记录，不是当前 Agent 生产验收 | 12.9 | `内网证据` |
+| 如何扩展到其他地区？ | 数据契约、CRS、编码、区域 ensemble 和独立验收必须重做 | 11 | `区域扩展` |
+
+## 1.3 现场回答模板
+
+推荐使用固定三段式：
+
+1. **结论**：一句话直接回答，不先讲背景。
+2. **证据**：给一个函数名、工具轨迹、基准数字或空间产物。
+3. **边界**：说明这个证据不能外推为什么。
+
+例如：
+
+> 结论：Gemma 4 确实在做任务级规划，但不直接计算 MPC 数值。证据：真实 ADK 评测中，它根据工具反馈形成版本阻断、六工具成功和八工具恢复三种轨迹。边界：A/B/C/D 是确定性算法阶段，不能把固定 pipeline 说成大模型动态规划。
+
+# 2. 系统目标与技术边界
+
+## 2.1 真实用户与任务
+
+目标用户不是泛化的“政府用户”，而是以下具体岗位：
+
+- 自然资源调查与国土空间规划人员。
+- 县域耕地整治与用途管制人员。
+- GIS 数据分析师和空间数据库工程师。
+- 需要审查规划过程、指标和空间成果的技术负责人。
+
+系统选择两类任务，是为了同时证明广度与深度：
+
+| 场景 | 频率与价值 | 原有痛点 | 系统输出 |
+|---|---|---|---|
+| NL2Semantic2GeoSQL | 高频、刚需 | SQL 看似合理但空间单位、SRID、谓词或去重错误 | 可执行 SQL、标量结果、地图与修正记录 |
+| 县域耕地空间优化 | 低频、高价值 | 不能只给文字建议，必须满足业务约束并生成空间成果 | MPC 方案、指标、变化图层、审计与图文报告 |
+
+## 2.2 核心设计原则
+
+系统遵循四个原则：
+
+- **模型做决策，工具做计算**：Gemma 4 不伪造 PostGIS 结果或 MPC 指标。
+- **验证独立于模型**：面积、坡度、连片度和产物完整性由确定性 Python 代码校验。
+- **失败默认停止**：版本、资源、SQL 安全或空间产物不满足要求时，不能把 warning 包装为成功。
+- **经验必须带证据**：可复用经验必须关联版本、参数、输出目录、摘要哈希和审计结论。
+
+## 2.3 一张图理解总体架构
+
+{width=96%}
+
+图中从上到下依次是用户与路由、Gemma 4 决策面、Google ADK 运行时、两个确定性行动面，以及独立治理与证据面。这里最重要的职责边界是：模型不直接伪造 GIS 或 MPC 数值，工具输出也不能绕过独立校验直接进入经验库。
+
+## 2.4 Google 技术栈的准确口径
+
+| 技术 | 当前角色 | 是否进入主链 | 现场表述 |
+|---|---|---:|---|
+| Gemma 4 | GeoSQL 合成、任务级工具决策、结果解释 | 是 | 核心模型 |
+| Google ADK | Agent 运行时、10 个函数工具、长任务工具、session 与事件 | 是 | 核心框架 |
+| AlphaEarth | 长期空间表征扩展方向 | 否 | `Tech Preview` |
+| OKF | 人和 Agent 可读的知识交换 sidecar | 否 | 未来知识治理接口 |
+
+ADK 是真正的架构组成，不是额外加一个 Google Logo。AlphaEarth 与 OKF 只在被追问未来扩展时说明，不应暗示已经进入生产闭环，也不应主张“Google 技术用得越多越有隐藏加分”。
+
+# 3. Gemma 4 模型选型与部署
+
+## 3.1 CQ-125 模型选型基准
+
+模型选择基于同一主机、同一 CQ-125 PostGIS 基准和同一评测口径。`EX` 表示执行结果正确，不只是 SQL 字符串相似。
+
+| 模型 | Full EX | Full Valid | 完整时间 |
+|---|---:|---:|---:|
+| Gemma4:e2b | 82/125 = 65.6% | 108/125 = 86.4% | 21.22 min |
+| Gemma4:e4b | 80/125 = 64.0% | 110/125 = 88.0% | 13.49 min |
+| Gemma4:12b | 107/125 = 85.6% | 120/125 = 96.0% | 16.45 min |
+| **Gemma4:26b** | **113/125 = 90.4%** | **118/125 = 94.4%** | **12.50 min** |
+| Gemma4:31b | 114/125 = 91.2% | 117/125 = 93.6% | 20.04 min |
+
+26B 只比 31B 少 1 道执行正确题，即 0.8 个百分点，但完整运行时间从 20.04 分钟下降到 12.50 分钟，约快 37.6%。因此选择 26B 是准确率与时延的工程折中，不是声称 26B 在统计上显著优于 31B。
+
+{width=90%}
+
+数据源：`docs/assets/gemma4_host228_scale_sweep_summary.csv`。
+
+## 3.2 模型网关
+
+`data_agent/model_gateway.py` 将逻辑模型名映射到实际后端。决赛环境关键配置为：
+
+```text
+logical model: gemma4-26b-ollama
+backend: LiteLLM
+model_id: ollama_chat/Gemma4:26b
+api: Ollama OpenAI-compatible endpoint
+extra_body: {"think": false}
+request_timeout: 600 s
+```
+
+调用链为：
+
+```text
+业务 Agent
+-> create_model("gemma4-26b-ollama")
+-> Google ADK LiteLlm wrapper
+-> LiteLLM / Ollama
+-> Gemma4:26b
+```
+
+## 3.3 为什么固定 `think=false`
+
+本地 Gemma 4 的 reasoning 模式会把大量内容放入 `message.thinking`，在某些工具循环中直到输出预算耗尽才产生可用的 `message.content`，表现为长时间空响应或超时。决赛版本关闭 thinking 输出，原因不是降低模型能力，而是：
+
+- 工具参数必须稳定、短而结构化。
+- 不向用户界面泄露内部推理文本。
+- 避免 5 分钟演示中出现不可控的首 token 延迟。
+- 让可审计证据落在函数调用和工具结果上，而不是不可验证的思维文本上。
+
+## 3.4 离线与数据边界
+
+Gemma 4、PostGIS、规划引擎和主要运行组件可在本地网络或内网环境运行。模型调用成本在当前本地部署中记为 0，但这不等于计算成本为零。模型服务、ONNX 推理、PostGIS 和 PDF 生成仍消耗主机 CPU、内存和等待时间。
+
+# 4. Google ADK、原生函数调用与两层规划
+
+## 4.1 两层规划必须分开讲
+
+系统中存在两种性质不同的规划：
+
+| 层级 | 规划者 | 输入 | 输出 | 是否由 LLM 决定 |
+|---|---|---|---|---:|
+| 任务级规划 | Gemma 4 + ADK | 用户目标、版本、资源、经验、审计反馈 | 下一工具、停止、重规划或提交 | 是 |
+| 数值级规划 | MPC | 空间状态、可行动作、转移模型预测、约束 | 具体空间块行动序列 | 否，确定性算法 |
+
+{width=96%}
+
+这一区分是回答“Gemma 4 真的在规划吗”的关键。Gemma 4 的证据是不同反馈导致不同工具轨迹；MPC 的证据是它在学习型状态转移模型上进行候选 rollout 和累计回报比较。
+
+## 4.2 两个显式路由 Agent
+
+### `@NL2SQL`
+
+本地 Gemma/Ollama 路径使用 `DirectNL2SemanticSQLAgent`。它是一个确定性 ADK 外层：先发出 `run_nl2semantic2sql(user_question)` 的 `function_call` 事件，再调用高层工具并发出 `function_response`。高层工具内部仍调用 Gemma 4 生成 SQL。
+
+这样设计是为了避免本地模型在多个低层语义、数据库工具之间重复循环。它保留了可审计函数事件，同时把空间 SQL 的 grounding、安全和执行交给确定性 harness。
+
+准确口径：
+
+> `@NL2SQL` 的外层工具选择是固定的，高层工具内部的 SQL 合成由 Gemma 4 完成；它不是本项目多步自主规划的主要证据。
+
+### `@WorldModelV21`
+
+`MentionWorldModelV21` 是标准 Google ADK `LlmAgent`，模型为 Gemma 4，且只绑定 `WorldModelV21Toolset`。Gemma 4 根据每次结构化响应决定下一工具。这一链路才是比赛要求中多步规划、工具使用和原生函数调用的主证据。
+
+代码位置：`data_agent/agent.py::_build_mention_world_model_v21_agent`。
+
+## 4.3 10 个 ADK 工具
+
+`WorldModelV21Toolset` 暴露 5 个同步 `FunctionTool` 和 5 个 `LongRunningFunctionTool`。
+
+| 工具 | ADK 类型 | 作用 | 关键输出或决策 |
+|---|---|---|---|
+| `world_model_v21_status` | FunctionTool | 检查适配器、包、算法版本与能力 | `version_compatible` |
+| `paper9_inspect_resources` | FunctionTool | 检查 prepared、samples、ONNX 与编码契约 | `planning_ready`、可复用阶段 |
+| `paper9_recall_verified_episodes` | FunctionTool | 按数据集召回已验证经验 | 最近 episode 与参数 |
+| `paper9_audit_run` | FunctionTool | 校验指标和空间产物 | commit / replan / human review |
+| `paper9_commit_verified_episode` | FunctionTool | 写入已验证经验 | `episode_id`、幂等状态 |
+| `world_model_v21_prepare` | LongRunningFunctionTool | DLTB + DEM 数据准备 | prepared 目录与摘要 |
+| `world_model_v21_sample` | LongRunningFunctionTool | 采样 transition 与 pairwise 数据 | NPZ 样本 |
+| `world_model_v21_train` | LongRunningFunctionTool | 训练对比学习 ensemble 并导出 ONNX | 模型成员与训练摘要 |
+| `world_model_v21_plan` | LongRunningFunctionTool | 单独执行 Tool 4 MPC | 规划目录、指标、地图 |
+| `world_model_v21_pipeline` | LongRunningFunctionTool | 执行或复用 A/B/C/D | 四阶段状态与规划结果 |
+
+长任务使用 `asyncio.to_thread` 包装同步计算，避免阻塞 ADK 的异步事件循环。工具参数采用 string-friendly 形式，进入服务层后统一转换与范围校验。
+
+## 4.4 三类真实 Gemma 4 + ADK 轨迹
+
+{width=96%}
+
+### 版本不兼容，2 个工具后停止
+
+工具序列为 `world_model_v21_status` → `paper9_inspect_resources`，随后停止并报告缺口。图中的“停止”是终态，不计作第 3 个工具。
+
+这是“知道什么时候不行动”的自主性证据。Agent 不能自动降级为旧算法再称为成功。
+
+### 首次成功，6 个工具
+
+工具序列为 `world_model_v21_status` → `paper9_inspect_resources` → `paper9_recall_verified_episodes` → `world_model_v21_pipeline` → `paper9_audit_run` → `paper9_commit_verified_episode`。
+
+### 首次失败后恢复，8 个工具
+
+工具序列为 `world_model_v21_status` → `paper9_inspect_resources` → `paper9_recall_verified_episodes` → `world_model_v21_pipeline` → 首次 `paper9_audit_run` 失败 → `world_model_v21_plan` → 再次 `paper9_audit_run` 通过 → `paper9_commit_verified_episode`。
+
+三种受控场景各运行 10 次，均形成预期终态和精确工具轨迹。恢复分支只允许一次 `world_model_v21_plan`，第二次仍失败必须停止。
+
+## 4.5 Prompt 约束与代码约束的区别
+
+Prompt 负责告诉模型应该怎样做，例如先检查状态、首次规划带面积不减少约束、失败最多重规划一次。代码负责保证即使模型不遵守也不能越权，例如：
+
+- 没有 `paper9_agent_audit.json`，提交函数直接拒绝。
+- `hard_constraint_passed=false`，经验库直接抛出异常。
+- 缺少优化空间产物，审计强制改为失败并转人工。
+- 版本和区域模型维度不兼容，规划服务拒绝执行。
+
+因此系统的可靠性不是只依赖 prompt，而是“模型策略 + 工具接口 + 确定性不变量”三层共同实现。
+
+# 5. NL2Semantic2GeoSQL 关键实现
+
+## 5.1 为什么不是普通 Text-to-SQL
+
+空间 SQL 的主要难点不是语法，而是执行语义：
+
+- 地理坐标系下 `geometry` 的长度和距离单位可能是度，不是米。
+- 不同 SRID 的空间对象不能直接比较。
+- `ST_Intersects`、`ST_Within`、`ST_Contains` 的方向和边界语义不同。
+- 一对多空间连接会让计数膨胀，需要按业务实体主键去重。
+- KNN `<->`、半径过滤和 `LIMIT` 的组合容易产生“看似合理但答非所问”的 SQL。
+- 生产环境必须拒绝 DDL/DML、未 grounding 的表和无界危险查询。
+
+所以系统不是把 schema 全量塞给模型，而是构建专门的 GeoSQL harness。
+
+## 5.2 完整执行链
+
+{width=96%}
+
+执行链可以归纳为四段：语义与实时 schema grounding、Gemma 4 SQL 合成、确定性语义修正与安全校验、PostGIS 真实执行与证据输出。图中的失败策略同样是运行契约的一部分，不是仅用于展示的说明文字。
+
+关键实现文件：
+
+| 文件 | 责任 |
+|---|---|
+| `data_agent/semantic_layer.py` | 语义源与字段定义 |
+| `data_agent/nl2sql_grounding.py` | 候选表、live schema、few-shot 与 grounding prompt |
+| `data_agent/nl2sql_executor.py` | Gemma SQL 合成、重试、执行与结构化返回 |
+| `data_agent/nl2sql_semantic_rewrite.py` | 配置驱动的空间语义改写 |
+| `data_agent/sql_postprocessor.py` | SQL AST/规则后处理与大表限制 |
+| `data_agent/runtime_guards.py` | 占位 SQL、幻觉表与 allow-list 检查 |
+| `data_agent/toolsets/nl2sql_tools.py` | 安全数据库执行 |
+| `data_agent/nl2sql_presentation.py` | 中文结果和地图 handoff |
+
+## 5.3 Semantic grounding
+
+Grounding 层不会只返回表名。每个候选列可携带：
+
+- 物理列名和安全引用形式。
+- 业务别名和描述。
+- PostgreSQL 类型、geometry 标记和 SRID。
+- 单位与 semantic domain。
+- 枚举值、原始编码和显示值映射。
+- 是否为标识字段，用于 `COUNT(DISTINCT ...)`。
+
+如果 Gemma 生成的 SQL 引用了未进入初始 top-k 的真实表，系统会先检查它是否由问题或语义上下文支持。未 grounding 的表默认触发一次约束重试；再次出现则拒绝，不会因为数据库里恰好存在就自动视为正确。
+
+## 5.4 Gemma 4 SQL 合成
+
+`nl2sql_executor.py::_generate_gemma_sql` 通过 LiteLLM 调用配置的 Gemma 4：
+
+```text
+temperature = 0.0
+think = false
+retry attempts = 3（可配置）
+output contract = SQL only
+```
+
+SQL 合成是 Gemma 4 的核心贡献。Python harness 不预先写死最终 SQL，而是在生成后校正可判定的空间语义错误。
+
+## 5.5 空间语义改写
+
+`apply_semantic_sql_rewrites` 是配置驱动的规则链，主要覆盖：
+
+- 表名版本归一化与问题别名匹配。
+- 列别名、引号和大小写修正。
+- 未知列拒绝。
+- 枚举编码和显示值转换。
+- 面积单位转换到平方米、平方公里或公顷。
+- `ST_DWithin` 的 `geography` 米制距离修正。
+- `ST_Distance`、`ST_Length` 的坐标与单位修正。
+- SRID transform 和子查询 geometry 投影修正。
+- 空间连接去重与 grouped count 修正。
+- 不应出现的空间连接或过滤条件删除。
+
+这类改写只使用 semantic context，不在 Python 中为某个 demo 问题硬编码答案。修正标签会随结构化结果返回，例如当前演示出现 `semantic_column_alias`。
+
+## 5.6 安全与失败处理
+
+执行前至少经过四层控制：
+
+1. Prompt 要求只生成只读查询。
+2. SQL postprocessor 拒绝写操作和危险结构，并可为大表增加限制。
+3. Runtime guard 拒绝 `SELECT 1` 等放弃型占位 SQL、文件路径形态表名和 allow-list 外表名。
+4. 数据库工具使用只读执行路径，返回结构化错误而不是继续猜测结果。
+
+需要强调：运行成功只证明 SQL 可执行，不自动证明业务语义完全正确。因此自动沉淀的 reference query 仍应经过 benchmark 或人工复核后再晋升为高置信知识。这是当前知识循环需要继续加强的边界。
+
+## 5.7 决赛查询为何返回 35
+
+用户问题：
+
+```text
+统计距离道路网络中最长桥梁100米范围内的高德POI数量。
+```
+
+锁定参考 SQL：
+
+```sql
+WITH longest_bridge AS (
+ SELECT geometry
+ FROM cq_osm_roads_2021
+ WHERE bridge = 'T'
+ ORDER BY ST_Length(geometry::geography) DESC
+ LIMIT 1
+)
+SELECT COUNT(DISTINCT p."ID") AS poi_count
+FROM cq_amap_poi_2024 AS p
+CROSS JOIN longest_bridge AS lb
+WHERE ST_DWithin(
+ p.geometry::geography,
+ lb.geometry::geography,
+ 100
+);
+```
+
+技术要点：
+
+- `ST_Length(geometry::geography)` 按米比较桥梁长度。
+- `ST_DWithin(...::geography, ..., 100)` 明确 100 米半径。
+- `COUNT(DISTINCT p."ID")` 避免空间连接重复计数。
+- roads 与 POI 使用锁定的版本表，而不是模糊表名。
+
+最终验收连续 3 次返回 `35`，延迟为 `14.414 / 6.761 / 6.884 s`。右侧地图从同一 PostGIS 伴随查询快照加载：
+
+- 高德 POI：35 个。
+- 最长桥梁：1 条，`osm_id=708725252`，龙溪河大道，长度约 2167.57 米。
+- 100 米缓冲区：1 个。
+
+{width=75%}
+
+标量、SQL 与三层地图互相核查，比只显示“35”更有说服力。
+
+## 5.8 CQ-125 能证明什么
+
+CQ-125 用于比较同一环境中不同模型的执行正确率，并作为空间语义回归基准。它能证明模型选型有数据依据，也能发现 SRID、距离、面积、空间连接等典型失败模式。
+
+它不能证明：
+
+- 所有自然语言空间问题都能正确处理。
+- 对任意数据库 schema 都有同样准确率。
+- 113/125 与 114/125 之间存在统计显著差异。
+
+# 6. 县域耕地空间优化引擎与地理空间世界模型
+
+## 6.1 命名与思想来源
+
+`Paper9` 是底层算法项目的内部研发代号，源于作者的第 9 篇论文工作。面向评委和用户，主名称统一为“县域耕地空间优化引擎”。只有在解释研究来源、代码路径或版本时才说明内部代号。
+
+它不是为了给比赛临时拼接的脚本，而是一个可复现的 model-based spatial planning 工程，包括数据准备、转移采样、对比学习 ensemble、ONNX 导出、MPC、ArcGIS/QGIS/CLI 接口和独立验证产物。
+
+## 6.2 与 GWM 的严格关系
+
+可以用五个部件说明：
+
+| GWM 部件 | 当前实现 | 技术含义 |
+|---|---|---|
+| 状态 `S` | 2,640 个空间块的 17 维 block 特征 + 12 维县域全局特征 | 当前世界状态表示 |
+| 行动 `A` | 从可行空间块中选择一个 block，在内部执行耕地/林地 paired swaps | 受限行动空间 |
+| 动力学 `T_theta` | 3-member transition model ensemble | 预测下一状态与奖励，是 GWM 内核雏形 |
+| 规划器 | MPC | 消费模型预测、搜索行动，不是世界模型本身 |
+| 验收层 | 面积、坡度、连片度与产物完整性校验 | 决定方案能否保存和复用 |
+
+{width=96%}
+
+一句准确表述：
+
+> 当前实现是领域化 GWM 原型。学习型状态转移模型集成是动力学内核雏形，MPC 是模型的消费者，Gemma 4 与 ADK 是任务级控制面，硬约束校验是决策治理外环。
+
+## 6.3 状态表示
+
+县域环境把图斑聚合为空间块。每个 block 的 17 维特征包含：
+
+- 耕地与林地坡度统计。
+- 可交换耕地/林地面积。
+- 最优坡度改善潜力。
+- 已使用置换预算与可用置换比例。
+- block 紧凑度和面积。
+- 邻接 block 的投资比例与耕地面积。
+- 当前耕地面积和是否已投资。
+
+12 维县域全局特征包含：
+
+- 剩余预算与执行进度。
+- 全局平均坡度和坡度改善。
+- 连片度与连片度改善。
+- 百亩方数量和面积比例。
+- 已投资 blocks 比例。
+- 乡镇间投资熵、跨乡镇协调占位和最大乡镇占比。
+
+因此状态不是一张原始栅格，也不是 LLM 文本，而是服务于县域行动预测的结构化空间状态。
+
+## 6.4 行动与环境
+
+行动空间为 `Discrete(n_blocks)`，每一步选择一个具备可行置换条件的 block。环境在该 block 内选择高坡耕地转林地、低坡林地转耕地，并保持 paired swaps。当前锁定运行每一步最多执行若干对置换，100 个环境步骤完成 406 对双向置换。
+
+面积不减少不只在事后检查。首次 planning 参数包含 `cultivated_area_floor_delta_ha=0`，环境的 action mask 会排除无法满足面积下限的候选行动。事后审计再独立复核最终指标，形成规划内约束与结果外审计两层保护。
+
+## 6.5 状态转移模型结构
+
+每个 ensemble member 是约 237K 参数的前馈 transition network：
+
+{width=96%}
+
+共享 block encoder 同时提供 selected-block 编码和全体 blocks 的 mean pooling；它们与 action embedding、global encoding 拼接为 128 维 context，再进入三个输出头。图底部的 ensemble 表示三个独立完整成员，而不是只对 reward head 做集成。
+
+模型采用 residual prediction：
+
+```text
+next_selected_block = selected_block + predicted_block_delta
+next_global_state = global_state + predicted_global_delta
+predicted_reward = reward_head(context)
+```
+
+每个成员导出为 ONNX，由 ONNX Runtime 在 CPU 上批量推理。当前决赛资源包含 3 个成员。
+
+重要扩展边界：`n_blocks` 会静态写入 ONNX 图和 action embedding，因此区域变化后不能直接复用其他区域的 ensemble。服务层通过 `assert_compatible` 与资源检查提前拒绝维度不匹配。
+
+## 6.6 为什么使用对比/排序损失
+
+普通 MSE 更关注预测值的绝对误差，但 MPC 更关心同一状态下“哪个行动更好”。Tool 2 除 transition tuple 外，还采集同一状态下不同动作的 pairwise 样本。
+
+训练目标可概括为：
+
+```text
+L_total = L_state_MSE + 0.1 * L_reward_MSE + lambda_rank * L_rank
+
+L_rank = max(0, margin - (R_hat(s, a_better) - R_hat(s, a_worse)))
+```
+
+当前默认 `lambda_rank=5.0`、`margin=0.1`。3 个成员使用不同随机种子训练，ensemble 同时提高稳定性，并可提供成员间预测差异作为不确定性信号。当前主 Agent 还没有把 ensemble 方差完整纳入自主风险决策，这是后续 GWM 完善方向。
+
+## 6.7 MPC 如何选择行动
+
+每个环境步骤的算法过程为：
+
+1. 获取当前 block features、global features 和 action mask。
+2. 对所有有效 actions 做一次 ensemble 批量预测。
+3. 保留全局 top-k 候选及其下一状态。
+4. 对每个候选继续向前 rollout `H-1` 步，continuation 可选 random 或 greedy。
+5. 计算折扣累计预测回报。
+6. 选择累计预测回报最高的第一个行动。
+7. 在真实县域环境中执行该行动，得到真实 reward 与下一状态。
+8. 重复直到预算或最大步骤结束。
+
+概念公式：
+
+```text
+a_t* = argmax_a sum(k=0..H-1) gamma^k * R_hat(s_t+k, a_t+k)
+```
+
+决赛快速路径使用 `horizon=1`、`top_k=1`、`n_episodes=1`。这里 `horizon=1` 是每次 MPC 的前瞻步长，`steps_run=100` 是环境实际执行步数，二者不能混为一谈。
+
+## 6.8 A/B/C/D 工具链
+
+| 阶段 | 输入 | 处理 | 输出 | 决赛运行 |
+|---|---|---|---|---|
+| A / Prepare | DLTB、DEM、行政区等 | 坡度、图斑与 blocks 准备 | prepared data | 复用 |
+| B / Sample | prepared environment | 随机 transition + pairwise action 采样 | `transitions.npz`、`pairwise.npz` | 复用 |
+| C / Train | transition/pairwise samples | 3-member contrastive ensemble 训练 | ONNX members | 复用 |
+| D / Plan | prepared + ONNX ensemble | MPC 搜索与真实环境执行 | 指标、数组、矢量成果 | 真实执行 |
+
+决赛中 A/B/C 显示“已复用”不是没执行过，而是地区级一次性装备已存在；现场真实执行的是 D/Tool 4。技术报告必须明确：当前 v2.2.3 本机证据不是 A/B/C/D 全量重训。
+
+## 6.9 锁定运行结果
+
+当前 0.3.3 / 2.2.3 锁定证据：
+
+| 指标 | 数值 |
+|---|---:|
+| 输入空间记录 | 101,657 |
+| 进入县域环境图斑 | 53,004 |
+| 空间 blocks | 2,640 |
+| ONNX ensemble members | 3 |
+| 环境步骤 | 100 |
+| 完成置换对 | 406 |
+| 耕地转林地 | 406 |
+| 林地转耕地 | 406 |
+| 耕地面积变化 | +1.9359359262 ha |
+| 平均坡度变化 | -0.6077654973% |
+| 连片度变化 | +0.0201841360 |
+| 百亩方面积变化 | +35.1316192716 ha |
+| 总回报 | 37.4093785660 |
+
+硬约束校验结果：面积未减少、坡度下降、连片度提升、优化空间产物存在，允许写入已验证经验。
+
+{width=43%}
+
+地图按 `CHG_FLAG` 分类：灰色保持不变，红色耕地转林地，绿色林地转耕地。
+
+## 6.10 输出产物
+
+Tool 4 至少产生：
+
+- `mpc_summary.json`：参数、ensemble、逐 episode 指标和 shapefile 汇总。
+- `mpc_land_use.npy`：最终地类状态。
+- `optimized_dltb.shp`：优化后矢量图斑。
+- `optimized_dltb.fgb`：便于 Web 地图加载的 FlatGeobuf。
+- `paper9_agent_audit.json`：审计策略、结论、失败原因、下一动作和摘要 SHA-256。
+
+聊天、地图、经验和 PDF 报告都应来自同一个运行目录，不从模型文字反推指标。
+
+## 6.11 为什么当前还不是完整 GWM
+
+当前原型仍缺：
+
+- 多尺度状态表示，例如图斑、街区、县域、流域之间的统一层级。
+- 多类型行动，而不只是 block 选择和两类地类置换。
+- 长期 rollout 与跨时间尺度动力学。
+- 完整的不确定性传播和风险敏感规划。
+- 观测同化、因果校准与现实反馈闭环。
+- 跨区域独立验证和可迁移的 action representation。
+
+因此最有技术含量的表述不是“我们已经完成通用 GWM”，而是“我们已经把状态、行动、学习型动力学、规划器和审核外环在一个高价值地理空间任务中跑通”。
+
+# 7. 受控自主、记忆与知识循环
+
+## 7.1 自主性定义
+
+自主性不是工具数量，也不是无限循环。当前系统把自主性定义为：
+
+Agent 观察结构化状态，选择受支持行动，获取真实工具反馈，再判断继续、恢复、提交或停止，并为每个终态记录可复核证据。
+
+自主范围被限定在当前数据集、10 个 ADK 工具、一次重规划和确定性业务约束内。
+
+## 7.2 决策状态机
+
+{width=94%}
+
+状态机中只有首次普通审核失败可以进入一次重规划；缺少空间产物直接转人工，第二次审核失败也必须停止。提交函数还会再次检查审核文件，因此图中的分支不是只依赖 prompt 的建议。
+
+## 7.3 硬约束校验
+
+`Paper9AuditPolicy` 默认规则：
+
+```text
+cultivated_area_change_ha >= 0
+slope_change_pct < 0
+cont_change > 0
+optimized spatial artifact exists
+max_replans = 1
+```
+
+审计返回机器可执行的 `next_action`：
+
+- `commit_verified_episode`
+- `replan_once`
+- `stop_and_request_human_review`
+
+如果 `mpc_summary.json`、记录字段或空间产物缺失，系统不会让 LLM 自行解释为成功。
+
+## 7.4 Verified Episodic Memory
+
+经验库不是聊天记录。每条记录包含：
+
+- `schema_version`
+- `episode_id`
+- 数据集和用户目标
+- 真实 `out_dir`
+- 规划参数
+- 指标与审计策略
+- 适配器、包、算法和代码版本 provenance
+- `mpc_summary.json` 的 SHA-256
+
+`episode_id` 由稳定字段哈希生成，同一运行重复提交保持幂等。文件采用 append-only JSONL，写入后执行 `flush + fsync`。提交前代码强制检查：
+
+```text
+hard_constraint_passed == true
+all_expected_outputs_exist == true
+```
+
+下一任务按 dataset 召回最近已验证 episode，用于提供有证据的参数和历史上下文。
+
+## 7.5 多层记忆与知识的分工
+
+| 层 | 机制 | 生命周期 | 可影响什么 |
+|---|---|---|---|
+| 即时状态 | ADK `output_key` / session state | 单次运行 | Agent 间结果传递 |
+| 会话记录 | Chainlit Thread / Step | 单会话或历史会话 | 用户可见上下文 |
+| 跨会话记忆 | Postgres memory service | 长期 | 历史片段召回 |
+| 领域经验 | Verified episodic memory | 长期、只追加 | 规划参数与成功证据 |
+| 语义知识 | Semantic registry、reference queries、ContextEngine | 版本化 | schema、术语、few-shot |
+
+记忆回答“过去发生了什么”，知识回答“领域中什么定义和规则有效”。两者不能混成一个无限增长的向量库。
+
+## 7.6 知识更新闭环的当前实现与缺口
+
+当前已实现：
+
+- GeoSQL 执行成功后可写入 reference query store，并做相似度去重。
+- 规划结果只有通过审计才能进入 verified episodic memory。
+- 召回结果按数据集隔离，并保留版本与摘要哈希。
+
+仍需加强：
+
+- “SQL 可执行”不等于“语义正确”，reference query 的高置信晋升应增加 benchmark 或人工审核。
+- ensemble 不确定性尚未自动触发风险升级。
+- 高风险耕地规则还不能由 Agent 自动改写或激活。
+- 知识过期、冲突和来源优先级需要统一的版本治理。
+
+目标知识循环应是：
+
+{width=96%}
+
+这比“让 Agent 自动学习一切”更符合自然资源高风险业务。
+
+# 8. 可观测性、地图与报告
+
+## 8.1 函数调用轨迹
+
+ADK 事件被应用层捕获，形成按实际发生顺序排列的 `function_call` 与 `function_response`。World Model 展示层把每一步转换为：
+
+- 观察/决策名称。
+- 结果状态。
+- 内部函数标识。
+- 真实单步用时。
+
+总用时包含模型推理、ADK 编排、工具执行和 UI 处理，因此应不小于各工具用时之和。小于 0.1 秒的快速工具显示三位小数，避免全部显示为 `0.0 秒`。
+
+## 8.2 地图证据
+
+地图不是装饰：
+
+- NL2SQL 地图由同一 PostGIS 快照的伴随查询产生，图层要素数与标量答案一致。
+- 县域规划地图来自本次 `optimized_dltb` 产物，按 `CHG_FLAG` 渲染。
+- 地图为空、图层数不一致或读取了其他运行目录，都应视为演示失败。
+
+## 8.3 图文 PDF 报告
+
+县域规划导出的 `County_Farmland_Planning_Report.pdf` 由 `data_agent/world_model_v21_report.py` 生成，数据来自同一次工具轨迹和运行产物。报告约 5 页，包含：
+
+- 规划成效看板。
+- 县域变化地图。
+- 六步原生函数调用轨迹。
+- 逐函数用时和总用时。
+- 硬约束校验表。
+- 经验编号和交付物摘要。
+
+报告不应暴露主机或容器绝对路径。内部函数名只作为审计证据，不作为产品名称。
+
+# 9. 评测、可靠性与工程质量
+
+## 9.1 证据必须分层
+
+| 证据层 | 运行内容 | 能证明什么 | 不能证明什么 |
+|---|---|---|---|
+| A | 真实 Gemma 4 + ADK，工具为确定性替身 | 模型工具选择、停止和恢复 | 30 次真实 MPC 计算 |
+| B | 本机真实 0.3.3 / 2.2.3 Tool 4 | 当前绑定、MPC、空间产物与校验 | A/B/C/D 全量重训或部内复测 |
+| C | 底层引擎历史内网交接记录 | 历史版本在目标环境的工程可行性 | 当前 GIS Data Agent 生产验收 |
+| D | AlphaEarth / OKF 设计 | 扩展方向 | 已进入主链 |
+
+## 9.2 Gemma 4 + ADK 可靠性
+
+三类场景各运行 10 次：
+
+| 场景 | 通过 | 平均延迟 | P95 | 精确轨迹一致性 |
+|---|---:|---:|---:|---:|
+| 首次审计通过 | 10/10 | 7.97 s | 9.53 s | 100% |
+| 版本不兼容停止 | 10/10 | 3.24 s | 4.88 s | 100% |
+| 一次重规划后通过 | 10/10 | 13.57 s | 15.31 s | 100% |
+
+总体 30/30，Wilson 95% 区间为 88.65% 到 100%。
+
+为什么仍不能说“100% 可靠”：样本只有 30 次，输入是受控场景，且规划工具响应使用确定性替身。统计区间下界 88.65% 也明确表示证据仍有限。
+
+## 9.3 真实 MPC 验证
+
+锁定路径真实执行 0.3.3 / 2.2.3 的 Tool 4，并通过同一 Agent 轨迹完成审计与经验提交。历史自动验证总耗时为 `93.490 s / 112.940 s`；新版中文 UI 浏览器运行总用时 `88.6 s`，其中 MPC `73.7 s`。不同运行的时间和 episode ID 会变化，业务指标在锁定数据与参数下保持一致。
+
+当前输入被识别为 `legacy_three_digit_test_data`，并复用旧流程生成的 prepared 与 ONNX。因此不能描述成 v2.2.3 对最新真实权威四库的全量重训。
+
+## 9.4 决赛质量检查
+
+| 检查 | 结果 | 说明 |
+|---|---:|---|
+| 最终镜像运行时回归 | 236 passed | NL2SQL、WorldModel、治理、预检与展示 |
+| 宿主交付契约 | 6 passed | Compose、配置、instruction 与脚本入口 |
+| 决赛关键 Python 子集 | 73 passed | 与 236 有范围关系，不能相加 |
+| 模型网关与工具过滤 | 52 passed | 路由、thinking 与工具分类兼容性 |
+| 确定性行为契约 | 5/5 | 成功、恢复、转人工、版本阻断、拒绝未审计写入 |
+| Ruff / Python 编译 | passed / passed | 决赛关键模块 |
+| 前端生产构建 | passed | 保留大 chunk 与依赖提示 |
+| Compose 与核心容器 | passed / healthy | app、PostGIS、Redis |
+| 主机预检 | 6/6 | 版本、prepared、3 个 ONNX、Gemma 标签 |
+
+测试数量按不同责任层报告，不合并成一个夸大的“总测试数”。
+
+## 9.5 边缘情况
+
+当前显式覆盖：
+
+- 版本不兼容，2 个工具后停止。
+- 缺资源或区域 ensemble 不匹配，拒绝规划。
+- 首次约束失败，只允许一次重规划。
+- 第二次仍失败，停止并转人工。
+- 缺空间产物，禁止经验写入。
+- 重复提交同一 episode，保持幂等。
+- SQL 引用未 grounding 表，重试后仍不合规则拒绝。
+- SQL 为占位、幻觉表或写操作，拒绝执行。
+
+## 9.6 已知非阻断项
+
+- 前端主包仍较大，生产化应继续拆包。
+- 部分 ADK/asyncio 依赖存在弃用 warning。
+- 真实 MPC 某些空分组计算保留 RuntimeWarning，但未中止锁定验收。
+- Live ADK 评测依赖 Ollama，不进入没有模型服务的普通 CI。
+- 当前尚缺真实用户数、节省工时和人工修订率的公开统计。
+
+# 10. 部署、安全与可复现性
+
+## 10.1 决赛运行拓扑
+
+{width=96%}
+
+核心 Compose 服务为 app、db 和 redis；MinIO 可承担对象存储扩展。底层算法包与 prepared/ensemble 以只读方式挂载，运行输出写入按用户和时间隔离的目录。
+
+## 10.2 预检
+
+正式演示前必须验证：
+
+- `paper9-mnr-offline-package == 0.3.3`
+- `paper9v2 == 2.2.3`
+- Bishan prepared 数据存在。
+- 3 个 ONNX ensemble members 存在。
+- `Gemma4:26b` 标签可用。
+- app、PostGIS、Redis 为 healthy。
+
+预检失败时应修复环境或切换到同版本录制视频，不应现场临时降级版本。
+
+## 10.3 安全设计
+
+- NL2SQL 通过只读策略、postprocessor、runtime guard 和数据库执行层形成纵深防御。
+- 工具参数做类型、范围和枚举校验。
+- 外部路径不直接展示给用户，输出目录按用户隔离。
+- 版本不匹配、缺模型、缺数据或缺产物均采用 fail closed。
+- 经验记录保存 schema version 和 SHA-256，便于追溯与防止无声替换。
+- 高风险业务约束不由 LLM 自评，也不允许 Agent 自动激活新规则。
+
+## 10.4 可复现性
+
+仓库提供：
+
+- `Dockerfile` 与 `docker-compose.gemma4-demo.yml`
+- `.env.finals.example`
+- 固定模型与算法版本检查
+- 预检脚本、真实提示词验证器与行为契约评测
+- CQ-125 模型选型数据
+- 锁定 MPC 摘要、审计 JSON、地图和发布哈希
+- 部署、演示、Q&A、主张边界与质量检查文档
+
+# 11. 扩展到其他地区和生产环境
+
+## 11.1 新增地区的必要步骤
+
+1. 明确 DLTB、DEM、行政区和可选参考图层的数据契约。
+2. 检查地类编码、唯一标识、几何有效性和面积单位。
+3. 选择适合本地区的投影 CRS，不能机械使用 EPSG:32648。
+4. 重新构建空间 blocks 与邻接关系。
+5. 重新采样 transition/pairwise 数据。
+6. 为新 `n_blocks` 训练本地区 ensemble。
+7. 进行 ONNX parity 和区域维度检查。
+8. 定义本地区硬约束与人工验收口径。
+9. 使用独立空间产物复算面积、坡度和连片度。
+10. 通过后才允许进入该地区的 verified memory。
+
+## 11.2 为什么不能直接全国泛化
+
+- 不同地区的地类编码、CRS、坡度分布和图斑粒度不同。
+- action embedding 静态依赖 `n_blocks`。
+- reward landscape 与政策约束具有地域性。
+- 当前 ensemble 学到的是特定地区、特定状态和行动定义下的动力学近似。
+
+架构支持扩展，不等于模型已经跨全国验证。
+
+## 11.3 生产化优先事项
+
+- 把 live model evaluation 放入具备 Ollama 的专用 CI runner。
+- 增加超时、模型不可用、数据库瞬断和报告生成失败的端到端测试。
+- 为 reference query 增加审核状态和自动降级机制。
+- 把 ensemble disagreement 纳入 Agent 的风险信号。
+- 对每个地区建立独立 benchmark、数据版本和验收清单。
+- 增加真实用户效率指标：任务耗时、人工修订率、SQL 一次执行率和方案复核时间。
+
+# 12. 深度技术问答
+
+## 12.1 Gemma 4 是不是只是把一个固定 Python pipeline 调起来？
+
+**20 秒回答**：不是，但要分层看。A/B/C/D 是确定性算法 pipeline；Gemma 4 的自主性体现在它先观察版本和资源，再根据经验与审计反馈选择停止、提交或只重跑 Tool 4。真实 ADK 评测出现 2、6、8 个工具三种轨迹，而不是每次固定走同一条链。
+
+**继续追问时**：指出 `MentionWorldModelV21` 是标准 `LlmAgent`，`WorldModelV21Toolset` 暴露 10 个函数；`paper9_agent_governance.py` 独立于 ADK，确保模型不能绕过审计。
+
+## 12.2 这算原生函数调用吗？
+
+**20 秒回答**：World Model 主链使用 ADK `FunctionTool` 与 `LongRunningFunctionTool`，Gemma 4 产生结构化函数名和参数，ADK 返回结构化 function response。10 个工具中 5 个是同步控制工具，5 个是长任务工具。UI 的六步轨迹来自真实 ADK 事件，不是事后拼接文本。
+
+**边界**：`@NL2SQL` 的外层函数事件是确定性 wrapper，SQL 合成本身由 Gemma 4 完成；多步动态工具选择的主证据来自 `@WorldModelV21`。
+
+## 12.3 为什么不让 Gemma 4 直接生成最终规划方案？
+
+**20 秒回答**：LLM 适合理解目标和选择工具，但不适合直接保证十万级空间记录上的面积守恒、坡度下降和连片度提升。数值方案由 transition model + MPC + 真实环境执行产生，约束由独立代码校验，这样结果可以复算和审计。
+
+## 12.4 世界模型与强化学习的关系是什么？
+
+**20 秒回答**：这是 model-based planning。先通过环境 rollout 采集 `(s, a, r, s')` 和 pairwise 行动排序数据，训练 transition/reward ensemble，再用 MPC 在模型上前瞻搜索。它借鉴了深度强化学习中的状态、行动、奖励和模型学习，但现场执行不是一个端到端 model-free policy。
+
+## 12.5 为什么叫 GWM 原型而不是普通优化器？
+
+**20 秒回答**：普通优化器可以只在当前状态上直接搜索；这里显式学习了 `state + action -> next state + reward` 的空间动力学，并让 MPC 消费这个预测模型。这已经具备世界模型的核心结构，但状态与行动仍是单领域、单尺度，所以只称领域原型。
+
+## 12.6 为什么使用 ensemble？
+
+**20 秒回答**：多个独立成员能降低单模型偶然误差，并提供预测分歧信号。当前 3 个 ONNX 成员在 MPC 中取均值预测；后续可以把成员方差作为风险信号，在不确定性过高时缩短 rollout、增加真实校验或转人工。
+
+**边界**：当前主闭环主要使用均值，尚未完整实现基于方差的自主风险控制。
+
+## 12.7 `horizon=1` 还能叫 MPC 吗？
+
+**20 秒回答**：可以，它是 MPC 的退化快速配置，每一步仍重新观测状态、评估候选并执行第一步行动。为了 5 分钟演示使用 `H=1, K=1`；研究和生产场景可以使用更大的 H 与 K，但计算时间会上升。`H=1` 不能代表完整长时推演能力。
+
+## 12.8 规划里面积不减少是软奖励还是硬约束？
+
+**20 秒回答**：两层都有。首次调用把 `cultivated_area_floor_delta_ha=0` 传给环境，action mask 排除会突破面积下限的动作；规划结束后，独立审计再次要求面积变化大于等于 0，并同时检查坡度、连片度和空间产物。
+
+## 12.9 自然资源部内网证据到底证明什么？
+
+**20 秒回答**：底层县域耕地优化项目的内部交接材料记录，历史版本曾在目标内网使用真实权威数据完成全流程。这证明了底层算法的环境适配和业务可行性。当前 GIS Data Agent 绑定 0.3.3 / 2.2.3 做本机验证，不能说当前版本已经部内复测或生产验收。
+
+## 12.10 为什么 30/30 还要给 Wilson 区间？
+
+**20 秒回答**：因为有限样本的 100% 不等于总体永远 100%。30/30 的 Wilson 95% 区间下界约为 88.65%，主动报告区间能防止过度主张，也说明下一步应该扩大提示词变体和故障场景。
+
+## 12.11 GeoSQL 的 benchmark 是不是只测语法？
+
+**20 秒回答**：不是，核心指标是 execution accuracy。SQL 必须在同一 PostGIS 数据上执行并返回正确结果。Valid 只能说明能执行，EX 才说明结果正确。模型选型主要看 Full EX 和完整时延。
+
+## 12.12 为什么要做 semantic rewrite，会不会变成手写规则代替模型？
+
+**20 秒回答**：模型负责开放式语言理解和 SQL 合成；rewrite 只修正可确定的数据库与 GIS 语义，例如 SRID、米制距离、字段别名、枚举编码和空间连接去重。规则来自 semantic context，不返回预写答案。它相当于编译器的类型检查和优化，不是替代模型。
+
+## 12.13 如何防止 SQL 注入或破坏数据库？
+
+**20 秒回答**：Prompt 只是第一层，之后还有 AST/规则后处理、写操作拒绝、allow-list 表检查、占位/幻觉 SQL 检测和只读数据库执行。任何一层失败都返回拒绝，不会让模型自行绕过。
+
+## 12.14 自动沉淀 reference query 会不会把错答案学进去？
+
+**20 秒回答**：这是必须承认的风险。当前执行成功案例可以自动沉淀并做相似度去重，但执行成功不等于语义正确。高置信知识的下一步应增加 benchmark 复核、人工审批、来源与版本字段，以及错误后的降级或撤销机制。
+
+## 12.15 Verified Memory 与普通 RAG 有什么不同？
+
+**20 秒回答**：普通 RAG 主要检索文本；这里保存的是通过业务约束和产物完整性校验的结构化 episode，包含版本、参数、指标、摘要哈希和审计策略。它只能辅助下一次决策，不能覆盖高风险规则。
+
+## 12.16 为什么只允许一次重规划？
+
+**20 秒回答**：无限自主循环会增加时延、成本和不可预测性。一次恢复足以证明 Agent 能根据失败反馈调整；第二次仍失败说明模型、资源或约束需要人工判断。这个上限同时写进 prompt 与审计策略。
+
+## 12.17 如何证明地图不是事先准备的图片？
+
+**20 秒回答**：NL2SQL 地图来自与标量查询同一 PostGIS 快照，图层数量必须是 35/1/1；规划地图来自本次运行目录的 `optimized_dltb`，按 `CHG_FLAG` 渲染。报告、地图、指标和 episode 都关联同一次运行产物。
+
+## 12.18 如何支持新数据集？
+
+**20 秒回答**：Agent 层按 dataset preset 解耦，但算法层必须重新完成数据契约、CRS、地类编码、block 构建、采样、训练和地区独立验收。由于 action embedding 静态依赖 `n_blocks`，不能直接拿 Bishan ensemble 跑另一个县。
+
+## 12.19 AlphaEarth 和 OKF 为什么没有放进主 Demo？
+
+**20 秒回答**：因为主 Demo 只展示已经进入真实闭环并有证据的技术。ADK 已在主链；AlphaEarth 目前是长期空间表征方向，OKF 是知识交换 sidecar。未完成的集成不应该为了技术数量占用 5 分钟并增加评委追问风险。
+
+## 12.20 最大创新点是什么？
+
+**20 秒回答**：不是单独的 NL2SQL 或单独的 MPC，而是把 Gemma 4 的任务级决策、ADK 原生函数调用、学习型空间动力学、MPC、确定性审计和已验证经验连接成受控闭环。它展示了下一代空间智能体从“LLM 加工具”走向“LLM 加 GWM”的可实现路径。
+
+# 13. 可说、限定说与不能说
+
+## 13.1 可以直接说
+
+- Gemma 4 26B 在 CQ-125 上执行正确 113/125，31B 为 114/125。
+- 26B 完整基准 12.50 分钟，31B 为 20.04 分钟，26B 约快 37.6%。
+- World Model Agent 使用 Google ADK 的 10 个函数工具。
+- 真实 Gemma 4 + ADK 在三个受控分支中运行 30 次，30/30 符合契约。
+- 当前真实 Tool 4 处理 101,657 条输入、53,004 个环境图斑和 2,640 个 blocks。
+- 406 对置换后，面积、坡度和连片度通过硬约束校验。
+- 未通过审计或缺空间产物的结果不能进入 verified memory。
+- 当前实现是领域化 GWM 原型。
+
+## 13.2 必须带限定词
+
+- 30/30 是真实模型编排评测，算法工具为确定性替身。
+- 本机运行真实执行 0.3.3 / 2.2.3 的 Tool 4，但复用历史 prepared/ONNX。
+- 历史内网证据属于底层优化项目，不等于当前 Agent 生产验收。
+- 架构支持区域扩展，不等于已经全国泛化。
+- AlphaEarth 是 `Tech Preview`，OKF 是知识交换 sidecar。
+
+## 13.3 不能说
+
+- “Gemma 4 直接计算了 MPC 数值。”
+- “A/B/C/D 固定 pipeline 就是大模型的动态规划。”
+- “MPC 本身就是世界模型。”
+- “当前已经是完整通用地理空间世界模型。”
+- “0.3.3 / 2.2.3 已在自然资源部完成生产验证。”
+- “当前 Bishan 输入是最新真实权威四库数据。”
+- “30/30 证明所有真实问题 100% 可靠。”
+- “AlphaEarth 和 OKF 已进入生产闭环。”
+
+# 14. 代码与证据索引
+
+## 14.1 核心代码
+
+| 主题 | 文件 / 函数 |
+|---|---|
+| Gemma 4 模型配置 | `data_agent/model_gateway.py::ModelRegistry` |
+| Agent 构建 | `data_agent/agent.py::_build_mention_nl2sql_agent`、`_build_mention_world_model_v21_agent` |
+| World Model instruction | `data_agent/paper9_agent_prompt.py::PAPER9_AGENT_INSTRUCTION` |
+| 10 个 ADK 工具 | `data_agent/toolsets/world_model_v21_tools.py::WorldModelV21Toolset` |
+| A/B/C/D 适配器 | `data_agent/world_model_v21.py::WorldModelV21Service` |
+| 硬约束审计 | `data_agent/paper9_agent_governance.py::evaluate_paper9_summary` |
+| 经验库 | `data_agent/paper9_agent_governance.py::Paper9EpisodeStore` |
+| NL2SQL 高层工具 | `data_agent/nl2sql_executor.py::run_nl2semantic2sql` |
+| Semantic rewrite | `data_agent/nl2sql_semantic_rewrite.py::apply_semantic_sql_rewrites` |
+| SQL runtime guard | `data_agent/runtime_guards.py::is_safe_sql` |
+| NL2SQL 地图 | `data_agent/nl2sql_presentation.py` |
+| 规划中文展示 | `data_agent/world_model_v21_presentation.py` |
+| 规划 PDF | `data_agent/world_model_v21_report.py` |
+
+底层县域优化项目：
+
+| 主题 | 文件 |
+|---|---|
+| 17/12 维状态与环境 | `arcgis-farmland-mpc/farmland_mpc/county_env.py` |
+| transition network | `arcgis-farmland-mpc/farmland_mpc/transition_model.py` |
+| 对比学习 trainer | `arcgis-farmland-mpc/farmland_mpc/contrastive_trainer.py` |
+| ensemble 训练与 ONNX | `arcgis-farmland-mpc/farmland_mpc/train_ensemble.py` |
+| ONNX ensemble runtime | `arcgis-farmland-mpc/farmland_mpc/ensemble_runner.py` |
+| MPC | `arcgis-farmland-mpc/farmland_mpc/mpc_plan.py` |
+
+## 14.2 关键证据
+
+| 证据 | 文件 |
+|---|---|
+| 模型选型 | `docs/assets/gemma4_host228_scale_sweep_summary.csv` |
+| ADK 30 次可靠性 | `data_agent/demo_evidence/paper9/finals_20260730/adk_reliability_report.json` |
+| 最终真实提示词验证 | `data_agent/demo_evidence/paper9/finals_20260730/verified_finals_demo_report.json` |
+| 锁定 MPC 摘要 | `docs/finals/evidence/world_model_bishan_20260730_155442/mpc_summary.json` |
+| 锁定审计 | `docs/finals/evidence/world_model_bishan_20260730_155442/paper9_agent_audit.json` |
+| 行为契约 | `data_agent/demo_evidence/paper9/finals_20260730/behavior_contract_report.json` |
+| 质量检查 | `docs/finals/quality_gate_report.md` |
+| 主张边界 | `docs/finals/claim_register.md` |
+| 唯一演示脚本 | `docs/finals/verified_demo_script.md` |
+
+# 附录 A：锁定参数与版本
+
+```text
+Gemma model: Gemma4:26b
+ADK model adapter: LiteLlm / ollama_chat
+WorldModel adapter: 2.1.0
+algorithm package: 0.3.3
+algorithm version: 2.2.3
+dataset: bishan
+env_kind: county
+horizon: 1
+top_k: 1
+n_episodes: 1
+continuation: greedy
+scoring: reward
+cultivated area floor: 0 ha delta
+ensemble members: 3
+environment max steps: 100
+```
+
+# 附录 B：术语表
+
+| 术语 | 含义 |
+|---|---|
+| ADK | Google Agent Development Kit，Agent 运行时与工具框架 |
+| FunctionTool | 短时同步函数工具 |
+| LongRunningFunctionTool | 长任务函数工具 |
+| GeoSQL | 带空间类型、谓词、距离、面积、SRID 等语义的 SQL |
+| EX | Execution Accuracy，执行结果正确率 |
+| Valid | SQL 能成功执行，不一定答案正确 |
+| GWM | Geospatial World Model，地理空间世界模型 |
+| Transition Model | 预测 `state + action -> next state + reward` 的模型 |
+| Ensemble | 多个独立模型成员组成的集成 |
+| MPC | Model Predictive Control，模型预测控制 |
+| Horizon | 每次 MPC 前瞻的步数 |
+| Steps Run | 环境实际执行的步数 |
+| Verified Episode | 通过业务约束和产物完整性校验的运行经验 |
+| Fail Closed | 缺证据或不兼容时默认拒绝，而不是默认放行 |
+
+# 附录 C：评分维度对应关系
+
+| 评审维度 | 本手册中的核心证据 |
+|---|---|
+| 真实影响力 30% | 第 2 章用户与任务；第 5.7 节真实空间问数；第 6.9 节县域指标；第 13 章证据边界 |
+| 技术卓越度 25% | 第 3 章模型选型；第 4 章 ADK；第 5 章 GeoSQL；第 6 章 GWM/MPC；第 7 章审核与记忆 |
+| 功能完备性 20% | 第 8 章地图/报告；第 9.5 节边缘情况；第 10 章部署预检 |
+| 创新性 15% | 第 4.1 节两层规划；第 6.2 节 GWM 关系；第 7 章受控自主闭环 |
+| 演示表现 10% | 第 1 章快速索引；第 8 章证据呈现；第 12 章深度问答；第 14 章代码与证据索引 |
diff --git a/docs/finals/GIS_Data_Agent_Gemma4_Finals_Technical_Handbook_CN.pdf b/docs/finals/GIS_Data_Agent_Gemma4_Finals_Technical_Handbook_CN.pdf
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+# Gemma 4 决赛资料入口
+
+更新时间：2026-07-30
+
+本目录是 5 分钟路演、3 分钟 Q&A、现场 Demo 和技术审查的单一入口。所有结论按证据类型分层，避免把模型编排评测、算法运行和历史内网记录混成同一种证明。
+
+## 决赛交付
+
+- [可编辑 PPTX](GIS_Data_Agent_Gemma4_Finals_CN.pptx)：6 页主路演 + 5 页 Q&A 附录，已绑定 406 对置换证据。
+- [离线 PDF](GIS_Data_Agent_Gemma4_Finals_CN.pdf)：与 PPTX 同次构建，用于断网备份和设备兼容检查。
+- 主路演逐字稿目标 `4:40`，预留 `20` 秒；最终文件哈希见 [release_manifest.sha256](release_manifest.sha256)。
+
+## 当前结论
+
+> 2026-07-30 最新真实验收已将县域耕地规划锁定结果更新为 406 对置换；PPTX/PDF 已按同一
+> 证据快照重建。录制前仍需按 [verified_demo_script.md](verified_demo_script.md) 完成人工 UI 复核。
+
+GIS Data Agent 的决赛主线已经从固定工作流升级为受控自主 Agent，并明确走向 `LLM + GWM`：
+
+```text
+版本与资源预检
+-> 检索已验证经验
+-> Gemma 4 选择县域规划工具
+-> 硬约束审计
+-> 通过则写入已验证经验库
+-> 失败则最多重规划一次
+-> 再失败则停止并转人工
+```
+
+当前工程证据：
+
+- Gemma 4 + Google ADK 三类分支各运行 10 次，30/30 通过行为契约。
+- 县域耕地空间优化引擎 `0.3.3 / 2.2.3` 已完成真实 MPC 规划；六次函数调用闭环、硬约束校验和同指标空间成果均通过验收。
+- 决赛主机预检全部通过：版本、Bishan prepared、3 个 ONNX 成员和 `Gemma4:26b` 标签均就绪。
+- 确定性行为契约覆盖首次成功、一次恢复、二次失败转人工、版本阻断和禁止未审计写入。
+- 最终镜像运行时回归 `236 passed`，宿主交付契约 `6 passed`，另有接口兼容测试 `52 passed`；Ruff、Python 编译、前端生产构建和 Compose 配置解析通过。
+- app、PostGIS 和 Redis 当前均为 `healthy`；前端大包与依赖弃用警告已记录，不能表述为零警告运行。
+- 当前县域耕地空间优化引擎是领域化 GWM 原型：状态转移模型集成是动力学内核雏形，MPC 是规划器，硬约束校验负责结果验收；不表述为完整通用 GWM。
+- 县域规划可导出约 5 页 A4 图文 PDF，包含指标看板、真实变化地图、六步调用轨迹、逐函数用时和审计表。
+
+## 文档索引
+
+| 文档 | 用途 |
+|---|---|
+| [scoring_evidence_matrix.md](scoring_evidence_matrix.md) | 按 30/25/20/15/10 权重核对证据、缺口和路演位置 |
+| [technical_report.md](technical_report.md) | 决赛技术报告短版，用于快速通读 |
+| [GIS_Data_Agent_Gemma4_Finals_Technical_Handbook_CN.md](GIS_Data_Agent_Gemma4_Finals_Technical_Handbook_CN.md) | 深度技术答辩手册源文档，含 9 张正式技术图、关键实现、代码索引和 20 个追问口径 |
+| [GIS_Data_Agent_Gemma4_Finals_Technical_Handbook_CN.docx](GIS_Data_Agent_Gemma4_Finals_Technical_Handbook_CN.docx) | 可编辑 Word 版，含二级可点击目录，并保留导航窗格章节树 |
+| [GIS_Data_Agent_Gemma4_Finals_Technical_Handbook_CN.pdf](GIS_Data_Agent_Gemma4_Finals_Technical_Handbook_CN.pdf) | 离线 PDF 版，适合比赛设备快速查阅 |
+| [assets/diagrams](assets/diagrams) | 技术图的 SVG 可编辑源文件与约 3200 像素宽 PNG 发布文件 |
+| [evidence/world_model_bishan_20260730_155442](evidence/world_model_bishan_20260730_155442) | 406 对置换锁定快照：原始汇总、硬约束审核和变化图斑；明确标注为旧三位测试编码 |
+| [claim_register.md](claim_register.md) | 哪些话能说、哪些必须限定、哪些不能说 |
+| [deployment.md](deployment.md) | 决赛环境部署、预检和验证 |
+| [quality_gate_report.md](quality_gate_report.md) | 决赛质量检查报告：精确测试集合、构建结果、运行状态与已知警告 |
+| [demo_runbook.md](demo_runbook.md) | 5 分钟现场流程、视频备份和故障预案 |
+| [verified_demo_script.md](verified_demo_script.md) | 2026-07-30 真实数据验收后的唯一最新版操作、录屏与逐字口播 |
+| [pitch_script.md](pitch_script.md) | 4 分 40 秒逐页逐字口播与强制切屏时间 |
+| [video_script.md](video_script.md) | 主备视频、异常恢复和版本阻断的镜头级脚本 |
+| [rehearsal_log.md](rehearsal_log.md) | 三轮彩排、投影可读性和五秒故障切换验收 |
+| [release_manifest.sha256](release_manifest.sha256) | 冻结决赛交付文件的 SHA-256 完整性清单 |
+| [qa.md](qa.md) | 3 分钟 Q&A 短答案；附录 A1–A5 直接对应五个高概率问题 |
+
+## 证据分层
+
+| 层级 | 内容 | 可证明 | 不可证明 |
+|---|---|---|---|
+| A | 真实 Gemma 4 26B + ADK 重复运行，规划工具为确定性替身 | 工具选择、分支控制、停止与恢复的模型可靠性 | 30 次底层算法计算成功 |
+| B | 本机 0.3.3 / 2.2.3 真实 MPC 运行 | 当前代码绑定、GWM 领域原型、实际 MPC、空间产物和硬约束校验 | 完整通用 GWM、v2.2.3 四库全流程重训或部内复测 |
+| C | 底层引擎（内部代号 Paper9）交接文档记录的历史内网运行 | 历史版本在目标环境处理真实权威数据的工程可行性 | GIS Data Agent 已在部内生产部署 |
+| D | AlphaEarth / OKF | 技术扩展方向和知识治理设计 | 已成为主 Agent 的生产闭环 |
+
+## 当前路演原则
+
+- Gemma 4 是决策与控制面；Google ADK 是 Agent 运行时；PostGIS、GIS 与县域耕地空间优化引擎是确定性行动面。
+- 主叙事统一使用“县域耕地空间优化引擎”；`Paper9` 仅作为技术文档中的内部研发代号。
+- GWM 关系必须说清：状态转移模型是动力学内核雏形，MPC 使用预测搜索方案，硬约束校验负责结果验收。
+- 主 Demo 展示一个成功闭环，异常恢复使用预录视频或已保存轨迹。
+- 先讲真实需求与量化证据，再讲技术；Google 技术栈是可信度乘数，不是 Logo 数量。
+- AlphaEarth 必须标注 `Tech Preview`；OKF 必须称为知识交换 sidecar。
+- 任何自然资源内网表述都必须遵守 [claim_register.md](claim_register.md)。
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+
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+
+
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+
+
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+
+
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diff --git a/docs/finals/claim_register.md b/docs/finals/claim_register.md
new file mode 100644
index 00000000..d7fd123f
--- /dev/null
+++ b/docs/finals/claim_register.md
@@ -0,0 +1,63 @@
+# 决赛主张与证据边界
+
+更新时间：2026-07-30
+
+本文件是 PPT、口播、视频字幕、README 和 Q&A 的事实核查基线。出现冲突时，以更保守的表述为准。
+
+## 可以直接说
+
+| 主张 | 证据 |
+|---|---|
+| Gemma 4 26B 在 CQ-125 上执行正确 113/125，31B 为 114/125 | `docs/assets/gemma4_host228_scale_sweep_summary.csv` |
+| 决赛 Agent 使用 Google ADK 的原生函数工具 | `WorldModelV21Toolset` 的 10 个工具和 ADK 运行事件 |
+| Gemma 4 能按工具反馈执行首次成功、版本阻断和一次恢复三个分支 | 30 次 live ADK 报告，三场景各 10/10 |
+| 县域耕地空间优化引擎当前适配器绑定包 0.3.3、算法 2.2.3 和指定 commit | 状态检查、预检报告与 verified episode provenance |
+| 本机当前县域耕地优化 Tool 4 通过硬约束校验 | `verified_episodes.jsonl` 与真实输出摘要 |
+| 未通过审计或缺空间产物的结果不能进入 verified memory | 治理代码和行为契约测试 |
+| 当前实现是领域化 GWM 原型：转移模型集成是动力学内核雏形，MPC 是规划器，硬约束校验负责结果验收 | 算法代码、真实 MPC 产物与技术报告 |
+| 规划结果可生成含地图、指标、调用轨迹和审计表的约 5 页图文 PDF | 同一次会话导出的 `County_Farmland_Planning_Report.pdf` |
+
+## 必须带限定词
+
+| 风险主张 | 推荐表述 |
+|---|---|
+| 自然资源部内网测试 | “底层县域耕地空间优化项目的内部交接材料记录历史版本曾在目标内网使用真实权威数据完成全流程；GIS Data Agent 当前另行绑定 v2.2.3 做本机验证。” |
+| v2.2.3 与现场关系 | “v2.2.3 修复了真实权威数据暴露的地类编码和融合问题，并形成新的离线候选包；当前没有把它表述为已完成部内复测。” |
+| 当前本机优化引擎运行 | “使用 0.3.3 / 2.2.3 代码执行 Tool 4，复用历史 Prepare / ONNX，输入为旧三位测试编码。” |
+| 30/30 可靠性 | “30 次为真实 Gemma 4 + ADK 编排评测，县域耕地优化工具响应是确定性替身，不是 30 次算法重跑。” |
+| 世界模型 | “县域耕地空间优化引擎是领域化 GWM 原型；学习型状态转移模型集成是动力学内核雏形，MPC 使用预测搜索方案，硬约束校验决定结果能否保存。” |
+| 自主 Agent | “受控、领域限定、可审计，最多一次重规划，高风险失败转人工。” |
+| 扩展性 | “架构支持增加区域和工具；当前证据不等于全国泛化。” |
+
+## 不能说
+
+- “Paper9 v2.2.3 已在自然资源部内网完成生产验证。”
+- “GIS Data Agent 已在自然资源部生产部署。”
+- “当前本机运行完成了 v2.2.3 A/B/C/D 全流程重训。”
+- “当前 Bishan 输入就是最新真实权威四库数据。”
+- “30/30 证明 Agent 在所有真实问题上 100% 可靠。”
+- “Gemma 4 直接算出了 MPC 或 GIS 数值。”
+- “A/B/C/D 固定 pipeline 本身就是 Gemma 动态规划。”
+- “Paper9 是 Dreamer 式 latent generative world model。”
+- “Paper9 已经是完整通用地理空间世界模型。”
+- “MPC 本身就是世界模型。”
+- “AlphaEarth 已生产化。”
+- “OKF 已自动更新并激活高风险领域知识。”
+- “使用更多 Google 技术有官方隐藏加分。”
+
+## Google 技术口径
+
+| 技术 | 正确角色 |
+|---|---|
+| Gemma 4 | 决策、工具选择、GeoSQL 生成和结果解释 |
+| Google ADK | Agent 运行时、函数调用、session 和工具事件 |
+| AlphaEarth | 地理表征扩展，`Tech Preview` |
+| OKF | 人和 Agent 可读的知识交换 sidecar |
+
+Google 技术栈应解释“为什么存在、在哪里调用、产生什么证据”，不应以 Logo 数量作为卖点。
+
+## 命名口径
+
+- 主 PPT、主口播、视频字幕和 UI 主标题统一使用“县域耕地空间优化引擎”。
+- `Paper9` 只在技术报告、代码路径或被追问思想来源时说明为内部研发代号。
+- `GWM` 首次出现应展开为“地理空间世界模型”，之后再使用缩写。
diff --git a/docs/finals/demo_runbook.md b/docs/finals/demo_runbook.md
new file mode 100644
index 00000000..f3377116
--- /dev/null
+++ b/docs/finals/demo_runbook.md
@@ -0,0 +1,95 @@
+# 5 分钟现场 Demo Runbook
+
+目标：4 分 40 秒完成，预留 20 秒给现场切换或评委打断。操作、数字和逐字口播的唯一最新版见
+[verified_demo_script.md](verified_demo_script.md)。
+
+## 1. 六页主线
+
+| 时间 | 页 | 结论 | 屏幕动作 |
+|---|---:|---|---|
+| 0:00–0:30 | 1 | GIS 问数高频、空间规划高价值，但都要求可执行和可审计 | PPT |
+| 0:30–1:05 | 2 | 模型、运行时、确定性行动与治理边界清晰 | PPT |
+| 1:05–1:55 | 3 | Gemma 4 26B 由 CQ-125 选出，空间语义被真实执行 | 播放 35 秒锁定视频或实时结果 |
+| 1:55–3:25 | 4 | Gemma 4 + ADK 控制 Agent；GWM 领域原型 + MPC 完成空间推演与搜索 | 播放 70 秒加速版真实视频 |
+| 3:25–4:20 | 5 | 不是一次 Demo：编排、算法、工程和历史环境证据分层 | PPT 证据表 |
+| 4:20–4:40 | 6 | 下一代空间智能体将由 LLM + GWM 共同驱动 | PPT 总结 |
+
+逐字口播见 [pitch_script.md](pitch_script.md)；镜头级录制与字幕验收见 [video_script.md](video_script.md)；三轮实测记录见 [rehearsal_log.md](rehearsal_log.md)。
+
+## 2. 县域耕地空间优化现场提示词
+
+```text
+@WorldModelV21 请使用 bishan 数据集运行一次快速县域 MPC 规划，完成硬约束审计，并仅在通过后保存已验证经验。
+```
+
+成功时只讲四件事：
+
+1. Gemma 4 选择了 `status -> inspect -> recall -> pipeline -> audit -> commit`。
+2. 先完成版本兼容性与资源检查，Tool 1/2/3 复用，Tool 4 真实规划。
+3. 面积、坡度和连片度由确定性硬约束校验，不由模型自评。
+4. 只有通过结果进入 verified memory，下一任务才能召回。
+
+2026-07-30 锁定路径两次真实运行总耗时为 `93.490s / 112.940s`，末次对应最终代码。现场 70 秒片段必须由
+无剪辑母版的等待段加速生成，并标注该母版实际耗时；不得表述为优化引擎实时 70 秒完成。
+
+不要现场展开全部参数，不解释每个 A/B/C/D 内部细节。
+
+## 3. NL2Semantic2GeoSQL 演示
+
+最终只锁定一个问题，优先选择：
+
+```text
+@NL2SQL 统计距离道路网络中最长桥梁100米范围内的高德POI数量。
+```
+
+画面必须同时出现：自然语言问题、关键 `ST_DWithin(...::geography, 100)`、只读执行结果 `35`。口播只解释空间距离单位和 schema grounding，不现场读 SQL。
+
+## 4. 异常恢复视频
+
+异常分支不在主现场故意触发，单独准备 35–45 秒视频：
+
+```text
+status -> inspect -> recall -> pipeline
+-> audit(attempt=0, retryable=true)
+-> plan -> audit(attempt=1) -> commit
+```
+
+再准备 15 秒版本阻断片段：
+
+```text
+status(version incompatible) -> inspect -> stop
+```
+
+它证明 Agent 会安全停止，不会为了完成目标伪造成功。
+
+## 5. 赛前 60 分钟检查
+
+- 运行 `scripts/check_gemma4_finals_preflight.py`，确认 `ready=true`。
+- `docker compose ps` 全部 healthy。
+- Ollama `Gemma4:26b` 已预热，现场不再拉模型。
+- NL2SQL 数据库快照固定，结果与视频一致。
+- 县域耕地优化成功产物、6 工具轨迹、地图和图文 PDF 均已打开到浏览器标签。
+- 本地视频使用 H.264 MP4，1080p，离线可播放。
+- PPT、MP4、关键截图各保留本机和 U 盘副本。
+- 关闭通知、同步盘弹窗、自动更新和屏幕休眠。
+- 浏览器缩放和系统字体在投影仪上可读。
+
+## 6. 故障切换
+
+| 故障 | 5 秒内动作 | 口播 |
+|---|---|---|
+| 模型首 token 超过 8 秒 | 立即切到成功链视频 | “这里切到同版本预录轨迹，完整参数与结果均保留在运行日志。” |
+| 县域优化工具运行失败 | 切到真实运行产物和 verified episode | “现场不重试高成本规划，展示已封存的同版本真实产物。” |
+| 数据库不可用 | 切 NL2SQL 结果视频 | “该片段使用固定数据库快照，SQL 和执行结果可复核。” |
+| 前端空白 | 切 PPT 中的轨迹和地图截图 | 不现场调试 |
+| 无网络 | 保持本地 Gemma 4、PostGIS、县域优化引擎离线演示 | “核心链路不依赖公网。” |
+
+## 7. 禁止事项
+
+- 不现场运行十小时级完整训练。
+- 不故意制造不可控错误。
+- 不用应用层 fallback 冒充 Gemma 工具调用。
+- 不把 30/30 编排评测说成算法重跑。
+- 不把 GWM 领域原型说成完整通用 GWM，也不把 MPC 本身说成世界模型。
+- 不说 v2.2.3 已完成部内生产验证。
+- 不在主 5 分钟展开 AlphaEarth 或 OKF；只在 Q&A/附录回答。
diff --git a/docs/finals/deployment.md b/docs/finals/deployment.md
new file mode 100644
index 00000000..561a4cc8
--- /dev/null
+++ b/docs/finals/deployment.md
@@ -0,0 +1,106 @@
+# 决赛部署与验证
+
+## 1. 主机要求
+
+- Docker Desktop 或 Docker Engine + Compose。
+- Ollama 已加载精确标签 `Gemma4:26b` 和 embedding 模型。
+- Paper9 源码为 `paper9-mnr-offline-package 0.3.3 / paper9v2 2.2.3`。
+- Bishan runs 中存在 prepared 数据和 ONNX ensemble。
+- 端口 `8000`、`5433` 和 `6379` 可用；冲突时先调整 Compose 映射。
+
+## 2. 配置
+
+```bash
+cp .env.finals.example .env.finals
+```
+
+填写三个主机绝对路径：
+
+```dotenv
+PAPER9_HOST_REPO=/absolute/path/to/paper9-mnr-offline-package
+PAPER9_BISHAN_RUNS_HOST=/absolute/path/to/bishan-runs
+PAPER9_DONGXING_RUNS_HOST=/absolute/path/to/dongxing-runs
+```
+
+`.env.finals` 不提交版本库。
+
+## 3. 只读预检
+
+```bash
+.venv/bin/python scripts/check_gemma4_finals_preflight.py \
+ --output data_agent/demo_evidence/paper9/finals_20260730/finals_preflight_report.json
+```
+
+通过条件：
+
+- `paper9_package_version = 0.3.3`
+- `paper9_algorithm_version = 2.2.3`
+- Bishan `DLTB_with_slope.shp` 存在
+- ONNX 成员数大于 0，当前基线为 3
+- Ollama 精确包含 `Gemma4:26b`
+- 报告顶层 `ready = true`
+
+## 4. Compose 配置解析
+
+```bash
+docker compose --env-file .env.finals \
+ -f docker-compose.gemma4-demo.yml config
+```
+
+检查解析后的 `/app/paper9-demo` 来源必须是新版 `paper9-mnr-offline-package`，不能是旧 `arcgis-farmland-mpc` 源码目录。
+
+## 5. 启动
+
+```bash
+docker compose --env-file .env.finals \
+ -f docker-compose.gemma4-demo.yml up -d --build
+```
+
+```bash
+docker compose --env-file .env.finals \
+ -f docker-compose.gemma4-demo.yml ps
+```
+
+打开 `http://localhost:8000`。
+
+## 6. 赛前验证顺序
+
+1. 登录并打开运行日志面板。
+2. 执行唯一锁定的 NL2Semantic2GeoSQL 问题，保存 SQL、结果和地图截图。
+3. 执行 Bishan Paper9 成功提示词。
+4. 确认轨迹包含 6 个工具，且顺序完全正确。
+5. 确认 UI 显示包版本、算法版本、硬约束校验结果和 verified episode ID。
+6. 确认地图显示灰/红/绿 `CHG_FLAG` 图层。
+7. 刷新页面并确认关键运行日志仍可查看。
+8. 关闭外部网络后再执行一次，确认本地 Ollama 和离线资源可用。
+
+## 7. 当前已验证基线
+
+2026-07-30 已确认：
+
+- Compose 配置解析通过。
+- app、PostGIS、Redis 3 个容器均为 `healthy`。
+- 主机只读预检 6/6 通过，顶层 `ready = true`。
+- 决赛关键测试 73 passed，模型网关与工具过滤兼容测试 52 passed。
+- Ruff、Python 编译和前端生产构建通过。
+
+前端仍有大 chunk 和 loader 导出警告；依赖仍有弃用提示。完整范围和命令见 [quality_gate_report.md](quality_gate_report.md)。
+
+## 8. 可靠性基线复跑
+
+```bash
+.venv/bin/python scripts/run_paper9_adk_reliability_eval.py \
+ --runs-per-scenario 10 \
+ --output data_agent/demo_evidence/paper9/finals_20260730/adk_reliability_report.json
+```
+
+该命令会调用真实 Gemma 4 + ADK，但使用确定性 Paper9 工具替身。普通 CI 不运行此命令，因为 CI 没有本地 Ollama。
+
+## 9. 停止
+
+```bash
+docker compose --env-file .env.finals \
+ -f docker-compose.gemma4-demo.yml down
+```
+
+不要在赛前使用 `down -v`，避免删除数据库和上传产物卷。
diff --git a/docs/finals/evidence/world_model_bishan_20260730_155442/README.md b/docs/finals/evidence/world_model_bishan_20260730_155442/README.md
new file mode 100644
index 00000000..3abd3f0c
--- /dev/null
+++ b/docs/finals/evidence/world_model_bishan_20260730_155442/README.md
@@ -0,0 +1,13 @@
+# Bishan county planning evidence snapshot
+
+Source run: `/app/data_agent/uploads/admin/world_model_v21/20260730_155442_050986`
+
+- `mpc_summary.json`: unmodified planning summary copied from the finals container.
+- `paper9_agent_audit.json`: unmodified hard-gate audit copied from the same run.
+- `optimized_changes.geojson`: the `CHG_FLAG != 0` features extracted from the run's
+ `optimized_dltb.fgb`; the finals deck uses the existing same-snapshot county geometry
+ as the unchanged background.
+
+The live run returned 406 farmland-to-forest features and 406 forest-to-farmland
+features. The deck generator validates these counts against `swaps_completed` before
+building the presentation.
diff --git a/docs/finals/evidence/world_model_bishan_20260730_155442/mpc_summary.json b/docs/finals/evidence/world_model_bishan_20260730_155442/mpc_summary.json
new file mode 100644
index 00000000..409372d0
--- /dev/null
+++ b/docs/finals/evidence/world_model_bishan_20260730_155442/mpc_summary.json
@@ -0,0 +1,68 @@
+{
+ "config": {
+ "horizon": 1,
+ "top_k": 1,
+ "gamma": 0.99,
+ "continuation": "greedy",
+ "scoring": "reward",
+ "mpc_batch_size": 1024,
+ "n_episodes": 1,
+ "threads": 0,
+ "max_steps": 100,
+ "n_blocks": 2640,
+ "n_parcels": 53004,
+ "prepared_dir": "/app/bishan-runs/prepared",
+ "proj_crs": "EPSG:32648",
+ "reward_overrides": {},
+ "cultivated_area_floor_delta_ha": 0.0,
+ "baimu_area_floor_delta_ha": null,
+ "pair_selection_overrides": {
+ "gamma_conn": null,
+ "delta_conn": null
+ }
+ },
+ "ensemble": {
+ "n_members": 3,
+ "paths": [
+ "ensemble_member0.onnx",
+ "ensemble_member1.onnx",
+ "ensemble_member2.onnx"
+ ]
+ },
+ "results": [
+ {
+ "episode": 0,
+ "seed": 0,
+ "slope_change_pct": -0.6077654972924247,
+ "cont_change": 0.020184135977336926,
+ "baimu_count_change": 0,
+ "baimu_area_change_ha": 35.13161927157044,
+ "total_reward": 37.40937856603974,
+ "steps_run": 100,
+ "swaps_completed": 406,
+ "mean_step_time_s": 0.5115316987037659,
+ "total_time_s": 57.81345272064209,
+ "cultivated_area_ha": 48278.9065452229,
+ "cultivated_area_change_ha": 1.935935926192999,
+ "cultivated_area_change_pct": 0.004010060908461798,
+ "cultivated_area_floor_delta_ha": 0.0,
+ "cultivated_area_floor_ha": 48276.9706092967,
+ "baimu_area_floor_delta_ha": null,
+ "baimu_area_floor_ha": null
+ }
+ ],
+ "aggregate": {
+ "slope_pct_mean": -0.6077654972924247,
+ "slope_pct_std": 0.0,
+ "cont_mean": 0.020184135977336926,
+ "baimu_ha_mean": 35.13161927157044
+ },
+ "shapefile_output": {
+ "n_input": 101657,
+ "n_in_env": 53004,
+ "n_farm_to_forest": 406,
+ "n_forest_to_farm": 406,
+ "n_unchanged": 52192,
+ "land_use_code_scheme": "legacy_three_digit_test_data"
+ }
+}
\ No newline at end of file
diff --git a/docs/finals/evidence/world_model_bishan_20260730_155442/optimized_changes.geojson b/docs/finals/evidence/world_model_bishan_20260730_155442/optimized_changes.geojson
new file mode 100644
index 00000000..e8da55b4
--- /dev/null
+++ b/docs/finals/evidence/world_model_bishan_20260730_155442/optimized_changes.geojson
@@ -0,0 +1,819 @@
+{
+"type": "FeatureCollection",
+"name": "optimized_changes_20260730_155442",
+"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
+"features": [
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diff --git a/docs/finals/evidence/world_model_bishan_20260730_155442/paper9_agent_audit.json b/docs/finals/evidence/world_model_bishan_20260730_155442/paper9_agent_audit.json
new file mode 100644
index 00000000..b9555a09
--- /dev/null
+++ b/docs/finals/evidence/world_model_bishan_20260730_155442/paper9_agent_audit.json
@@ -0,0 +1,40 @@
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+ "records": [
+ {
+ "baimu_area_change_ha": 35.13161927157044,
+ "baimu_count_change": 0.0,
+ "cont_change": 0.020184135977336926,
+ "cultivated_area_change_ha": 1.935935926192999,
+ "episode": 0,
+ "slope_change_pct": -0.6077654972924247,
+ "steps_run": 100,
+ "total_reward": 37.40937856603974
+ }
+ ],
+ "recovery_hints": [],
+ "retryable": false,
+ "schema_version": "paper9.agent_audit.v1"
+}
diff --git a/docs/finals/pitch_script.md b/docs/finals/pitch_script.md
new file mode 100644
index 00000000..7a1ae3be
--- /dev/null
+++ b/docs/finals/pitch_script.md
@@ -0,0 +1,74 @@
+# 4 分 40 秒决赛逐字口播
+
+目标时长：`4:40`。现场硬上限是 `5:00`，本稿固定预留 `20` 秒处理切屏、掌声或评委打断。主路演只讲六页；附录只在 Q&A 中按问题调用。
+
+## 计时总表
+
+| 时间 | 页 | 必须让评委记住的结论 | 屏幕动作 |
+|---|---:|---|---|
+| 0:00-0:30 | 1 | 这不是聊天地图，而是可执行、可审计、可复用的 GIS Agent | PPT |
+| 0:30-1:05 | 2 | 同一 Agent 覆盖高频问数和高价值规划，模型与确定性工具职责分离 | PPT |
+| 1:05-1:55 | 3 | Gemma 4 26B 经 CQ-125 选型，空间 SQL 被真实执行 | 锁定视频或实时结果 |
+| 1:55-3:25 | 4 | Gemma 4 + ADK 控制 Agent，GWM 领域原型 + MPC 负责空间推演与搜索 | 成功链视频或实时轨迹 |
+| 3:25-4:20 | 5 | 编排、算法、工程和历史环境证据彼此独立、可复核 | PPT |
+| 4:20-4:40 | 6 | 下一代空间智能体将由 LLM + GWM 共同驱动 | PPT |
+
+## 01 · 开场 · 0:00-0:30
+
+屏幕：停在封面，指向右侧真实空间成果，不移动鼠标追逐细节。
+
+口播：
+
+> 各位评委好，我是周宁。GIS Data Agent 不是会聊天的地图，而是把自然语言问题落到可执行、可审计、可复用 GIS 行动的受控 Agent。今天我只展示两件事：高频空间问数，以及高价值县域耕地规划。右侧不是示意图，是本次决赛代码真实运行产生的空间成果。
+
+## 02 · 真实需求与架构边界 · 0:30-1:05
+
+屏幕：先指左、右两个场景，再沿底部从 Gemma 4 指到已验证经验库。
+
+口播：
+
+> 空间问数看似是 NL2SQL，但距离、坐标系和空间连接，都会让“看似正确”的 SQL 在执行时失败；它是 GIS 分析师的高频刚需。县域耕地规划频率更低、价值更高，必须同时满足面积、坡度和连片度约束。边界很清楚：Gemma 4 选择和解释，Google ADK 承载函数调用，PostGIS 和县域耕地空间优化引擎负责确定性计算，治理代码决定能否提交。
+
+## 03 · NL2Semantic2GeoSQL · 1:05-1:55
+
+屏幕：切入锁定片段；结果出现后停在 `ST_DWithin(...::geography, 100)` 和 `35`。不要滚动整段 SQL。
+
+口播：
+
+> 先看空间问数。我们锁定同一个问题：最长桥梁一百米范围内有多少高德 POI。系统先做 schema 和空间语义 grounding，再由 Gemma 4 生成 SQL；这里最关键的是把距离按 geography 的米制单位执行，并通过只读护栏访问 PostGIS，结果是三十五。模型也不是凭印象选择：在同一主机、同一 CQ-125 上，Gemma 4 26B 是一百一十三题正确，只比 31B 少一题，但完整运行约快百分之三十七点六，因此选为准确率与现场时延的工程平衡点。
+
+## 04 · 县域耕地规划 Agent · 1:55-3:25
+
+屏幕：成功链按 `status -> inspect -> recall -> pipeline/plan -> audit -> commit` 出现；随后定格地图与四个业务指标。
+
+口播：
+
+> 第二个场景不是生成一段建议，而是执行县域耕地空间布局优化。这里有两层规划：Gemma 4 观察版本、资源和已验证经验，再决定下一工具；Google ADK 把六个决策落实为结构化原生函数调用。县域耕地空间优化引擎则把图斑和空间块作为状态，用学习型状态转移模型集成预测候选行动后果，再由 MPC 搜索方案，不让大模型猜空间数值。
+>
+> 这也是我们走向地理空间世界模型的领域原型：状态转移模型是动力学内核雏形，MPC 使用预测搜索方案，硬约束校验负责结果验收；当前还不是完整通用 GWM。只有校验通过的方案才会写入已验证经验库；失败最多重规划一次，再失败转人工。这就是受控自主：模型能选择、观察和恢复，但不能绕过业务规则。界面同时显示六次函数调用各自的真实用时、总用时和规划用时。
+>
+> 这次真实 MPC 规划处理十万一千六百五十七条输入，完成四百零六对双向置换。结果耕地增加一点九四公顷，平均坡度下降百分之零点六零八，连片度和百亩方面积同时提升，并生成可交付的空间成果。此前锁定路径两次真实通过，耗时约一分半到两分钟；现场片段只加速等待段，不改写工具、用时和结果。
+
+## 05 · 分层证据 · 3:25-4:20
+
+屏幕：先扫过四个顶栏数字，再落到左侧三条可靠性分支，最后指向右侧真实环境证据。
+
+口播：
+
+> 一次跑通不能证明可靠。模型侧，真实 Gemma 4 26B 加 ADK 在首次成功、版本阻断和一次恢复三个分支各跑十次，三十次全部符合契约；Wilson 百分之九十五区间是百分之八十八点六五到一百，因此不夸大为普遍百分之百。算法侧是一次真实 MPC 规划和空间产物，不能与前面的确定性工具替身编排评测混算。工程侧分为二百三十六个镜像运行时测试、六个宿主交付契约和五十二个接口兼容测试，三个核心容器健康。历史内网记录与当前本机验证也严格分层，两者都不等于本系统生产验收。
+
+## 06 · 收束 · 4:20-4:40
+
+屏幕：停在总结页，结束后不要跳到附录。
+
+口播：
+
+> 下一代空间智能体不会只是给大模型不断增加 GIS 工具，而会由 LLM 加地理空间世界模型共同驱动：Gemma 4 与 ADK 理解目标、选择工具和协同，GWM 表示空间状态并推演行动后果，MPC 搜索方案，审核机制决定结果能否保存。今天我们已经验证空间问数、GWM 领域原型和受控闭环；下一步扩展多尺度、多行动与长期不确定性推演。谢谢。
+
+## 现场纪律
+
+- 到 `1:05` 和 `1:55` 必须完成切屏；任何一次首帧等待超过 `5` 秒，立即播放同版本离线片段。
+- 不朗读完整 SQL、工具参数或测试文件名；只说自然语言问题、关键空间语义、执行结果和证据边界。
+- 到 `4:20` 无论前面是否有小延迟，都必须进入总结页；绝不占用 Q&A 时间补讲。
+- 面向评委统一称“县域耕地空间优化引擎”；`Paper9` 仅在被追问研发来源时说明为内部代号。
+- 不说“完整通用 GWM”“生产部署”“全国泛化”“百分之百可靠”或“30 次真实算法重跑”。
diff --git a/docs/finals/qa.md b/docs/finals/qa.md
new file mode 100644
index 00000000..15fcad6e
--- /dev/null
+++ b/docs/finals/qa.md
@@ -0,0 +1,60 @@
+# 3 分钟 Q&A 口径
+
+更新时间：2026-07-30
+
+每个答案控制在 15–25 秒。顺序固定为：一句结论、一个证据、一个边界。附录 A1–A5 分别对应前五个高概率问题。
+
+## A1. Gemma 4 真的在规划，还是 Python 固定流程？
+
+结论：系统有两层规划。Gemma 4 通过 Google ADK 根据版本、资源、已验证经验和审计反馈选择下一工具、停止或恢复；县域耕地空间优化引擎再做确定性数值规划。真实 Gemma 4 + ADK 已跑出版本阻断、首次成功和一次恢复三种不同轨迹。边界是 A/B/C/D 仍为确定性算法阶段，不能冒充 LLM 动态规划。
+
+## A2. 县域耕地优化引擎是不是地理空间世界模型？
+
+结论：它是领域化 GWM 原型，不是完整通用地理空间世界模型。图斑和空间块构成状态，候选地类置换构成行动，学习型状态转移模型集成预测行动后果，这一部分是 GWM 动力学内核的雏形；MPC 使用预测搜索方案，硬约束校验负责结果验收。内部研发代号是 `Paper9`。当前还缺多尺度、多行动、长期与不确定性推演、因果校准和跨区域验证。
+
+## A3. 结果为什么可信，失败时怎么办？
+
+结论：模型编排、真实算法和约束校验分开验证。Gemma 4 + ADK 三类受控场景为 30/30，真实 MPC 另行产生 406 对置换和空间成果；只有面积、坡度、连片度及产物完整性全部通过硬约束校验，经验才允许保存。边界是 30/30 的 Wilson 95% 区间为 88.65%–100%，且规划工具使用确定性替身，不是 30 次算法重跑；失败最多重规划一次，再失败转人工。
+
+## A4. 真实落地证据是什么，哪些还没有证明？
+
+结论：需求、受众、规模、历史环境与当前运行都有分层证据。系统面向 GIS 分析师和自然资源、县域整治人员；当前规划处理 101,657 条输入记录，底层引擎旧版本的内部交接材料记录曾在目标内网使用真实权威数据完成全流程，当前 0.3.3 / 2.2.3 则在本机完成真实 MPC 闭环。边界是这不等于当前版本部内复测或生产验收，也没有公开用户数、节省工时和人工修订率。
+
+## A5. 如何落地、扩展，并保证工程质量？
+
+结论：核心链路可本地离线运行，并按数据契约、区域适配器和工具接口解耦扩展。工程证据分为 236 个最终镜像运行时测试、6 个宿主交付契约和 52 个接口兼容测试，另有 Ruff、编译、前端构建、Compose 解析和 3/3 核心容器健康证据。边界是这些测试集合范围不同，不能直接混成一个“总测试数”，新增地区仍需独立数据检查和硬约束验证。
+
+## 备用 1. 为什么一定要用 Google ADK？
+
+ADK 不是展示用依赖，而是主运行时：它承载 `LlmAgent`、10 个原生函数工具、长任务工具、session 和函数调用事件。Gemma 4 是决策面，ADK 把决策变成结构化调用，PostGIS 与县域耕地优化引擎是行动面。这种分层让模型选择、数值计算和治理审计可以分别评测。
+
+## 备用 2. 为什么选择 Gemma 4 26B，不选 31B？
+
+同一 CQ-125 上，26B 为 113/125，31B 为 114/125，只差 1 题；26B 完整运行 12.50 分钟，31B 为 20.04 分钟，约快 37.6%。这是空间 SQL 准确率与现场延迟的工程权衡，不声称 26B 在统计上显著优于 31B。
+
+## 备用 3. 和普通 NL2SQL 有什么区别？
+
+空间查询还要处理 SRID、`geometry/geography` 单位、距离面积长度、空间谓词和空间 join 去重。系统先做语义与 schema grounding，再由 Gemma 4 生成 SQL，通过只读护栏真实执行并把结果加载到地图。CQ-125 是可复核的空间 SQL 基准，不是几个手工案例。
+
+## 备用 4. Memory 如何提升自主性？为什么不自动更新领域规则？
+
+系统只保存通过硬约束校验且空间产物完整的已验证经验，并记录版本、参数和摘要哈希；下一任务先召回这些经验。Agent 可以提出知识候选，但高风险耕地规则必须带来源、版本和作用域并经人工审批后才能激活。目标是受控自主，而不是让模型无限自我修改。
+
+## 备用 5. 自然资源部测试的到底是什么？
+
+底层县域耕地优化引擎旧版本的内部交接材料记录曾在目标内网以真实权威数据完成全流程；测试对象不是整个 GIS Data Agent。当前系统绑定 0.3.3 / 2.2.3 做本机验证，并复用历史 Prepare / ONNX 产物。不能说当前版本已完成部内复测或生产验收。
+
+## 备用 6. AlphaEarth 和 OKF 是否已经在主链使用？
+
+没有。Google ADK 已进入主链；AlphaEarth 只作为长期空间状态表征的 `Tech Preview`，OKF 只作为人和 Agent 可读的知识交换 sidecar。比赛没有“Google 技术越多越加分”的明示规则，只有能解释必要性、调用位置和证据的技术才会转化为技术分。
+
+## 备用 7. 如何扩展到其他地区？
+
+Agent 与工具接口按数据集解耦。新增地区需要完成数据契约、坐标与编码检查、prepared/ensemble 绑定，并使用该地区的独立硬约束校验进行验收。架构支持扩展，但当前证据不能直接外推为全国稳定性。
+
+## 禁止口径
+
+- 不说“Paper9 是完整通用 GWM”或“MPC 本身就是世界模型”。
+- 不说“v2.2.3 已在自然资源部完成生产验证”。
+- 不说“30/30 证明系统在所有真实问题上 100% 可靠”。
+- 不说“AlphaEarth 或 OKF 已进入生产闭环”。
diff --git a/docs/finals/quality_gate_report.md b/docs/finals/quality_gate_report.md
new file mode 100644
index 00000000..212b3653
--- /dev/null
+++ b/docs/finals/quality_gate_report.md
@@ -0,0 +1,152 @@
+# 决赛质量检查报告
+
+验证日期：2026-07-30
+
+本报告记录决赛关键链路的可重复检查。它不把局部测试集合表述为整个仓库的零缺陷证明，也不把模型编排评测表述为底层规划算法重复运行。
+
+## 1. 结论
+
+| 检查项 | 结果 | 覆盖范围 |
+|---|---:|---|
+| 最终镜像运行时回归 | 236 passed | NL2SQL、WorldModel、Paper9 治理、预检、路由、展示与验证器 |
+| 仓库交付契约 | 6 passed | Compose、环境模板、模型配置、Agent instruction 和脚本入口 |
+| 真实提示词验收 | 2/2 场景 passed | 最终 NL2SQL 连续 3 次；Paper9 锁定路径两次通过且最终代码状态末次通过 |
+| 决赛关键 Python 测试 | 73 passed | Paper9 服务、ADK 工具、治理、展示、路由、预检和部署契约 |
+| 模型网关与工具过滤兼容测试 | 52 passed | thinking 参数、离线模型路由、六字段意图路由和工具分类 |
+| 确定性行为契约 | 5/5 | 首次成功、一次恢复、二次失败转人工、版本阻断、禁止未审计写入 |
+| Ruff | passed | 9 个决赛关键模块与脚本，包含 PPT 构建器 |
+| Python 编译 | passed | 9 个决赛关键模块与脚本，包含 PPT 构建器 |
+| 前端生产构建 | passed | TypeScript + Vite，4,134 个模块完成转换 |
+| Compose 配置解析 | passed | `docker-compose.gemma4-demo.yml` |
+| 运行中容器 | 3/3 healthy | app、PostGIS、Redis |
+| 主机只读预检 | 6/6 passed | Paper9 版本、prepared、ONNX ensemble、Gemma 4 标签 |
+| PPT/PDF 包完整性 | passed | 11 页、11/11 speaker notes、11/11 视觉检查；业务截图与 406 结果一致，PPTX/PDF 哈希校验通过 |
+| `git diff --check` | passed | 决赛范围差异的空白与冲突标记检查 |
+
+## 2. 精确测试集合
+
+最终镜像运行时回归：
+
+```bash
+docker exec gisdataagent-app-1 python -m pytest \
+ data_agent/test_finals_demo_verifier.py \
+ data_agent/test_finals_preflight.py \
+ data_agent/test_nl2sql_executor.py \
+ data_agent/test_nl2sql_semantic_rewrite.py \
+ data_agent/test_nl2sql_tools.py \
+ data_agent/test_nl2sql_toolset_registration.py \
+ data_agent/test_paper9_agent_evaluation.py \
+ data_agent/test_paper9_agent_governance.py \
+ data_agent/test_world_model_v21.py \
+ data_agent/test_world_model_v21_presentation.py \
+ data_agent/test_world_model_v21_routes.py \
+ data_agent/test_world_model_v21_tools.py -q
+```
+
+结果：`236 passed, 5 warnings`。仓库根文件不打包进运行镜像，因此部署契约在宿主仓库单独运行，
+结果为 `6 passed`；没有用 skip 掩盖测试分层。
+
+精确真实提示词验收：
+
+```bash
+docker exec gisdataagent-app-1 python scripts/verify_gemma4_finals_demo.py \
+ --nl2sql-repeats 3 \
+ --paper9-timeout 360 \
+ --output /tmp/verified_finals_demo_report.json
+```
+
+最终报告 `passed=true`：NL2SQL 三次均为 `35`；县域耕地规划 Agent 严格六次函数调用，指标来自
+同一个最终运行目录，硬约束校验、空间产物和已验证经验写入全部通过。历史锁定路径耗时为
+`93.490s / 112.940s`；新版中文 UI 浏览器运行总用时 `88.6s`、MPC 规划 `73.7s`，业务指标一致。
+
+决赛关键测试命令：
+
+```bash
+.venv/bin/python -m pytest -q \
+ data_agent/test_world_model_v21.py \
+ data_agent/test_world_model_v21_tools.py \
+ data_agent/test_world_model_v21_presentation.py \
+ data_agent/test_world_model_v21_routes.py \
+ data_agent/test_paper9_agent_governance.py \
+ data_agent/test_paper9_agent_evaluation.py \
+ data_agent/test_finals_preflight.py \
+ data_agent/test_finals_deployment_contract.py
+```
+
+结果：`73 passed, 1 warning`。警告来自 OpenTelemetry 依赖的弃用提示，不影响测试结论。
+
+接口兼容测试命令：
+
+```bash
+.venv/bin/python -m pytest -q \
+ data_agent/test_model_gateway.py \
+ data_agent/test_tool_filter.py
+```
+
+结果：`52 passed, 3 warnings`。测试已与当前 DeepSeek thinking 配置、Gemma 4 离线优选和六字段意图路由接口对齐。
+
+## 3. 其他可重复检查
+
+```bash
+uvx ruff check \
+ data_agent/finals_preflight.py \
+ data_agent/paper9_agent_governance.py \
+ data_agent/paper9_agent_evaluation.py \
+ data_agent/paper9_agent_prompt.py \
+ data_agent/world_model_v21_presentation.py \
+ scripts/check_gemma4_finals_preflight.py \
+ scripts/run_paper9_adk_reliability_eval.py \
+ scripts/run_paper9_finals_contract_eval.py \
+ scripts/build_gemma4_finals_ppt.py
+```
+
+结果：`All checks passed!`
+
+```bash
+.venv/bin/python scripts/run_paper9_finals_contract_eval.py
+```
+
+结果：`5/5`，报告写入 `data_agent/demo_evidence/paper9/finals_20260730/behavior_contract_report.json`。
+
+```bash
+cd frontend && npm run build
+```
+
+结果：构建成功。当前仍有 loader `spawn` 导出提示和大于 500 kB 的 chunk 警告，因此不能表述为“零警告构建”。
+
+```bash
+docker compose -f docker-compose.gemma4-demo.yml config --quiet
+docker compose -f docker-compose.gemma4-demo.yml ps
+```
+
+结果：配置解析通过；app、db、redis 均为 `healthy`。正式换机时仍应先复制 `.env.finals.example` 为 `.env.finals` 并填写主机绝对路径。
+
+PPT 交付检查：
+
+```bash
+uv run python scripts/build_gemma4_finals_ppt.py
+unzip -t docs/finals/GIS_Data_Agent_Gemma4_Finals_CN.pptx
+pdfinfo docs/finals/GIS_Data_Agent_Gemma4_Finals_CN.pdf
+```
+
+结果：PPTX 为 11 页且 11 页均含 speaker notes；ZIP 包无损坏；LibreOffice 成功导出 11 页、
+16:9 PDF。主 6 页与附录 5 页逐页检查无重叠和裁切，主路演不含旧版指标或未解释的内部代号；
+`release_manifest.sha256` 已按最终 PPTX/PDF 重建并通过校验。
+
+## 4. 与模型和算法证据的关系
+
+| 证据 | 数量 | 证明对象 |
+|---|---:|---|
+| 确定性行为契约 | 5/5 | 治理状态机和经验写入规则 |
+| 真实 Gemma 4 26B + ADK | 30/30 | 三类受控场景中的工具选择、停止和恢复 |
+| 真实 MPC 规划 | 锁定路径 2 次 + 新版浏览器 1 次 | 当前 0.3.3 / 2.2.3 代码绑定、MPC 执行、空间产物、硬约束校验和经验写入 |
+
+30/30 的总体 Wilson 95% 区间为 88.65%–100%，规划工具在该评测中使用确定性替身。真实 MPC 运行复用了历史数据准备与 ONNX 产物。详细主张边界见 [claim_register.md](claim_register.md)。
+
+## 5. 已知非阻断项
+
+- 前端主包较大，赛后应继续拆分；当前不影响本机决赛演示。
+- 运行时回归保留 5 个 ADK/asyncio 依赖弃用警告；真实 MPC 保留空分组除法 RuntimeWarning，均未中止验收，不能表述为零警告。
+- 恢复的数据库卷含非决赛 UWM migration ownership 日志；不能描述为全仓库零警告运行。
+- 仍需按 [rehearsal_log.md](rehearsal_log.md) 完成真实投影设备上的三轮计时彩排。
+- 完整六次函数调用与非空地图截图已自动留存；该截图生成于快速调用耗时精度调整前，只作链路证据。五个视频尚未录制，最终录制前需按最新版演示脚本人工复跑并覆盖截图。
diff --git a/docs/finals/rehearsal_log.md b/docs/finals/rehearsal_log.md
new file mode 100644
index 00000000..d5ec3db5
--- /dev/null
+++ b/docs/finals/rehearsal_log.md
@@ -0,0 +1,40 @@
+# 决赛彩排与现场验收记录
+
+本文件只记录真实执行结果。没有完成的项目保留“待执行”，不得用计划值代替实测值。
+
+## 三轮完整彩排
+
+| 轮次 | 日期/场地 | 总时长 | Demo 路径 | 切换最慢耗时 | Q&A 题目 | 发现问题 | 结论 |
+|---|---|---:|---|---:|---|---|---|
+| 1 · 内容轮 | 待执行 | - | 实时优先 | - | 3 题 | - | 待执行 |
+| 2 · 故障轮 | 待执行 | - | 主动切视频/静态证据 | - | 3 题 | - | 待执行 |
+| 3 · 场地轮 | 待执行 | - | 决赛设备与投影 | - | 3 题 | - | 待执行 |
+
+## 放行标准
+
+| 维度 | 必须达到 | 结果 |
+|---|---|---|
+| 路演时长 | 三轮均 `<= 4:45`，目标 `4:40` | 待执行 |
+| 故障切换 | 模型、数据库、前端任一故障均在 `<= 5s` 进入备份证据 | 待执行 |
+| 可读性 | 最后一排可读第 3、4、5 页最小文字与地图图例 | 待执行 |
+| 离线能力 | 断网后 PPT、五个 MP4、PostGIS 快照和县域耕地优化真实产物可打开 | 待执行 |
+| 主张一致性 | PPT、口播、字幕、README 无越界表述 | 待执行 |
+| 工程预检 | `ready=true`；app/PostGIS/Redis `3/3 healthy` | 待执行 |
+| Q&A | 任取三题，单题 `15-25s`，先结论再证据和边界 | 待执行 |
+
+## 每轮必测故障
+
+1. 模型首 token 超过 8 秒：不等待第二次，切 `03_farmland_planning_success_70s.mp4`。
+2. 县域优化工具报错：切封存的 Tool 4 地图、审计 JSON 和 verified episode，不现场重跑高成本任务。
+3. PostGIS 不可用：切 `02_nl2geosql_locked_35s.mp4`，明确使用固定数据库快照。
+4. 前端空白：直接回 PPT 第 3 或第 4 页，不打开开发者工具。
+5. 完全断网：确认 Gemma 4、PostGIS、县域耕地优化主链不依赖公网；Google 扩展预览不进入主 Demo。
+
+## 场地交付清单
+
+- [ ] 演示电脑本机：PPTX、PDF、五个 MP4、关键 PNG、预检 JSON。
+- [ ] U 盘：同一份只读副本，目录结构与本机一致。
+- [ ] 浏览器：已登录、固定缩放、关闭自动填充弹窗与通知。
+- [ ] 投影：16:9、1080p、镜像/扩展模式已确认，地图红绿标记可区分。
+- [ ] 电源与网络：电源适配器、有线/无线备份；断网路径已演练。
+- [ ] 计时：演讲者可见倒计时，`4:20` 强制进入总结页。
diff --git a/docs/finals/release_manifest.sha256 b/docs/finals/release_manifest.sha256
new file mode 100644
index 00000000..7bbd371f
--- /dev/null
+++ b/docs/finals/release_manifest.sha256
@@ -0,0 +1,43 @@
+e8bb1c4dad6b04bf0cc6eca168bb86de6b0db4c6d1f40ebe53646fd18aaac95d GIS_Data_Agent_Gemma4_Finals_Technical_Handbook_CN.md
+689721bf3ae74e69762c75023536d46f71c9be0137a58012c43f5e41f90fcf55 README.md
+33ae9ceabae0002cd99615e2997538560b5b6ffbbcfc76889eddf143add71016 claim_register.md
+ec4f534c6e46028a1bf21a5669715a15e300b6d28e1927ba2dd097b876761645 demo_runbook.md
+9e8f2f583953f43c1388eb70f83f43d2e075d45ebd20bf9fb170ecb5968326de deployment.md
+9295048373989a2b88deee11421c5b12da04b30f83374218c0f11af6c193e92a pitch_script.md
+3bd8a7aecfd9bde65cdcfbfa07e7a5a1c90638bea6acdfafa301dd38f179afe1 qa.md
+5633eb2d6c45139b3772223abe874b40e95a377d334a88a811865fd46e9e7f37 quality_gate_report.md
+825329e4df0d8eba6c33aa9a5050406557d07b7f11138dc0391653c5544fc189 rehearsal_log.md
+4b7a5a76c32bd9eff45963a20962a57142a31f5793c60a4dbcca08674756f673 scoring_evidence_matrix.md
+3ecee80cf24b171c7a636ff31c5a0ac2af192637da923df9eaa2f9d4542d9d3f technical_report.md
+925324baa8d12e9fa4402246c6b58a4e6b8e94bcd52f30cb6e920f8e07790ed9 verified_demo_script.md
+1b024a4e17040b6b3883dc14ea7d48efcc52c32d7c9e33e3ecbbacf35a1974a8 video_script.md
+43b7825c413eca1f182ece8285364140b6d47c806521a990b2b34cc579c455b5 GIS_Data_Agent_Gemma4_Finals_CN.pptx
+3b1097b8d2a3a49880bac9b9980477efa86fb2da8a63409e666abe00b60aa977 GIS_Data_Agent_Gemma4_Finals_CN.pdf
+eb280638b94d536c03193c5a8830db4f0fefe1ddc2fee9dbc31caf9e480dbc22 GIS_Data_Agent_Gemma4_Finals_Technical_Handbook_CN.pdf
+b4604582a11ad956a659fb493beccfaa07f0151dac7bd42117cc100752908554 GIS_Data_Agent_Gemma4_Finals_Technical_Handbook_CN.docx
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+01b80a897f139facc5e37981192c693314d25957ad10dedb1d0e56c8cfd759e7 assets/paper9_tool4_change_map.png
+deedc82d85eed5f1a053304ecff464921e25e08baa95cbd682b297c97ffac7b8 assets/diagrams/adk_tool_trajectories.png
+92e7ba08c4aa828035e0ad8ccf060cc5d2cff20fe8bbbbcc972b4bf588b8532b assets/diagrams/architecture_overview.png
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+3e76d214df663a308ec609403e3700f6f7f01d1e4324c89548c4f141ba93e611 assets/diagrams/knowledge_memory_loop.png
+f3197df665c53a93edae0b55d19b6e1db91a8c725dcc7fed69384a754e50b691 assets/diagrams/nl2semantic2geosql_pipeline.png
+8ce12e725935f529f7c2057c8364e6d08d0619a941d46ce2282bdcf4bd73c8fd assets/diagrams/transition_network.png
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+c083b4cfdea231d28f7048c6b5b0109b6827f1f641b278e8e4798d42fbe71bc4 assets/diagrams/adk_tool_trajectories.svg
+8487dd276476c043d85e49560b432404380f3f50c5d03b83ec94bd276c6bf973 assets/diagrams/architecture_overview.svg
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+0124a7626a6e7e34d34924b337a03effccf2c71c1462baf6d83b67e786de8d5d assets/diagrams/nl2semantic2geosql_pipeline.svg
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+8ed6a4962063901526378727ae6a4a2b3850b6f7716468d34b007e3e3a7e682d evidence/world_model_bishan_20260730_155442/README.md
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diff --git a/docs/finals/scoring_evidence_matrix.md b/docs/finals/scoring_evidence_matrix.md
new file mode 100644
index 00000000..64a47feb
--- /dev/null
+++ b/docs/finals/scoring_evidence_matrix.md
@@ -0,0 +1,64 @@
+# 决赛评分证据矩阵
+
+更新时间：2026-07-30
+
+## 1. 真实影响力 30%
+
+| 评审关注 | 当前证据 | 路演位置 | 状态与缺口 |
+|---|---|---|---|
+| 是否解决真实需求 | GIS 智能问数解决空间谓词、单位、SRID、空间连接等落地难点；县域耕地空间优化引擎解决耕地布局的面积、坡度与连片度约束 | 第 1、3、4 页 | 已有技术与运行证据；仍缺公开的用户数量和节省工时统计 |
+| 受众是否清晰 | 国土空间规划、自然资源调查、GIS 数据分析和县域耕地整治人员 | 第 1 页 | 路演必须说具体岗位，不能只说“政府用户” |
+| 是否可量化 | CQ-125：Gemma 4 26B 为 113/125；当前 MPC 处理 101,657 条输入并给出面积、坡度、连片度和百亩方面积变化 | 第 2、4 页 | 技术规模已量化；用户数、节省工时与人工修订率仍缺 |
+| 是否有真实环境证据 | 底层优化引擎内部交接材料记录历史版本在目标内网和真实权威数据上运行；当前 v2.2.3 本机 MPC 另行验证 | 第 4 页、Q&A | 必须区分历史内网记录和当前本机验证，不声称 v2.2.3 已部内复测 |
+| 是否可扩展 | 语义层、PostGIS 工具、规划引擎适配器和已验证经验库均按数据集/区域解耦 | 第 5 页 | 架构可扩展不等于已跨全国验证 |
+
+## 2. 技术卓越度 25%
+
+| 评审关注 | 当前证据 | 代码或报告 | 状态 |
+|---|---|---|---|
+| Gemma 4 使用深度 | 26B 基于五规格 CQ-125 选型；真实 ADK 运行控制 2/6/8 工具分支 | `model_gateway.py`, `adk_reliability_report.json` | 已就绪 |
+| 原生函数调用 | 10 个 ADK `FunctionTool` / `LongRunningFunctionTool`，参数和响应均结构化 | `toolsets/world_model_v21_tools.py` | 已就绪 |
+| 多步规划 | `状态检查 -> 资源检查 -> 经验召回 -> MPC 规划 -> 硬约束审计 -> 经验写入/重规划/人工` | `paper9_agent_prompt.py` | 已就绪；与 MPC 数值规划明确分层 |
+| GWM 技术关系 | 状态 `S`、受限行动 `A`、学习型转移预测 `Tθ`、MPC 规划器与治理外环分层 | 第 4、6 页、附录 A2、[technical_report.md](technical_report.md) | 已讲清；当前只称领域原型，不称完整通用 GWM |
+| 架构设计 | LLM 决策、ADK 运行时、确定性 GIS 行动、审计治理和知识层分离 | [technical_report.md](technical_report.md) | 已就绪 |
+| 代码质量 | 版本 fail-closed、纯函数审计、幂等 append-only memory、schema version、SHA-256、CI lint/test/eval | 最终镜像 236 个运行时测试、宿主 6 个交付契约、52 个兼容测试、[quality_gate_report.md](quality_gate_report.md) | 已就绪；三类测试范围不同，不直接混加；保留非阻断警告 |
+| 可靠性统计 | 30/30；总体 Wilson 95% 区间 88.65%–100%；每场景轨迹一致性 100% | `adk_reliability_report.json` | 基线已建立，仍应持续增加变体输入 |
+
+## 3. 功能完备性 20%
+
+| 评审关注 | 当前证据 | 现场动作 | 状态 |
+|---|---|---|---|
+| Demo 可运行 | Compose 可解析；app/PostGIS/Redis 3/3 healthy；主机预检 6/6 | 演示前执行预检并保存 JSON | 已就绪 |
+| 核心功能完整 | NL2Semantic2GeoSQL 可执行；县域耕地规划 Agent 可规划、审计、生成地图并提交已验证经验 | NL2SQL 可实时；县域规划播放真实母版的 70 秒等待段加速版 | 已就绪 |
+| 报告交付 | 同一次规划生成约 5 页 A4 图文 PDF，含指标看板、真实变化地图、六步调用轨迹、逐函数用时和审计表 | Demo 后点击“导出 PDF 报告”，附录 A4/A5 | 已完成自动验收；录制前人工复核 PDF 与当前会话一致 |
+| 边缘情况 | 版本不兼容停止、首次审计失败仅重规划一次、二次失败转人工、缺空间产物拒绝提交 | 视频或附录轨迹 | 已就绪 |
+| 可复现 | `.env.finals.example`、Docker Compose、部署文档和固定版本 | 代码仓库 | 已就绪 |
+| 演示降级 | 预录视频、已保存真实产物、静态轨迹和截图四级备份 | [demo_runbook.md](demo_runbook.md) | 需在最终场地完成三轮彩排 |
+
+## 4. 创新性 15%
+
+| 创新点 | 与普通方案的差异 | 证据 | 表述边界 |
+|---|---|---|---|
+| NL2Semantic2GeoSQL | 不只生成 SQL，而是做空间语义 grounding、只读执行和结果校验 | CQ-125、真实 PostGIS 查询 | 不说成“所有空间问题都准确” |
+| LLM + GWM 领域原型 | Gemma 4 负责任务决策；学习型状态转移模型预测行动后果；MPC 比较方案；硬约束校验控制结果保存 | 第 4、6 页、附录 A2、真实 MPC 产物 | 最大创新主张；当前不是完整通用 GWM，MPC 也不是世界模型本身 |
+| 受控自主性 | LLM 可选择、观察和恢复；确定性代码守住版本与业务约束 | 2/6/8 工具分支重复评测 | 不把固定 A/B/C/D 内部流程说成 LLM 动态规划 |
+| 受治理经验库 | 只有通过硬约束校验且产物完整的经验可复用 | append-only store 与禁止写入测试 | 当前不是自主改写高风险领域规则 |
+| Google 扩展生态 | ADK 为主运行时；AlphaEarth 与 OKF 提供未来表征和知识交换方向 | 代码与架构 | AlphaEarth=`Tech Preview`；OKF=`sidecar` |
+
+## 5. 演示表现 10%
+
+| 评审关注 | 交付 | 完成标准 |
+|---|---|---|
+| 技术报告清晰 | [technical_report.md](technical_report.md) | 所有关键数字可追溯到文件 |
+| 视频专业 | [video_script.md](video_script.md) 定义 4:40 主备视频、35 秒问数、70 秒成功链、45 秒恢复和 15 秒阻断 | 镜头与字幕脚本已就绪；五个成片仍待录制 |
+| 代码文档完善 | README、部署、claim register、Q&A、质量检查报告、CI | 新机器按文档可完成预检 |
+| Q&A 清晰 | 附录 A1–A5 分别回答规划归属、GWM 边界、可信与失败、落地证据、工程扩展 | 每题 15–25 秒，先结论、再证据、最后边界 |
+| 现场节奏 | 6 页主 PPT，5:00 内结束 | 三次彩排均不超过 4:45，留 15 秒缓冲 |
+
+## 决赛前剩余硬门槛
+
+1. 使用有效本地 UI 凭据，从当前健康 Compose 环境保存完整 6 工具轨迹与 5 页图文报告截图。
+2. 按 [video_script.md](video_script.md) 录制五个主备片段；异常分支只在预录评测中展示。
+3. 唯一 NL2Semantic2GeoSQL 问题已经锁定；录制时冻结返回 `35` 的数据库快照并记录校验信息。
+4. 按 [rehearsal_log.md](rehearsal_log.md) 完成三轮彩排，并记录时长、投影可读性与故障切换。
+5. 将 PPTX、PDF、MP4 和关键 PNG 复制到 U 盘，并在离线设备上核对 SHA-256 与播放能力。
diff --git a/docs/finals/technical_report.md b/docs/finals/technical_report.md
new file mode 100644
index 00000000..9022e5f6
--- /dev/null
+++ b/docs/finals/technical_report.md
@@ -0,0 +1,196 @@
+# GIS Data Agent 决赛技术报告
+
+版本：2026-07-30 finals baseline
+
+本文件是决赛技术报告短版。现场遇到模型、ADK、GeoSQL、GWM、MPC、受控自主、记忆、审计、部署或工程质量的深入追问时，使用 [GIS_Data_Agent_Gemma4_Finals_Technical_Handbook_CN.md](GIS_Data_Agent_Gemma4_Finals_Technical_Handbook_CN.md)。
+
+## 摘要
+
+GIS Data Agent 面向两类真实地理空间任务：高频的自然语言空间问数，以及低频但高价值的耕地空间布局优化。系统使用 Gemma 4 26B 理解目标和选择工具，使用 Google ADK 承载函数调用与 Agent 运行时，并将 PostGIS、GIS 与县域耕地空间优化引擎封装为确定性行动工具。该优化引擎的内部研发代号为 `Paper9`，面向评委统一使用业务名称。
+
+决赛版本的核心改进是把县域耕地空间优化从固定流水线升级为受控自主闭环。Gemma 4 可以根据版本、资源和审计反馈选择提交、一次重规划或转人工；代码强制禁止未审计结果进入经验记忆。当前三类行为分支共完成 30 次真实 Gemma 4 + ADK 运行，30/30 通过契约。
+
+## 1. 问题与用户
+
+目标用户是自然资源与国土空间规划人员、县域耕地整治人员和 GIS 数据分析师。
+
+他们面对两个共性问题：
+
+- 空间问数不是普通 SQL。距离、面积、长度、SRID、`geometry/geography`、空间谓词和空间连接去重都会造成可执行错误或语义错误。
+- 空间规划不是文字建议。结果必须满足面积不减少、坡度下降、连片度提升等硬约束，并生成可审查的空间成果。
+
+因此系统不让大模型直接编造 GIS 结果，而是让大模型负责决策与编排，让领域工具负责计算和验证。
+
+## 2. Gemma 4 选型
+
+模型选择基于同一主机和同一 CQ-125 PostGIS 基准：
+
+| 模型 | 执行正确率 | 完整时间 |
+|---|---:|---:|
+| Gemma4:12b | 107/125 = 85.6% | 16.45 min |
+| Gemma4:26b | 113/125 = 90.4% | 12.50 min |
+| Gemma4:31b | 114/125 = 91.2% | 20.04 min |
+
+26B 只比 31B 少 1 道正确题，但整体运行约快 37.6%，适合严格 5 分钟的现场工具调用。该结果是固定环境部署探针，不声称 26B 在统计上显著优于 31B。
+
+本地运行通过 `ollama_chat/Gemma4:26b` 接入 ADK/LiteLLM，并固定 `think=false`，避免模型思维流占满输出预算或导致工具回合空响应。
+
+## 3. 系统架构
+
+```text
+用户目标
+-> Gemma 4 26B：理解、选择、解释
+-> Google ADK：LlmAgent、Toolset、FunctionTool、运行事件
+-> PostGIS / GIS / 县域耕地空间优化引擎：确定性执行
+-> 版本兼容性检查 / 硬约束校验：确定性验证
+-> Verified Memory：只保存通过结果
+-> UI / Map / Report / Trace：可审计呈现
+```
+
+职责边界：
+
+| 层 | 责任 |
+|---|---|
+| Gemma 4 | 数据集识别、工具选择、根据反馈选择下一动作、结果解释 |
+| Google ADK | Agent 生命周期、函数声明、调用事件、session 与运行轨迹 |
+| 县域耕地空间优化引擎 / PostGIS / GIS | SQL 执行、状态转移预测、MPC 和空间成果生成 |
+| 治理代码 | 版本兼容性检查、硬约束校验、重规划次数、经验保存权限 |
+| 知识与记忆 | 为下一任务提供已验证上下文，不直接覆盖高风险规则 |
+
+Google ADK 是主架构的一部分，不是附加 Logo。AlphaEarth 只作为地理表征扩展的 `Tech Preview`；OKF 只作为知识交换 sidecar。
+
+## 4. 两类核心场景
+
+### 4.1 NL2Semantic2GeoSQL
+
+链路为：
+
+```text
+自然语言问题
+-> semantic/schema grounding
+-> Gemma 4 SQL 生成
+-> 空间语义改写与只读护栏
+-> PostGIS 真实执行
+-> 结果和地图
+-> 经验证成功案例进入 reference queries
+```
+
+差异化来自空间语义与执行正确性，不是普通的文本到 SQL。CQ-125 同时承担模型选型和回归基准的作用。
+
+### 4.2 县域耕地空间优化与 GWM 领域原型
+
+县域耕地空间优化引擎使用 learned transition model ensemble 与 MPC 做 model-based spatial planning。决赛版本向 Gemma 4 暴露 10 个 ADK 工具：状态、资源检查、经验召回、Prepare、Sample、Train、Plan、Pipeline、审计和提交经验。
+
+它与地理空间世界模型的关系必须分层说明：
+
+| 组成 | 当前实现 | 与 GWM 的关系 |
+|---|---|---|
+| 状态 `S` | 图斑、空间块、地类、坡度和连片度等状态 | 领域状态表示 |
+| 行动 `A` | 候选地类置换 | 当前受限行动空间 |
+| 转移模型 `Tθ` | 学习型状态转移模型集成预测行动后果 | GWM 动力学内核雏形 |
+| 规划器 | MPC 消费预测并比较候选方案 | 世界模型的消费者，不是世界模型本身 |
+| 治理外环 | 版本兼容性检查、硬约束审计、保存或转人工 | 决策治理外环 |
+
+因此当前整体应称为“领域化 GWM 原型”，不能称为完整通用地理空间世界模型。完整 GWM 仍需多尺度状态、多行动动力学、长期 rollout、不确定性估计、因果校准和跨区域验证。
+
+首次成功轨迹：
+
+```text
+status -> inspect -> recall -> pipeline -> audit -> commit
+```
+
+恢复轨迹：
+
+```text
+status -> inspect -> recall -> pipeline -> audit(失败)
+-> plan -> audit -> commit 或转人工
+```
+
+自主性来自 Gemma 4 根据工具反馈选择分支；A/B/C/D 仍是确定性算法阶段。二者必须明确区分。
+
+每次通过的规划可导出约 5 页 A4 图文报告，包含规划成效看板、按 `CHG_FLAG` 渲染的变化地图、六步原生函数调用轨迹、逐函数用时、审计表和交付物摘要；报告数据直接来自同一次运行产物，不从聊天文本推断。
+
+## 5. 自主性、记忆与知识循环
+
+系统将自主性限定在可审计边界内：
+
+- 版本或资源不兼容时停止，不降级为旧算法后宣称成功。
+- 审计失败最多允许一次重规划，禁止无限循环。
+- 耕地面积、坡度和连片度由确定性代码审计，不由 LLM 自评。
+- 空间产物缺失时 fail closed。
+- 只有通过硬约束校验且成果完整的 episode 能进入 append-only memory。
+- 同一 episode 提交幂等，记录版本、参数、输出目录和摘要 SHA-256。
+- 高风险领域规则只能由 Agent 提出候选，不能自主激活。
+
+这形成一个有限闭环：
+
+```text
+validated context -> plan -> act -> observe -> audit
+-> replan/escalate -> verified commit -> next-task recall
+```
+
+## 6. 真实验证
+
+### 6.1 Gemma 4 + ADK 编排
+
+三类场景各运行 10 次：
+
+| 场景 | 通过 | 轨迹一致性 | 平均延迟 | P95 |
+|---|---:|---:|---:|---:|
+| 首次审计通过 | 10/10 | 100% | 7.97 s | 9.53 s |
+| 版本不兼容停止 | 10/10 | 100% | 3.24 s | 4.88 s |
+| 一次重规划后通过 | 10/10 | 100% | 13.57 s | 15.31 s |
+
+总体 30/30，Wilson 95% 区间为 88.65%–100%。工具响应使用确定性替身，以隔离测量模型和 ADK 的工具编排；它不等于 30 次县域耕地优化算法计算。
+
+### 6.2 县域耕地空间优化引擎真实 Tool 4
+
+当前绑定：
+
+- 包：`paper9-mnr-offline-package 0.3.3`
+- 算法：`paper9v2 2.2.3`
+- commit：`848514383948474e4b6387f171aea8a9272821db`
+- 101,657 条输入空间记录，53,004 个环境图斑，2,640 个 blocks
+- 100 个 MPC 步骤，406 对置换
+- 耕地面积 `+1.935936 ha`
+- 平均坡度 `-0.607765%`
+- 连片度 `+0.020184`
+- 百亩方面积 `+35.131619 ha`
+- 硬约束校验通过
+- 锁定路径两次真实 Gemma 4 + ADK 六工具闭环通过，总耗时 `93.490s / 112.940s`；末次对应最终代码状态
+
+该运行使用当前 0.3.3 / 2.2.3 代码执行 Tool 4，并由同一次 Agent 轨迹完成审计与 verified memory 提交；
+但它复用了旧流程生成的 Prepare / ONNX 产物，输入被识别为旧三位测试编码。它不是 v2.2.3
+真实权威四库的完整重训证据。
+
+## 7. 工程质量
+
+- `paper9_agent_governance.py` 与 ADK 解耦，核心审计可做确定性单元测试。
+- JSON 报告均带 schema version；关键摘要保存 SHA-256。
+- 版本、资源、输出缺失均 fail closed。
+- verified memory 为 append-only、幂等写入并执行 `fsync`。
+- CI 使用 Python 3.11，运行 Ruff、行为契约、单元测试和前端构建。
+- live ADK 随机评测不放入无 Ollama 的普通 CI；其报告作为版本化基线保存。
+
+2026-07-30 决赛质量检查结果：
+
+| 检查 | 结果 |
+|---|---:|
+| 最终镜像运行时回归 | 236 passed |
+| 宿主交付契约 | 6 passed |
+| 模型网关与工具过滤兼容测试 | 52 passed |
+| 确定性行为契约 | 5/5 |
+| Ruff / Python 编译 | passed / passed |
+| 前端生产构建 / Compose 解析 | passed / passed |
+| 运行中容器 | 3/3 healthy |
+
+另有 `73 passed` 的决赛关键子集用于快速复核；它与上述集合存在范围关系，不能累加为总测试数。前端仍有大 chunk 和 loader 导出警告，依赖仍有弃用提示，因此本报告不声称零警告。完整命令与范围见 [quality_gate_report.md](quality_gate_report.md)。
+
+## 8. 局限与后续
+
+- 30 次编排基线仍是受控输入，不替代长尾用户问题和负载测试。
+- 当前 v2.2.3 本机验证不是 A/B/C/D 全量重训，也不是部内复测。
+- 自然资源历史内网证据属于底层县域耕地优化项目，不等于 GIS Data Agent 部署验收。
+- AlphaEarth 尚处于 Tech Preview；OKF 尚未自动驱动知识晋升。
+- 当前仅为领域化 GWM 原型，不具备完整通用 GWM 的多尺度、多行动、长期与不确定性推演能力。
+- 下一阶段需要增加提示词变体、超时/缺产物、二次失败转人工和前端端到端测试。
diff --git a/docs/finals/verified_demo_script.md b/docs/finals/verified_demo_script.md
new file mode 100644
index 00000000..e38be931
--- /dev/null
+++ b/docs/finals/verified_demo_script.md
@@ -0,0 +1,181 @@
+# 决赛唯一最新版演示脚本
+
+更新时间：2026-07-30
+适用版本：Gemma 4 26B + Google ADK + 县域耕地空间优化引擎 `0.3.3 / 2.2.3`
+状态：新版中文输出已完成真实浏览器验收；等待录制前人工 UI 复核
+
+本文件是当前唯一的演示操作与口播基线。若其他旧文档中的数字、时长或提示词与本文件冲突，以本文件和
+[`verified_finals_demo_report.json`](../../data_agent/demo_evidence/paper9/finals_20260730/verified_finals_demo_report.json)
+为准。
+
+> 重要：最终截图、录屏和 PPT 必须使用同一份 `406` 对置换证据。每次人工复跑都会产生新的
+> 经验编号和实际用时，字幕只能引用录制母版当次界面显示的数字。
+
+## 1. 已自动验证的事实
+
+2026-07-30 使用与界面相同的两个提示词，在决赛 Docker 环境中完成真实验证：
+
+| 项目 | 已验证结果 |
+|---|---|
+| 环境预检 | 6/6 通过；规划引擎 0.3.3、算法 2.2.3、Bishan prepared、3 个 ONNX 成员、Gemma4:26b 均就绪 |
+| NL2SQL | 最终验收连续 3 次通过，均返回 `35`；延迟 `14.414 / 6.761 / 6.884s` |
+| GeoSQL 语义 | 使用 `ST_DWithin`、`geography`、`100m` 和锁定的 roads/POI 表 |
+| 县域耕地规划 Agent | 新版浏览器运行通过；严格六次函数调用，无重规划，地图非空 |
+| 规划数据规模 | 输入 `101,657` 条，其中 `53,004` 条进入县域环境，共 `2,640` 个空间块 |
+| MPC 优化 | 耕地 `+1.9359 ha`，坡度 `-0.6078%`，连片度 `+0.02018`，百亩方面积 `+35.13 ha` |
+| 空间交换 | 耕地转林地 `406`，林地转耕地 `406`，等量双向交换 |
+| 治理闭环 | 硬约束校验通过，空间产物存在，已验证经验成功保存 |
+| 最新浏览器运行 | 总用时 `88.6s`，MPC 规划 `73.7s`；经验编号 `9cfab6f00ec9a551dfc3` |
+| 历史总耗时 | `93.490s / 112.940s`；仅用于估算等待时间，录屏以当次界面真实用时为准 |
+
+说明：人工复跑会产生新的 `out_dir`、时间戳和 episode ID，这是正常现象；其余锁定结果应一致。当前
+Bishan 输入为旧三位测试编码，不得描述为最新真实权威四库数据。
+
+最新 UI 证据：`tests/e2e/artifacts/world_model_v21_finals_2026_07_30/response.txt` 与
+`response_top.png`。截图同时显示中文 Agent 轨迹、实际总用时和右侧非空规划地图。该截图生成于
+快速调用耗时精度调整前，只作链路证据，不直接进入最终录屏；录制前复跑会覆盖为毫秒级显示。
+
+## 2. 录制前只运行这两条命令
+
+在仓库根目录下执行：
+
+```bash
+docker compose -f docker-compose.gemma4-demo.yml up -d --build app
+docker exec gisdataagent-app-1 python scripts/check_gemma4_finals_preflight.py \
+ --output /tmp/gemma4_finals_preflight.json
+```
+
+开始人工验证的硬门槛：预检输出必须为 `"ready": true` 且 `failed_checks` 为空；
+`docker compose -f docker-compose.gemma4-demo.yml ps` 中 app、db、redis 必须为 `healthy`。
+
+浏览器打开：`http://127.0.0.1:8000`。使用新会话，浏览器缩放建议 125%，关闭通知和任何可能显示密钥的终端窗口。
+
+## 3. 人工验证一：NL2Semantic2GeoSQL
+
+在聊天框原样输入，不增加解释性文字：
+
+```text
+@NL2SQL 统计距离道路网络中最长桥梁100米范围内的高德POI数量。
+```
+
+必须逐项确认：
+
+- 路由到 `MentionNL2SQL`，实际工具为 `run_nl2semantic2sql`。
+- 状态为成功，结果为 `35`。
+- 最终 SQL 同时包含 `cq_osm_roads_2021`、`cq_amap_poi_2024`、`ST_DWithin`、`geography` 和 `100`。
+- 最长桥梁 CTE 仍查询 roads 表，不能被语义修正误换成 POI 表。
+- 右侧地图自动加载三层真实 PostGIS 结果：高德 POI `35` 个、最长桥梁 `1` 条、100 米范围 `1` 个；图层数与标量结果必须一致。
+- 地图摘要中的最长桥梁应为 `osm_id=708725252`（龙溪河大道），长度约 `2167.57 m`；三个图层来自同一条 PostGIS 伴随查询快照。
+
+截图：自然语言问题、关键 SQL、结果 `35` 和右侧三层地图必须同屏；展开地图图层控制，保留 `35 个` POI 的图层名称。不要截图密钥、连接串、绝对用户路径或内部思考文本。
+
+判退条件：结果不是 35、缺任一关键空间语义、SQL 未成功执行、界面显示的表与锁定表不一致、地图未自动加载，或 POI 图层不是 35 个要素。
+
+## 4. 人工验证二：县域耕地规划 Agent
+
+新建会话，在聊天框原样输入：
+
+```text
+@WorldModelV21 请使用 bishan 数据集运行一次快速县域 MPC 规划，完成硬约束审计，并仅在通过后保存已验证经验。
+```
+
+历史两次真实端到端运行分别耗时 93.490 秒和 112.940 秒。人工验证按 1.5-2 分钟预留，不要在 5 分钟
+路演中承诺 70 秒现场完成。等待到最终回答和地图完全出现，不要中途刷新页面。
+
+必须逐项确认：
+
+1. 路由到 `MentionWorldModelV21`。
+2. 工具轨迹严格为：
+
+```text
+world_model_v21_status
+-> paper9_inspect_resources
+-> paper9_recall_verified_episodes
+-> world_model_v21_pipeline
+-> paper9_audit_run
+-> paper9_commit_verified_episode
+```
+
+3. `world_model_v21_pipeline` 首次规划已带 `cultivated_area_floor_delta_ha=0`；数据准备、样本生成和模型训练显示“已复用（未重复执行）”，MPC 规划显示“完成”。
+4. 没有出现 `world_model_v21_plan` 二次调用；若出现，说明首次规划未走锁定成功路径，本轮不能用于主视频。
+5. 中文主标题必须是“县域耕地受控规划完成”；页面说明 Gemma 4 负责工具决策、Google ADK 承载 `6` 次原生函数调用，数值计算由县域耕地空间优化引擎完成。
+6. “受控自主决策轨迹”严格显示六步“观察/决策 → 结果 → 内部函数名”；除代码格式的内部函数名和版本字段外，不应出现中英文混排的状态标签。
+7. 六次函数调用每一步均显示真实用时；小于 0.1 秒的调用显示三位小数，不能再显示为 `0.0 秒`。“运行耗时”同时显示总用时、MPC 规划用时、审计与经验提交用时。总用时应不小于六次调用用时之和，允许包含模型推理和 UI 编排开销。
+8. `hard_constraint_passed=true`、`all_expected_outputs_exist=true`、commit 状态为 `committed`；界面对应显示“硬约束校验：通过”“已验证经验库：已写入”。
+9. 最终指标应为：
+
+```text
+input_features=101657
+environment_parcels=53004
+n_blocks=2640
+swaps_completed=406
+farm_to_forest=406
+forest_to_farm=406
+cultivated_area_change_ha=+1.9359359262
+slope_change_pct=-0.6077654973
+cont_change=+0.0201841360
+baimu_area_change_ha=+35.1316192716
+```
+
+10. 右侧地图存在且非空，按 `CHG_FLAG` 显示：灰色不变、红色耕地转林地、绿色林地转耕地。
+11. 最终回答不显示 prepared/ensemble 绝对路径；交付成果只显示文件名。
+12. 最终回答不能把 Gemma 4 描述为直接计算 MPC 数值；正确关系是 Gemma 4 选择工具，ADK 执行函数调用，县域耕地空间优化引擎计算，治理代码审计。面向评委统一使用“县域耕地空间优化引擎”，不直接使用内部代号 `Paper9`。
+13. 点击“导出 PDF 报告”，下载文件名应为 `County_Farmland_Planning_Report.pdf`，不能仍是仅含聊天文字的通用报告。
+14. PDF 应为 5 页左右的 A4 报告，至少包含 3 组真实可视化：规划成效看板、按 `CHG_FLAG` 渲染的县域变化地图、六步原生函数调用轨迹。
+15. PDF 中的图斑数、置换数、四项业务指标、逐函数用时、审计状态和经验编号必须与当前会话一致；不得出现本机或容器绝对路径。报告中的内部函数标识只作为审计证据，不作为产品名称。
+16. 若现场解释 GWM 关系，必须使用以下口径：图斑与空间块是状态，候选地类置换是行动，学习型状态转移模型集成是动力学内核雏形，MPC 消费预测并搜索方案，硬约束校验负责结果验收；当前整体是领域原型，不是完整通用 GWM。
+
+截图至少保留四张：六次函数调用与真实用时、硬约束校验与经验写入状态、优化地图与四项业务指标、PDF 的地图或审计页。
+
+判退条件：轨迹不是六次函数调用、任一步没有真实用时、发生二次重规划、面积减少、坡度未下降、连片度未提升、空间产物缺失、未通过审计却写入经验，出现内部绝对路径、地图空白，或 PDF 仍只有聊天文字。
+
+## 5. 70 秒县域耕地规划成片剪辑
+
+先保留完整无剪辑母版，再生成 70 秒现场播放版。只能加速 `world_model_v21_pipeline` 的等待段，
+不能改写工具顺序、隐藏失败或拼接不同运行的指标。字幕必须填写录制母版的实际总耗时，
+不能机械沿用本次验证数字。
+
+| 成片时间 | 画面 | 验收内容 |
+|---|---|---|
+| 0:00-0:06 | 输入锁定提示词 | `bishan`、MPC、审计后才保存 |
+| 0:06-0:16 | 状态、资源、经验召回 | 0.3.3 / 2.2.3、资源就绪、已验证经验召回 |
+| 0:16-0:52 | MPC 规划等待段加速 | 叠字“本次原始总用时 XXX.X 秒，MPC 规划 XXX.X 秒，本等待段加速” |
+| 0:52-1:01 | 审计、经验写入 | 硬约束校验通过、已写入、两步实际用时 |
+| 1:01-1:10 | 地图与指标定格 | `101,657 / 53,004 / 2,640 / 406` 与四项变化 |
+
+## 6. 4 分 40 秒逐字口播
+
+### 0:00-0:30 · 第 1 页
+
+> 各位评委好，我是周宁。GIS Data Agent 不是会聊天的地图，而是把自然语言问题落到可执行、可审计、可复用 GIS 行动的受控 Agent。今天我只展示两件事：高频空间问数，以及高价值县域耕地规划。右侧不是示意图，是本次决赛代码真实运行产生的空间成果。
+
+### 0:30-1:05 · 第 2 页
+
+> 空间问数看似是 NL2SQL，但距离、坐标系和空间连接，都会让看似正确的 SQL 在执行时失败；它是 GIS 分析师的高频刚需。县域耕地规划频率更低、价值更高，必须同时满足面积、坡度和连片度约束。边界很清楚：Gemma 4 选择和解释，Google ADK 承载函数调用，PostGIS 和县域耕地空间优化引擎负责确定性计算，治理代码决定能否提交。
+
+### 1:05-1:55 · 第 3 页与 NL2SQL 片段
+
+> 先看空间问数。我们锁定同一个问题：最长桥梁一百米范围内有多少高德 POI。系统先做 schema 和空间语义 grounding，再由 Gemma 4 生成 SQL；关键是把距离按 geography 的米制单位执行，并通过只读护栏访问 PostGIS，结果是三十五。右侧同步加载同一 PostGIS 快照中的最长桥梁、百米范围和三十五个 POI，让标量答案可核查。模型也不是凭印象选择：在同一主机、同一 CQ-125 上，Gemma 4 26B 是一百一十三题正确，只比 31B 少一题，但完整运行约快百分之三十七点六，因此选为准确率与现场时延的工程平衡点。
+
+### 1:55-3:25 · 第 4 页与县域耕地规划加速片段
+
+> 第二个场景不是生成一段建议，而是执行县域耕地空间布局优化。这里有两层规划：Gemma 4 先观察版本、资源和已验证经验，再决定下一工具；Google ADK 把六个决策落实为结构化原生函数调用。县域耕地空间优化引擎把图斑和空间块作为状态，用学习型状态转移模型集成预测候选行动后果，再由 MPC 搜索方案，不让大模型猜空间数值。
+>
+> 这也是我们走向地理空间世界模型的领域原型：状态转移模型是动力学内核雏形，MPC 使用预测搜索方案，硬约束校验负责结果验收；当前还不是完整通用 GWM。只有校验通过的方案才会写入已验证经验库；失败最多重规划一次，再失败转人工。界面显示六次调用各自的真实用时、总用时和 MPC 规划用时。这次运行处理十万一千六百五十七条输入，完成四百零六对双向置换，面积、坡度和连片度指标均通过审计。
+
+### 3:25-4:20 · 第 5 页
+
+> 一次跑通不能证明可靠。模型侧，真实 Gemma 4 26B 加 ADK 在首次成功、版本阻断和一次恢复三个分支各跑十次，三十次全部符合契约；但这是编排评测，不是三十次算法重跑。算法侧是当前这次真实 MPC 规划和空间产物。工程侧分为二百三十六个镜像运行时测试、六个宿主交付契约和五十二个接口兼容测试。环境侧，历史内网记录与当前本机验证严格分层，二者都不等于生产验收。
+
+### 4:20-4:40 · 第 6 页
+
+> 下一代空间智能体不会只是给大模型不断增加 GIS 工具，而会由 LLM 加地理空间世界模型共同驱动：Gemma 4 与 ADK 理解目标、选择工具和协同，GWM 表示空间状态并推演行动后果，MPC 搜索方案，审核机制决定结果能否保存。今天我们已经验证空间问数、GWM 领域原型和受控闭环；下一步扩展多尺度、多行动与长期不确定性推演。谢谢。
+
+## 7. 最终录制纪律
+
+- 主路演播放 70 秒加速版，不现场等待约 1.5-2 分钟的高成本规划；完整母版保留给技术核查，字幕保留当次真实用时。
+- NL2SQL 可以实时运行；首 token 或页面响应超过 8 秒，立即切同版本锁定视频。
+- 不说“v2.2.3 已在自然资源部内网完成生产验证”“全国泛化”或“30 次真实算法重跑”。
+- 不说“Paper9 是完整通用 GWM”或“MPC 本身就是世界模型”；主画面与主口播统一使用业务名称。
+- 不展示 AlphaEarth 或 OKF 主流程；仅在 Q&A 中按 Tech Preview / knowledge sidecar 口径回答。
+- 到 4:20 必须进入第 6 页，4:40 结束，保留 20 秒缓冲。
diff --git a/docs/finals/video_script.md b/docs/finals/video_script.md
new file mode 100644
index 00000000..92390447
--- /dev/null
+++ b/docs/finals/video_script.md
@@ -0,0 +1,87 @@
+# 决赛视频脚本与镜头验收
+
+所有片段使用本机离线文件，统一为 H.264 MP4、`1920x1080`、30 fps。字幕只陈述画面中可复核的事实，不出现密码、Token、绝对路径、内网地址或个人通知。
+
+## 交付清单
+
+| 文件名 | 时长 | 用途 | 当前状态 |
+|---|---:|---|---|
+| `01_main_fallback_4m40s.mp4` | 4:40 | 实时演示整体不可用时的完整备份 | 待录制 |
+| `02_nl2geosql_locked_35s.mp4` | 0:35 | 主路演第 3 页锁定问题 | 待录制 |
+| `03_farmland_planning_success_70s.mp4` | 1:10 | 完整真实运行母版的等待段加速版 | 待人工 UI 验收后录制 |
+| `04_farmland_planning_recovery_45s.mp4` | 0:45 | Q&A 展示一次受控恢复 | 待录制 |
+| `05_version_block_15s.mp4` | 0:15 | Q&A 展示 fail-closed | 待录制 |
+
+## 1. 完整备份视频 · 4:40
+
+完整视频严格复用 [pitch_script.md](pitch_script.md) 的时间轴和口播，不制作第二套叙事。PPT 页面、Demo 片段和字幕中的数字必须来自同一份构建产物。
+
+| 时间 | 画面 | 验收点 |
+|---|---|---|
+| 0:00-0:30 | 第 1 页，右侧真实 MPC 规划地图 | 无加载、地图非空 |
+| 0:30-1:05 | 第 2 页，两类用户与五层架构 | 用户岗位清晰 |
+| 1:05-1:40 | 插入 NL2GeoSQL 锁定片段 | 问题、`geography, 100`、结果 `35` 同屏出现 |
+| 1:40-1:55 | 第 3 页 benchmark 定格 | `113/125`、`90.4%`、26B 选型可读 |
+| 1:55-3:05 | 插入县域耕地规划成功片段 | 六次函数调用、硬约束校验、经验写入和真实用时均可见 |
+| 3:05-3:25 | 第 4 页真实地图与业务指标 | GWM 领域原型关系、四项变化与 MPC 规划规模可读 |
+| 3:25-4:20 | 第 5 页分层证据 | 30/30 边界字幕不得省略 |
+| 4:20-4:40 | 第 6 页 `LLM + GWM` 总结 | 已验证能力与未来路线边界同时可读；4:40 前结束 |
+
+## 2. NL2Semantic2GeoSQL 锁定片段 · 35 秒
+
+| 时间 | 画面 | 字幕/高亮 |
+|---|---|---|
+| 0:00-0:04 | 已登录应用，输入锁定问题 | “最长桥梁 100 米范围内的高德 POI 数量” |
+| 0:04-0:10 | 展示意图识别与 schema grounding | 只保留数据表和关键字段，不展开内部提示词 |
+| 0:10-0:22 | SQL 结果区域 | 高亮 `ST_DWithin(...::geography, 100)` 与只读执行 |
+| 0:22-0:30 | 执行结果出现 | 放大结果 `35`，禁止剪掉执行状态 |
+| 0:30-0:35 | 定格证据 | “Gemma 4 26B · locked PostGIS snapshot” |
+
+## 3. 县域耕地规划真实成功链 · 70 秒加速版
+
+此片段只能使用 2026-07-30 已验证的真实 Gemma 4 + ADK 六次函数调用轨迹和同一次 MPC 空间产物。
+锁定路径两次验收总耗时为 `93.490s / 112.940s`，末次对应最终代码；录制时先保留完整母版，再只加速 pipeline
+等待段，并在画面注明该母版的实际总耗时。
+不得用旧两工具截图代替六工具链，也不得拼接不同运行的指标。
+面向评委的字幕统一使用“县域耕地空间优化引擎”；`paper9_*` 只保留为可审计的内部函数名。
+
+| 时间 | 画面 | 必须出现的证据 |
+|---|---|---|
+| 0:00-0:06 | 输入唯一县域规划提示词 | 数据集 `bishan`、快速县域 MPC、审计后才保存 |
+| 0:06-0:18 | `status`、`inspect` | 包/算法版本和资源状态 |
+| 0:18-0:27 | `recall` | 只召回 verified episode；没有也允许继续 |
+| 0:27-0:46 | 加速 `world_model_v21_pipeline` 等待段 | ADK 函数调用事件；字幕注明该母版真实总耗时与 MPC 规划用时 |
+| 0:46-0:57 | `paper9_audit_run` | 硬约束校验通过、空间产物存在、显示本次调用用时 |
+| 0:57-1:04 | `paper9_commit_verified_episode` | 已验证经验写入，显示本次调用用时与经验编号 |
+| 1:04-1:10 | 地图与指标定格 | `101,657`、`53,004`、`2,640`、`406` |
+
+## 4. 一次受控恢复 · 45 秒
+
+片头和片尾都必须注明：“Gemma 4 + ADK 编排评测；规划工具响应为确定性替身”。它证明工具选择与恢复行为，不证明又完成了一次真实 MPC 算法运行。
+
+| 时间 | 画面 | 必须出现的证据 |
+|---|---|---|
+| 0:00-0:04 | 评测边界字幕 | 真实 Gemma 4 26B + ADK；确定性工具替身 |
+| 0:04-0:12 | `status -> inspect -> recall` | 版本与资源先行 |
+| 0:12-0:22 | `pipeline` 后首次 `audit` | `attempt=0`、`retryable=true`、失败原因 |
+| 0:22-0:32 | Gemma 4 选择 `plan` | 只能重规划一次，展示调整后的参数摘要 |
+| 0:32-0:39 | 第二次 `audit` | `attempt=1`、passed |
+| 0:39-0:43 | `commit` | 只有通过后写入已验证经验库 |
+| 0:43-0:45 | 轨迹定格 | `8 tools · single bounded replan` |
+
+## 5. 版本阻断 · 15 秒
+
+| 时间 | 画面 | 必须出现的证据 |
+|---|---|---|
+| 0:00-0:04 | `status` | 显示不兼容的版本契约 |
+| 0:04-0:09 | `inspect` | 资源可见，但版本兼容性检查未通过 |
+| 0:09-0:13 | Agent 停止 | 无 `pipeline`、无 `audit`、无 `commit` |
+| 0:13-0:15 | 结论定格 | “安全停止 · 2 次调用 · 不写入经验” |
+
+## 统一验收标准
+
+- 视频首帧在 `1` 秒内出现；任意切换不得出现桌面、终端历史、密码管理器或通知中心。
+- 演示浏览器使用 125%-150% 缩放；投影设备上最小字幕不少于等效 24 px。
+- 录制后断网复播一遍；使用系统自带播放器从 U 盘再复播一遍。
+- 每个片段的画面、字幕和口播必须通过 [claim_register.md](claim_register.md)；不能用剪辑隐藏失败或伪造工具返回。
+- 保留无旁白母版和有旁白成片；现场优先使用无旁白片段，由演讲者同步口播。
diff --git a/pyproject.toml b/pyproject.toml
index da6f7dc3..164fa50c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -83,6 +83,7 @@ dev = [
"pytest>=9.0",
"pytest-asyncio>=0.23",
"coverage>=7.0",
+ "ruff>=0.9",
]
[project.scripts]
@@ -107,3 +108,10 @@ data_agent = [
filterwarnings = [
"ignore:BaseAgentConfig is deprecated.*:DeprecationWarning",
]
+
+[tool.ruff]
+target-version = "py311"
+line-length = 100
+
+[tool.ruff.lint]
+select = ["E", "F", "I", "B", "UP"]
diff --git a/scripts/build_finals_technical_diagrams.mjs b/scripts/build_finals_technical_diagrams.mjs
new file mode 100644
index 00000000..a4c4636d
--- /dev/null
+++ b/scripts/build_finals_technical_diagrams.mjs
@@ -0,0 +1,419 @@
+#!/usr/bin/env node
+
+import { mkdir, writeFile } from "node:fs/promises";
+import path from "node:path";
+import process from "node:process";
+
+import { chromium } from "playwright";
+
+const ROOT = process.cwd();
+const OUTPUT_DIR = path.join(ROOT, "docs", "finals", "assets", "diagrams");
+
+const C = {
+ ink: "#17313A",
+ muted: "#60727A",
+ line: "#8CA0A8",
+ pale: "#F4F7F8",
+ blue: "#1976D2",
+ blueFill: "#EAF3FC",
+ teal: "#087E8B",
+ tealFill: "#E7F5F5",
+ green: "#2E7D32",
+ greenFill: "#EDF7EE",
+ amber: "#A86200",
+ amberFill: "#FFF5E5",
+ red: "#C33C54",
+ redFill: "#FCECEF",
+ violet: "#6950A1",
+ violetFill: "#F2EEFA",
+ white: "#FFFFFF",
+};
+
+function esc(value) {
+ return String(value)
+ .replaceAll("&", "&")
+ .replaceAll("<", "<")
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+ parts.push(box({ x: 70, y: 160, w: 300, h: 105, heading: "用户目标", lines: ["自然语言问题", "显式 @ 路由"], accent: C.teal, fill: C.tealFill }));
+ parts.push(box({ x: 450, y: 160, w: 480, h: 105, heading: "@NL2SQL", lines: ["高频空间问数", "Gemma 4 合成 GeoSQL"], accent: C.blue, fill: C.blueFill }));
+ parts.push(box({ x: 1010, y: 160, w: 520, h: 105, heading: "@WorldModelV21", lines: ["县域空间优化", "Gemma 4 动态选择下一工具"], accent: C.violet, fill: C.violetFill }));
+ parts.push(arrow(370, 212, 450, 212));
+ parts.push(polyline([[370, 225], [410, 225], [410, 145], [1270, 145], [1270, 160]]));
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+ parts.push(box({ x: 70, y: 315, w: 1460, h: 125, heading: "Gemma 4 26B 决策面", lines: ["意图理解 · GeoSQL 合成 · 工具选择 · 根据反馈停止 / 恢复 / 提交"], accent: C.blue, fill: C.blueFill, center: true, headingSize: 29, bodySize: 22 }));
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+ parts.push(arrow(1270, 265, 1270, 315));
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+ parts.push(box({ x: 70, y: 490, w: 1460, h: 120, heading: "Google ADK 运行时", lines: ["LlmAgent / BaseAgent · 10 个函数工具 · session / event · function_call / function_response"], accent: C.teal, fill: C.tealFill, center: true, headingSize: 29, bodySize: 22 }));
+ parts.push(arrow(800, 440, 800, 490));
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+ parts.push(box({ x: 70, y: 660, w: 700, h: 135, heading: "NL2Semantic2GeoSQL 行动面", lines: ["Semantic Layer + GeoSQL Harness", "PostGIS 只读执行 + 三层地图证据"], accent: C.blue, fill: C.white }));
+ parts.push(box({ x: 830, y: 660, w: 700, h: 135, heading: "县域耕地空间优化行动面", lines: ["Transition Ensemble + ONNX Runtime", "MPC + 真实县域环境执行"], accent: C.violet, fill: C.white }));
+ parts.push(arrow(530, 610, 420, 660));
+ parts.push(arrow(1070, 610, 1180, 660));
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+ parts.push(box({ x: 70, y: 845, w: 1460, h: 105, heading: "治理与证据面", lines: ["版本兼容 · SQL 安全 · 硬约束校验 · Verified Episodic Memory · Map / Trace / PDF"], accent: C.green, fill: C.greenFill, center: true, headingSize: 28, bodySize: 21 }));
+ parts.push(arrow(420, 795, 420, 845));
+ parts.push(arrow(1180, 795, 1180, 845));
+ return svgFrame(parts.join(""), { ariaLabel: "GIS Data Agent 总体技术架构" });
+}
+
+function twoLevelPlanning() {
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+ p.push(``);
+ p.push(textLines({ x: 105, y: 205, lines: ["任务级规划"], size: 29, weight: 700, fill: C.blue }));
+ p.push(pill({ x: 1250, y: 177, w: 230, text: "由 Gemma 4 决定", color: C.blue, fill: C.white }));
+ const task = [
+ [110, "用户目标", ["问题、版本、资源"]],
+ [420, "Gemma 4 + ADK", ["理解目标并选工具"]],
+ [760, "函数工具反馈", ["状态、结果、审计结论"]],
+ [1100, "下一动作", ["停止 / 恢复 / 提交"]],
+ ];
+ task.forEach(([x, heading, lines]) => p.push(box({ x, y: 250, w: 270, h: 130, heading, lines, accent: C.blue, fill: C.white, center: true, headingSize: 23, bodySize: 18 })));
+ p.push(arrow(380, 315, 420, 315));
+ p.push(arrow(690, 315, 760, 315));
+ p.push(arrow(1030, 315, 1100, 315));
+ p.push(polyline([[1235, 380], [1235, 425], [555, 425], [555, 380]], { color: C.blue, label: "结构化反馈驱动下一步", labelX: 900, labelY: 418 }));
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+ p.push(textLines({ x: 105, y: 560, lines: ["数值级规划"], size: 29, weight: 700, fill: C.violet }));
+ p.push(pill({ x: 1240, y: 532, w: 240, text: "确定性算法执行", color: C.violet, fill: C.white }));
+ const numeric = [
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+ [420, "转移模型集成", ["预测 s' 与 reward"]],
+ [760, "MPC 搜索", ["比较候选 rollout"]],
+ [1100, "真实环境执行", ["执行首个行动", "重新观测状态"]],
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+ numeric.forEach(([x, heading, lines]) => p.push(box({ x, y: 610, w: 270, h: 145, heading, lines, accent: C.violet, fill: C.white, center: true, headingSize: 23, bodySize: 18 })));
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+
+ p.push(box({ x: 315, y: 865, w: 970, h: 70, heading: "责任边界：Gemma 4 决定调用什么；MPC 计算具体空间行动；独立代码审核结果", accent: C.red, fill: C.redFill, center: true, headingSize: 22 }));
+ return svgFrame(p.join(""), { ariaLabel: "任务级规划与数值级规划的责任边界" });
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+
+function adkTrajectories() {
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+ { x: 1070, title: "失败后恢复", badge: "8 个工具，一次重规划", color: C.amber, fill: C.amberFill, nodes: ["检查版本与能力", "检查资源与契约", "召回已验证经验", "执行 / 复用 A–D", "首次审计失败", "单独重跑 MPC", "再次审计通过", "提交已验证经验"] },
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+ return svgFrame(p.join(""), { ariaLabel: "ADK 两工具、六工具和八工具轨迹" });
+}
+
+function nl2sqlPipeline() {
+ const p = [title("NL2Semantic2GeoSQL 完整执行链", "Gemma 4 负责 SQL 合成；语义约束、安全、执行与地图证据由确定性组件负责")];
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+ ["语义解析", "表 / 字段 / 别名 / 单位", C.teal, C.tealFill],
+ ["实时 Grounding", "类型 / geometry / SRID", C.teal, C.tealFill],
+ ["Few-shot 检索", "相似参考查询", C.teal, C.tealFill],
+ ["Gemma 4 合成", "SQL only · T=0", C.blue, C.blueFill],
+ ["空间语义改写", "米制距离 / SRID / 去重", C.violet, C.violetFill],
+ ["安全与运行校验", "只读 / AST / allow-list", C.red, C.redFill],
+ ["PostGIS 执行", "真实数据 · 结构化结果", C.green, C.greenFill],
+ ["证据与沉淀", "标量 + SQL + 地图 + 案例", C.green, C.greenFill],
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+
+function gwmMpcArchitecture() {
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+
+ p.push(box({ x: 100, y: 820, w: 650, h: 115, heading: "独立硬约束校验", lines: ["面积不减少 · 坡度下降 · 连片度提升 · 产物完整"], accent: C.red, fill: C.redFill, center: true, headingSize: 25, bodySize: 19 }));
+ p.push(box({ x: 850, y: 820, w: 650, h: 115, heading: "Verified Episodic Memory", lines: ["仅通过校验的运行经验可保存并被下次召回"], accent: C.green, fill: C.greenFill, center: true, headingSize: 25, bodySize: 19 }));
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+ return svgFrame(p.join(""), { ariaLabel: "地理空间世界模型原型、MPC 和审核记忆的关系" });
+}
+
+function transitionNetwork() {
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+ { y: 410, title: "行动索引", lines: ["Discrete(n_blocks)"], color: C.amber, fill: C.amberFill },
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+ p.push(box({ x: 425, y: 395, w: 300, h: 150, heading: "Action Embedding", lines: ["n_blocks → 32", "选择目标 block"], accent: C.amber, fill: C.white, center: true, headingSize: 23, bodySize: 19 }));
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+ p.push(arrow(340, 470, 425, 470));
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+ p.push(box({ x: 795, y: 440, w: 310, h: 180, heading: "Context 拼接", lines: ["selected block 32", "+ action 32 + global 32", "+ mean pool 32 = 128"], accent: C.violet, fill: C.violetFill, center: true, headingSize: 25, bodySize: 19 }));
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+ const heads = [
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+ { y: 390, title: "Global Delta Head", lines: ["128 → 256 → 12"], color: C.blue, fill: C.blueFill },
+ { y: 590, title: "Reward Head", lines: ["128 → 64 → 1"], color: C.green, fill: C.greenFill },
+ ];
+ heads.forEach((item) => {
+ p.push(box({ x: 1180, y: item.y, w: 350, h: 135, heading: item.title, lines: item.lines, accent: item.color, fill: item.fill, center: true, headingSize: 23, bodySize: 19 }));
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+ p.push(box({ x: 300, y: 830, w: 1000, h: 95, heading: "3-member Ensemble → ONNX Runtime → MPC 批量评估候选行动", accent: C.violet, fill: C.pale, center: true, headingSize: 26 }));
+ return svgFrame(p.join(""), { width: 1600, height: 970, ariaLabel: "237K 参数状态转移网络结构" });
+}
+
+function autonomyStateMachine() {
+ const p = [title("受控自主决策状态机", "自主性体现在观察、选择、反馈后恢复或停止；权限边界由代码强制执行")];
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+ p.push(box({ x: 550, y: 230, w: 500, h: 95, heading: "检查版本、能力与资源", accent: C.blue, fill: C.blueFill, center: true, headingSize: 24 }));
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+ p.push(diamond({ cx: 800, cy: 390, w: 390, h: 105, heading: "版本与资源可用？", color: C.amber, fill: C.amberFill }));
+ p.push(arrow(800, 325, 800, 338));
+ p.push(box({ x: 1125, y: 340, w: 370, h: 100, heading: "停止并报告缺口", lines: ["不自动降级旧算法"], accent: C.red, fill: C.redFill, center: true, headingSize: 23, bodySize: 18 }));
+ p.push(arrow(995, 390, 1125, 390, { color: C.red, label: "否", labelY: 375 }));
+ p.push(box({ x: 550, y: 485, w: 500, h: 95, heading: "召回已验证经验", accent: C.teal, fill: C.tealFill, center: true, headingSize: 24 }));
+ p.push(arrow(800, 443, 800, 485, { color: C.green, label: "是", labelX: 835, labelY: 472 }));
+ p.push(box({ x: 550, y: 635, w: 500, h: 95, heading: "执行 Pipeline / MPC Plan", accent: C.violet, fill: C.violetFill, center: true, headingSize: 24 }));
+ p.push(arrow(800, 580, 800, 635));
+ p.push(diamond({ cx: 800, cy: 805, w: 430, h: 115, heading: "确定性审核通过？", color: C.amber, fill: C.amberFill }));
+ p.push(arrow(800, 730, 800, 748));
+ p.push(box({ x: 1130, y: 755, w: 370, h: 100, heading: "提交已验证经验", lines: ["幂等写入 + SHA-256"], accent: C.green, fill: C.greenFill, center: true, headingSize: 22, bodySize: 18 }));
+ p.push(arrow(1015, 805, 1130, 805, { color: C.green, label: "通过", labelY: 790 }));
+ p.push(box({ x: 105, y: 755, w: 340, h: 100, heading: "最多重规划一次", lines: ["仅重跑 Tool 4 MPC"], accent: C.amber, fill: C.amberFill, center: true, headingSize: 22, bodySize: 18 }));
+ p.push(arrow(585, 805, 445, 805, { color: C.amber, label: "首次失败", labelY: 790 }));
+ p.push(arrow(800, 862, 800, 910, { color: C.red, label: "缺产物", labelX: 860, labelY: 895 }));
+ p.push(box({ x: 105, y: 910, w: 340, h: 100, heading: "再次确定性审核", accent: C.amber, fill: C.white, center: true, headingSize: 22 }));
+ p.push(arrow(275, 855, 275, 910));
+ p.push(box({ x: 630, y: 910, w: 340, h: 100, heading: "转人工复核", lines: ["二次失败或缺少空间产物"], accent: C.red, fill: C.redFill, center: true, headingSize: 22, bodySize: 17 }));
+ p.push(arrow(445, 960, 630, 960, { color: C.red, label: "失败", labelY: 945 }));
+ p.push(polyline([[275, 1010], [275, 1035], [1315, 1035], [1315, 855]], { color: C.green, label: "通过", labelX: 800, labelY: 1028 }));
+ return svgFrame(p.join(""), { width: 1600, height: 1080, ariaLabel: "受控自主决策状态机" });
+}
+
+function knowledgeMemoryLoop() {
+ const p = [title("知识与记忆的受控更新闭环", "记忆保存已发生且可复核的经验；知识保存领域定义、规则与适用范围")];
+ const nodes = [
+ { x: 100, y: 235, w: 360, h: 125, title: "候选事实 / 经验", lines: ["GeoSQL 成功案例", "县域规划 episode"], color: C.teal, fill: C.tealFill },
+ { x: 620, y: 175, w: 360, h: 145, title: "来源与适用范围", lines: ["版本 · 时间 · 空间范围", "数据集 · 摘要哈希"], color: C.blue, fill: C.blueFill },
+ { x: 1140, y: 235, w: 360, h: 125, title: "分级验证", lines: ["自动校验", "高风险项人工审批"], color: C.amber, fill: C.amberFill },
+ { x: 1140, y: 610, w: 360, h: 125, title: "激活为可用知识", lines: ["Verified Memory", "Reference Query / Registry"], color: C.green, fill: C.greenFill },
+ { x: 620, y: 680, w: 360, h: 145, title: "运行中检索与引用", lines: ["按数据集与版本隔离", "记录 provenance"], color: C.violet, fill: C.violetFill },
+ { x: 100, y: 610, w: 360, h: 125, title: "新结果与新证据", lines: ["真实执行结果", "审核结论与反馈"], color: C.teal, fill: C.tealFill },
+ ];
+ nodes.forEach((n) => p.push(box({ x: n.x, y: n.y, w: n.w, h: n.h, heading: n.title, lines: n.lines, accent: n.color, fill: n.fill, center: true, headingSize: 24, bodySize: 18 })));
+ p.push(arrow(460, 297, 620, 248));
+ p.push(arrow(980, 248, 1140, 297));
+ p.push(arrow(1320, 360, 1320, 610));
+ p.push(arrow(1140, 672, 980, 752));
+ p.push(arrow(620, 752, 460, 672));
+ p.push(arrow(280, 610, 280, 360));
+ p.push(box({ x: 535, y: 420, w: 530, h: 150, heading: "治理规则", lines: ["SQL 可执行 ≠ 语义正确", "审核未通过不得写入经验", "新证据可触发再验证 / 降级 / 废止"], accent: C.red, fill: C.redFill, center: true, headingSize: 25, bodySize: 19 }));
+ p.push(polyline([[800, 420], [800, 350], [1140, 350]], { color: C.red, dashed: true }));
+ p.push(pill({ x: 100, y: 855, w: 420, text: "即时 / 会话记忆：提供上下文", color: C.muted, fill: C.pale, size: 19 }));
+ p.push(pill({ x: 590, y: 855, w: 420, text: "领域经验：只追加、带证据", color: C.green, fill: C.greenFill, size: 19 }));
+ p.push(pill({ x: 1080, y: 855, w: 420, text: "语义知识：版本化、可撤销", color: C.blue, fill: C.blueFill, size: 19 }));
+ return svgFrame(p.join(""), { ariaLabel: "知识与记忆更新闭环" });
+}
+
+function deploymentTopology() {
+ const p = [title("决赛运行与部署拓扑", "核心服务本地或内网部署；模型、数据、算法资源与用户产物边界清晰")];
+ p.push(box({ x: 70, y: 330, w: 270, h: 145, heading: "现场浏览器", lines: ["PPT / Demo 操作端", "HTTP :8000"], accent: C.teal, fill: C.tealFill, center: true, headingSize: 25, bodySize: 19 }));
+ p.push(``);
+ p.push(textLines({ x: 445, y: 215, lines: ["Docker Compose / 内网运行边界"], size: 28, weight: 700, fill: C.ink }));
+ p.push(box({ x: 465, y: 295, w: 380, h: 210, heading: "GIS Data Agent App", lines: ["Chainlit + API", "Google ADK 运行时", "用户与运行目录隔离"], accent: C.blue, fill: C.blueFill, center: true, headingSize: 26, bodySize: 19 }));
+ p.push(arrow(340, 402, 465, 402, { label: "请求 / 地图 / 报告", labelX: 402, labelY: 372 }));
+
+ p.push(box({ x: 930, y: 260, w: 250, h: 125, heading: "Gemma 4 / Ollama", lines: ["本地模型服务"], accent: C.blue, fill: C.white, center: true, headingSize: 22, bodySize: 18 }));
+ p.push(box({ x: 1225, y: 260, w: 240, h: 125, heading: "PostGIS", lines: ["只读空间查询", ":5432"], accent: C.teal, fill: C.white, center: true, headingSize: 22, bodySize: 18 }));
+ p.push(box({ x: 930, y: 440, w: 250, h: 125, heading: "Redis", lines: ["会话与运行状态", ":6379"], accent: C.amber, fill: C.white, center: true, headingSize: 22, bodySize: 18 }));
+ p.push(box({ x: 1225, y: 440, w: 240, h: 125, heading: "MinIO（可选）", lines: ["对象存储扩展", "非主 Demo 依赖"], accent: C.muted, fill: C.white, center: true, headingSize: 21, bodySize: 17 }));
+ p.push(arrow(845, 350, 930, 322));
+ p.push(polyline([[845, 375], [885, 375], [885, 235], [1345, 235], [1345, 260]]));
+ p.push(arrow(845, 425, 930, 502));
+ p.push(polyline([[845, 450], [885, 450], [885, 590], [1345, 590], [1345, 565]], { dashed: true }));
+
+ p.push(box({ x: 465, y: 610, w: 310, h: 140, heading: "只读算法资源", lines: ["0.3.3 / 2.2.3 包", "Bishan prepared + ONNX"], accent: C.violet, fill: C.violetFill, center: true, headingSize: 23, bodySize: 18 }));
+ p.push(box({ x: 835, y: 610, w: 310, h: 140, heading: "按用户隔离的产物", lines: ["uploads / output", "Map / Audit / PDF"], accent: C.green, fill: C.greenFill, center: true, headingSize: 23, bodySize: 18 }));
+ p.push(box({ x: 1205, y: 610, w: 260, h: 140, heading: "预检与健康检查", lines: ["版本 · 模型 · 数据", "app / db / redis"], accent: C.red, fill: C.redFill, center: true, headingSize: 22, bodySize: 18 }));
+ p.push(arrow(620, 610, 620, 505));
+ p.push(arrow(790, 505, 935, 610));
+ p.push(polyline([[1205, 680], [1175, 680], [1175, 785], [430, 785], [430, 475], [465, 475]], { color: C.red, dashed: true, label: "启动前", labelX: 800, labelY: 776 }));
+ p.push(box({ x: 260, y: 865, w: 1080, h: 75, heading: "失败策略：版本、模型、数据或核心服务预检不通过时停止，不在现场临时降级", accent: C.red, fill: C.redFill, center: true, headingSize: 22 }));
+ return svgFrame(p.join(""), { ariaLabel: "GIS Data Agent 决赛运行和部署拓扑" });
+}
+
+const diagrams = [
+ ["architecture_overview", architectureOverview],
+ ["two_level_planning", twoLevelPlanning],
+ ["adk_tool_trajectories", adkTrajectories],
+ ["nl2semantic2geosql_pipeline", nl2sqlPipeline],
+ ["gwm_mpc_architecture", gwmMpcArchitecture],
+ ["transition_network", transitionNetwork],
+ ["autonomy_state_machine", autonomyStateMachine],
+ ["knowledge_memory_loop", knowledgeMemoryLoop],
+ ["deployment_topology", deploymentTopology],
+];
+
+async function main() {
+ await mkdir(OUTPUT_DIR, { recursive: true });
+ const browser = await chromium.launch({ headless: true });
+ try {
+ for (const [name, build] of diagrams) {
+ const svg = build();
+ if (svg.includes("undefined")) throw new Error(`${name}: generated SVG contains undefined`);
+ const svgPath = path.join(OUTPUT_DIR, `${name}.svg`);
+ const pngPath = path.join(OUTPUT_DIR, `${name}.png`);
+ await writeFile(svgPath, svg, "utf8");
+
+ const dimensions = /width="(\d+)" height="(\d+)"/.exec(svg);
+ const width = Number(dimensions[1]);
+ const height = Number(dimensions[2]);
+ const context = await browser.newContext({ viewport: { width, height }, deviceScaleFactor: 2 });
+ const page = await context.newPage();
+ await page.setContent(`${svg}`);
+ await page.locator("svg").screenshot({ path: pngPath, animations: "disabled" });
+ await context.close();
+ process.stdout.write(`${name}: ${width * 2}x${height * 2}\n`);
+ }
+ } finally {
+ await browser.close();
+ }
+}
+
+await main();
diff --git a/scripts/build_gemma4_finals_ppt.py b/scripts/build_gemma4_finals_ppt.py
new file mode 100644
index 00000000..3bc8c7b3
--- /dev/null
+++ b/scripts/build_gemma4_finals_ppt.py
@@ -0,0 +1,2086 @@
+#!/usr/bin/env python3
+"""Build the evidence-backed Gemma 4 finals deck and its visual assets."""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import json
+from pathlib import Path
+from typing import Any
+
+import geopandas as gpd
+import matplotlib.pyplot as plt
+from matplotlib import font_manager
+from matplotlib.patches import Patch
+from PIL import Image, ImageOps
+from pptx import Presentation
+from pptx.dml.color import RGBColor
+from pptx.enum.shapes import MSO_AUTO_SHAPE_TYPE, MSO_CONNECTOR
+from pptx.enum.text import MSO_ANCHOR, PP_ALIGN
+from pptx.util import Inches, Pt
+
+ROOT = Path(__file__).resolve().parents[1]
+FINALS_DIR = ROOT / "docs" / "finals"
+ASSET_DIR = FINALS_DIR / "assets"
+DEFAULT_TEMPLATE = Path.home() / "Downloads" / "路演框架（GDG Shanghai）.pptx"
+DEFAULT_OUTPUT = FINALS_DIR / "GIS_Data_Agent_Gemma4_Finals_CN.pptx"
+
+BENCHMARK_CSV = ROOT / "docs" / "assets" / "gemma4_host228_scale_sweep_summary.csv"
+RELIABILITY_JSON = (
+ ROOT
+ / "data_agent"
+ / "demo_evidence"
+ / "paper9"
+ / "finals_20260730"
+ / "adk_reliability_report.json"
+)
+PREFLIGHT_JSON = RELIABILITY_JSON.with_name("finals_preflight_final.json")
+FARMLAND_BASE_RUN_DIR = (
+ ROOT
+ / "data_agent"
+ / "uploads"
+ / "finals_verification_fixed"
+ / "world_model_v21"
+ / "20260730_003511_666173"
+)
+FARMLAND_EVIDENCE_DIR = (
+ FINALS_DIR / "evidence" / "world_model_bishan_20260730_155442"
+)
+FARMLAND_SUMMARY_JSON = FARMLAND_EVIDENCE_DIR / "mpc_summary.json"
+FARMLAND_AUDIT_JSON = FARMLAND_EVIDENCE_DIR / "paper9_agent_audit.json"
+FARMLAND_CHANGES_GEOJSON = FARMLAND_EVIDENCE_DIR / "optimized_changes.geojson"
+FARMLAND_BASE_SHP = FARMLAND_BASE_RUN_DIR / "optimized_dltb.shp"
+NL2SQL_SCREENSHOT = (
+ ROOT
+ / "tests"
+ / "e2e"
+ / "artifacts"
+ / "nl2sql_longest_bridge_map_2026_07_30"
+ / "result.png"
+)
+
+FONT = "Microsoft YaHei"
+WIDE = Inches(13.333333)
+HIGH = Inches(7.5)
+
+COLORS = {
+ "bg": RGBColor(247, 249, 252),
+ "white": RGBColor(255, 255, 255),
+ "ink": RGBColor(22, 29, 44),
+ "muted": RGBColor(91, 102, 121),
+ "line": RGBColor(219, 226, 235),
+ "navy": RGBColor(20, 31, 51),
+ "blue": RGBColor(66, 133, 244),
+ "green": RGBColor(52, 168, 83),
+ "yellow": RGBColor(251, 188, 5),
+ "red": RGBColor(234, 67, 53),
+ "cyan": RGBColor(0, 137, 123),
+ "pale_blue": RGBColor(232, 240, 254),
+ "pale_green": RGBColor(231, 244, 234),
+ "pale_yellow": RGBColor(254, 247, 224),
+ "pale_red": RGBColor(253, 235, 233),
+ "pale_cyan": RGBColor(226, 242, 241),
+}
+
+
+def color(name: str) -> RGBColor:
+ return COLORS[name]
+
+
+def load_json(path: Path) -> dict[str, Any]:
+ with path.open(encoding="utf-8") as handle:
+ return json.load(handle)
+
+
+def load_benchmark() -> list[dict[str, str]]:
+ with BENCHMARK_CSV.open(encoding="utf-8") as handle:
+ return list(csv.DictReader(handle))
+
+
+def finals_evidence() -> dict[str, Any]:
+ benchmark = load_benchmark()
+ reliability = load_json(RELIABILITY_JSON)
+ summary = load_json(FARMLAND_SUMMARY_JSON)
+ audit = load_json(FARMLAND_AUDIT_JSON)
+ preflight = load_json(PREFLIGHT_JSON)
+ runs = reliability["runs"]
+ result = summary["results"][0]
+ spatial_output = summary["shapefile_output"]
+ if not audit.get("hard_constraint_passed"):
+ raise ValueError("Finals farmland evidence did not pass the hard-constraint audit")
+ if spatial_output["n_farm_to_forest"] != result["swaps_completed"]:
+ raise ValueError("Farmland-to-forest feature count disagrees with swaps_completed")
+ if spatial_output["n_forest_to_farm"] != result["swaps_completed"]:
+ raise ValueError("Forest-to-farmland feature count disagrees with swaps_completed")
+ return {
+ "benchmark": benchmark,
+ "reliability": reliability,
+ "reliability_passed": sum(bool(run["passed"]) for run in runs),
+ "reliability_total": len(runs),
+ "summary": summary,
+ "result": result,
+ "audit": audit,
+ "preflight": preflight,
+ }
+
+
+def ensure_assets(evidence: dict[str, Any]) -> dict[str, Path]:
+ ASSET_DIR.mkdir(parents=True, exist_ok=True)
+ benchmark_path = ASSET_DIR / "gemma4_model_selection.png"
+ map_path = ASSET_DIR / "farmland_mpc_change_map.png"
+ nl2sql_path = ASSET_DIR / "nl2geosql_demo_crop.png"
+ build_benchmark_chart(evidence["benchmark"], benchmark_path)
+ if FARMLAND_BASE_SHP.exists():
+ build_farmland_optimization_map(
+ FARMLAND_BASE_SHP,
+ FARMLAND_CHANGES_GEOJSON,
+ evidence["summary"],
+ map_path,
+ )
+ elif not map_path.exists():
+ raise FileNotFoundError(
+ "The base Shapefile is unavailable and no evidence-backed map asset exists"
+ )
+
+ if NL2SQL_SCREENSHOT.exists():
+ build_nl2sql_crop(NL2SQL_SCREENSHOT, nl2sql_path)
+ elif not nl2sql_path.exists():
+ raise FileNotFoundError(
+ "The NL2SQL screenshot is unavailable and no verified crop asset exists"
+ )
+ return {"benchmark": benchmark_path, "map": map_path, "nl2sql": nl2sql_path}
+
+
+def chart_font() -> font_manager.FontProperties:
+ candidates = [
+ Path("/System/Library/Fonts/STHeiti Light.ttc"),
+ Path("/System/Library/Fonts/STHeiti Medium.ttc"),
+ Path("/System/Library/Fonts/Supplemental/Arial Unicode.ttf"),
+ ]
+ selected = next((path for path in candidates if path.exists()), None)
+ return (
+ font_manager.FontProperties(fname=str(selected))
+ if selected
+ else font_manager.FontProperties()
+ )
+
+
+def build_benchmark_chart(rows: list[dict[str, str]], output: Path) -> None:
+ labels = [row["model"].replace("Gemma4:", "") for row in rows]
+ accuracy = [float(row["full_ex_pct"]) for row in rows]
+ runtime = [float(row["full_minutes"]) for row in rows]
+ selected = labels.index("26b")
+ font = chart_font()
+
+ fig, ax = plt.subplots(figsize=(12.5, 5.6), dpi=160)
+ fig.patch.set_facecolor("#F7F9FC")
+ ax.set_facecolor("#F7F9FC")
+ bars = ax.bar(
+ labels,
+ accuracy,
+ color=["#D7DFEA" if index != selected else "#4285F4" for index in range(len(labels))],
+ width=0.58,
+ zorder=3,
+ )
+ ax.set_ylim(55, 100)
+ ax.set_ylabel("CQ-125 执行正确率 (%)", fontproperties=font, color="#5B6679")
+ ax.grid(axis="y", color="#E4E9F0", linewidth=0.8, zorder=0)
+ ax.spines[["top", "right", "left"]].set_visible(False)
+ ax.tick_params(axis="y", length=0, colors="#5B6679")
+ ax.tick_params(axis="x", length=0, colors="#161D2C", labelsize=11)
+ for index, bar in enumerate(bars):
+ ax.text(
+ bar.get_x() + bar.get_width() / 2,
+ bar.get_height() + 0.9,
+ f"{accuracy[index]:.1f}%",
+ ha="center",
+ va="bottom",
+ fontsize=11,
+ color="#4285F4" if index == selected else "#5B6679",
+ fontweight="bold" if index == selected else "normal",
+ )
+
+ runtime_ax = ax.twinx()
+ runtime_ax.plot(
+ labels, runtime, color="#EA4335", marker="o", linewidth=2.2, markersize=6, zorder=4
+ )
+ runtime_ax.set_ylim(0, 25)
+ runtime_ax.set_ylabel("完整运行时间 (min)", fontproperties=font, color="#EA4335")
+ runtime_ax.spines[["top", "right", "left"]].set_visible(False)
+ runtime_ax.tick_params(axis="y", length=0, colors="#EA4335")
+ for index, value in enumerate(runtime):
+ runtime_ax.text(
+ index, value + 0.9, f"{value:.1f}", ha="center", fontsize=9, color="#EA4335"
+ )
+ ax.set_title(
+ "同一主机 · 同一 CQ-125 · 26B 是准确率与时延的工程平衡点",
+ fontproperties=font,
+ fontsize=15,
+ fontweight="bold",
+ color="#161D2C",
+ loc="left",
+ pad=14,
+ )
+ fig.tight_layout(pad=1.4)
+ fig.savefig(output, bbox_inches="tight", facecolor=fig.get_facecolor())
+ plt.close(fig)
+
+
+def build_farmland_optimization_map(
+ background_shapefile: Path,
+ changes_geojson: Path,
+ summary: dict[str, Any],
+ output: Path,
+) -> None:
+ background = gpd.read_file(background_shapefile, columns=[])
+ changes = gpd.read_file(changes_geojson, columns=["CHG_FLAG"])
+ if background.crs != changes.crs:
+ changes = changes.to_crs(background.crs)
+ farm_to_forest = changes[changes["CHG_FLAG"] == 1]
+ forest_to_farm = changes[changes["CHG_FLAG"] == 2]
+ spatial_output = summary["shapefile_output"]
+ expected_swaps = int(summary["results"][0]["swaps_completed"])
+ if len(farm_to_forest) != expected_swaps or len(forest_to_farm) != expected_swaps:
+ raise ValueError("Change-map feature counts disagree with the planning summary")
+ unchanged_count = int(spatial_output["n_input"]) - len(changes)
+
+ fig, ax = plt.subplots(figsize=(9, 9), dpi=170)
+ fig.patch.set_facecolor("#F7F9FC")
+ ax.set_facecolor("#F7F9FC")
+ background.plot(ax=ax, color="#D8DEE8", edgecolor="none", rasterized=True)
+ farm_to_forest.plot(ax=ax, color="#EA4335", edgecolor="#B82C20", linewidth=0.12)
+ forest_to_farm.plot(ax=ax, color="#34A853", edgecolor="#1F7A38", linewidth=0.12)
+ ax.set_axis_off()
+ ax.margins(0.015)
+ ax.legend(
+ handles=[
+ Patch(facecolor="#D8DEE8", label=f"保持不变 {unchanged_count:,}"),
+ Patch(facecolor="#EA4335", label=f"耕地 → 林地 {len(farm_to_forest):,}"),
+ Patch(facecolor="#34A853", label=f"林地 → 耕地 {len(forest_to_farm):,}"),
+ ],
+ loc="lower left",
+ prop=chart_font(),
+ frameon=True,
+ framealpha=0.96,
+ facecolor="white",
+ edgecolor="#DBE2EB",
+ fontsize=10,
+ )
+ fig.tight_layout(pad=0.15)
+ fig.savefig(output, bbox_inches="tight", facecolor=fig.get_facecolor())
+ plt.close(fig)
+
+
+def build_nl2sql_crop(source: Path, output: Path) -> None:
+ with Image.open(source) as image:
+ if image.width / image.height > 1.4:
+ rgb = image.convert("RGB")
+ result_crop = rgb.crop(
+ (
+ int(image.width * 0.047),
+ int(image.height * 0.255),
+ int(image.width * 0.214),
+ int(image.height * 0.785),
+ )
+ )
+ map_crop = rgb.crop(
+ (
+ int(image.width * 0.214),
+ int(image.height * 0.037),
+ int(image.width * 0.625),
+ image.height,
+ )
+ )
+ canvas = Image.new("RGB", (1500, 1500), "#E8EAED")
+ result_panel = ImageOps.contain(
+ result_crop,
+ (650, 1500),
+ method=Image.Resampling.LANCZOS,
+ )
+ map_panel = ImageOps.contain(
+ map_crop,
+ (850, 1500),
+ method=Image.Resampling.LANCZOS,
+ )
+ canvas.paste(result_panel, (0, int((1500 - result_panel.height) / 2)))
+ canvas.paste(map_panel, (650, int((1500 - map_panel.height) / 2)))
+ fitted = canvas
+ else:
+ crop = image.crop((70, 250, 1320, 1770)).convert("RGB")
+ fitted = ImageOps.fit(crop, (1500, 1500), method=Image.Resampling.LANCZOS)
+ fitted.save(output, quality=94)
+
+
+def set_fill(shape, fill: str) -> None:
+ shape.fill.solid()
+ shape.fill.fore_color.rgb = color(fill)
+
+
+def set_line(shape, line: str = "line", width: float = 0.8) -> None:
+ shape.line.color.rgb = color(line)
+ shape.line.width = Pt(width)
+
+
+def apply_font(run, size: float, fill: str = "ink", bold: bool = False) -> None:
+ run.font.name = FONT
+ run.font.size = Pt(size)
+ run.font.color.rgb = color(fill)
+ run.font.bold = bold
+
+
+def add_text(
+ slide,
+ x,
+ y,
+ w,
+ h,
+ value: str,
+ size: float = 16,
+ fill: str = "ink",
+ bold: bool = False,
+ align=PP_ALIGN.LEFT,
+ anchor=MSO_ANCHOR.TOP,
+ margin: float = 0.02,
+):
+ box = slide.shapes.add_textbox(x, y, w, h)
+ frame = box.text_frame
+ frame.clear()
+ frame.word_wrap = True
+ frame.margin_left = Inches(margin)
+ frame.margin_right = Inches(margin)
+ frame.margin_top = Inches(margin)
+ frame.margin_bottom = Inches(margin)
+ frame.vertical_anchor = anchor
+ for index, line in enumerate(value.split("\n")):
+ paragraph = frame.paragraphs[0] if index == 0 else frame.add_paragraph()
+ paragraph.alignment = align
+ paragraph.space_after = Pt(0)
+ paragraph.line_spacing = 1.0
+ run = paragraph.add_run()
+ run.text = line
+ apply_font(run, size, fill, bold)
+ return box
+
+
+def rect(slide, x, y, w, h, fill: str = "white", line: str = "line", radius: bool = False):
+ kind = MSO_AUTO_SHAPE_TYPE.ROUNDED_RECTANGLE if radius else MSO_AUTO_SHAPE_TYPE.RECTANGLE
+ shape = slide.shapes.add_shape(kind, x, y, w, h)
+ set_fill(shape, fill)
+ set_line(shape, line)
+ return shape
+
+
+def pill(slide, x, y, w, h, label: str, fill: str, text_fill: str = "white", size: float = 11):
+ shape = rect(slide, x, y, w, h, fill, fill, radius=True)
+ add_text(slide, x, y, w, h, label, size, text_fill, True, PP_ALIGN.CENTER, MSO_ANCHOR.MIDDLE)
+ return shape
+
+
+def add_image(slide, path: Path, x, y, w, h, line: str = "line"):
+ rect(slide, x, y, w, h, "white", line, radius=True)
+ with Image.open(path) as image:
+ width, height = image.size
+ ratio = min(w / width, h / height)
+ placed_w = int(width * ratio)
+ placed_h = int(height * ratio)
+ placed_x = x + int((w - placed_w) / 2)
+ placed_y = y + int((h - placed_h) / 2)
+ return slide.shapes.add_picture(str(path), placed_x, placed_y, placed_w, placed_h)
+
+
+def add_notes(slide, text: str) -> None:
+ frame = slide.notes_slide.notes_text_frame
+ frame.clear()
+ frame.text = text
+
+
+def base_background(slide, dark: bool = False) -> None:
+ slide.background.fill.solid()
+ slide.background.fill.fore_color.rgb = color("navy" if dark else "bg")
+ x = 0.0
+ for fill, width in (("blue", 3.333), ("red", 3.333), ("yellow", 3.333), ("green", 3.333)):
+ bar = rect(slide, Inches(x), 0, Inches(width), Inches(0.065), fill, fill)
+ bar.line.fill.background()
+ x += width
+
+
+def header(slide, section: str, title: str, subtitle: str, page: str) -> None:
+ base_background(slide)
+ pill(
+ slide,
+ Inches(0.56),
+ Inches(0.27),
+ Inches(1.42),
+ Inches(0.32),
+ section,
+ "pale_blue",
+ "blue",
+ 9.5,
+ )
+ add_text(slide, Inches(0.56), Inches(0.72), Inches(11.6), Inches(0.45), title, 23, "ink", True)
+ add_text(slide, Inches(0.58), Inches(1.18), Inches(11.3), Inches(0.3), subtitle, 11.5, "muted")
+ rect(slide, Inches(0.58), Inches(1.6), Inches(0.62), Inches(0.045), "green", "green")
+ footer(slide, page)
+
+
+def footer(slide, page: str, source: str | None = None) -> None:
+ add_text(
+ slide,
+ Inches(0.56),
+ Inches(7.08),
+ Inches(5.4),
+ Inches(0.18),
+ "GIS Data Agent · Gemma 4 AI Agent Finals",
+ 8,
+ "muted",
+ )
+ if source:
+ add_text(
+ slide,
+ Inches(5.0),
+ Inches(7.08),
+ Inches(6.9),
+ Inches(0.18),
+ source,
+ 7.2,
+ "muted",
+ False,
+ PP_ALIGN.RIGHT,
+ )
+ add_text(
+ slide,
+ Inches(12.22),
+ Inches(7.06),
+ Inches(0.55),
+ Inches(0.2),
+ page,
+ 8.5,
+ "muted",
+ False,
+ PP_ALIGN.RIGHT,
+ )
+
+
+def metric_block(slide, x, y, w, value: str, label: str, note: str, accent: str):
+ rect(slide, x, y, w, Inches(1.08), "white", "line", radius=True)
+ rect(slide, x, y, Inches(0.055), Inches(1.08), accent, accent)
+ add_text(
+ slide,
+ x + Inches(0.18),
+ y + Inches(0.12),
+ w - Inches(0.32),
+ Inches(0.32),
+ value,
+ 22,
+ accent,
+ True,
+ )
+ add_text(
+ slide,
+ x + Inches(0.18),
+ y + Inches(0.51),
+ w - Inches(0.32),
+ Inches(0.2),
+ label,
+ 10.5,
+ "ink",
+ True,
+ )
+ add_text(
+ slide,
+ x + Inches(0.18),
+ y + Inches(0.77),
+ w - Inches(0.32),
+ Inches(0.18),
+ note,
+ 8.2,
+ "muted",
+ )
+
+
+def flow_arrow(slide, x1, y1, x2, y2, fill: str = "muted"):
+ line = slide.shapes.add_connector(MSO_CONNECTOR.STRAIGHT, x1, y1, x2, y2)
+ line.line.color.rgb = color(fill)
+ line.line.width = Pt(1.6)
+ line.line.end_arrowhead = True
+ return line
+
+
+def slide_cover(prs: Presentation, evidence: dict[str, Any], assets: dict[str, Path]):
+ slide = prs.slides.add_slide(prs.slide_layouts[6])
+ base_background(slide, dark=True)
+ rect(slide, Inches(0.0), Inches(0.065), Inches(7.35), Inches(7.435), "bg", "bg")
+ add_text(
+ slide,
+ Inches(0.72),
+ Inches(0.86),
+ Inches(5.9),
+ Inches(0.55),
+ "GIS Data Agent",
+ 34,
+ "ink",
+ True,
+ )
+ add_text(
+ slide,
+ Inches(0.74),
+ Inches(1.45),
+ Inches(5.95),
+ Inches(0.36),
+ "Gemma 4 驱动的受控空间决策 Agent",
+ 17,
+ "blue",
+ True,
+ )
+ add_text(
+ slide,
+ Inches(0.74),
+ Inches(2.07),
+ Inches(5.9),
+ Inches(1.05),
+ "让自然语言问题落到\n可执行、可审计、可复用的 GIS 行动",
+ 23,
+ "ink",
+ True,
+ )
+ stages = [("问", "blue"), ("算", "green"), ("验", "yellow"), ("记", "red")]
+ for index, (label, accent) in enumerate(stages):
+ x = Inches(0.78 + index * 1.35)
+ pill(slide, x, Inches(3.46), Inches(0.76), Inches(0.55), label, accent, "white", 16)
+ if index < len(stages) - 1:
+ add_text(
+ slide,
+ x + Inches(0.85),
+ Inches(3.56),
+ Inches(0.26),
+ Inches(0.22),
+ "→",
+ 14,
+ "muted",
+ True,
+ )
+ value = evidence["result"]
+ mini = [
+ ("90.4%", "CQ-125", "blue"),
+ ("30/30", "Gemma 4 + ADK", "green"),
+ ("硬约束校验", "真实 MPC 规划", "red"),
+ ]
+ for index, (number, label, accent) in enumerate(mini):
+ x = Inches(0.74 + index * 2.02)
+ rect(slide, x, Inches(4.65), Inches(1.72), Inches(0.94), "white", "line", radius=True)
+ add_text(
+ slide,
+ x + Inches(0.15),
+ Inches(4.8),
+ Inches(1.42),
+ Inches(0.26),
+ number,
+ 15,
+ accent,
+ True,
+ PP_ALIGN.CENTER,
+ )
+ add_text(
+ slide,
+ x + Inches(0.15),
+ Inches(5.18),
+ Inches(1.42),
+ Inches(0.18),
+ label,
+ 8.5,
+ "muted",
+ False,
+ PP_ALIGN.CENTER,
+ )
+ add_text(
+ slide,
+ Inches(0.76),
+ Inches(6.45),
+ Inches(5.7),
+ Inches(0.2),
+ "周宁 · Gemma 4 AI Agent 赛道 A 决赛",
+ 9.5,
+ "muted",
+ )
+
+ add_image(slide, assets["map"], Inches(7.66), Inches(0.74), Inches(5.12), Inches(5.72), "navy")
+ pill(
+ slide,
+ Inches(8.0),
+ Inches(5.98),
+ Inches(2.15),
+ Inches(0.42),
+ "101,657 条空间记录",
+ "white",
+ "navy",
+ 9.5,
+ )
+ pill(
+ slide,
+ Inches(10.36),
+ Inches(5.98),
+ Inches(2.03),
+ Inches(0.42),
+ f"坡度 {value['slope_change_pct']:.3f}%",
+ "green",
+ "white",
+ 9.5,
+ )
+ add_text(
+ slide,
+ Inches(7.82),
+ Inches(6.72),
+ Inches(4.75),
+ Inches(0.22),
+ "县域耕地空间优化引擎 · 真实 MPC 规划产物",
+ 9,
+ "white",
+ True,
+ PP_ALIGN.CENTER,
+ )
+ add_text(
+ slide,
+ Inches(12.18),
+ Inches(7.05),
+ Inches(0.6),
+ Inches(0.2),
+ "01",
+ 8.5,
+ "white",
+ False,
+ PP_ALIGN.RIGHT,
+ )
+ add_notes(
+ slide,
+ (
+ "0:00-0:30。开场：GIS Data Agent 不是会聊天的地图，而是把自然语言问题落到"
+ "可执行、可审计、可复用 GIS 行动的受控 Agent。今天只展示两件事：高频空间"
+ "问数，以及高价值县域规划。右侧不是示意图，是本次决赛版本真实 MPC 规划产物。"
+ ),
+ )
+ return slide
+
+
+def slide_problem_solution(prs: Presentation, evidence: dict[str, Any], assets: dict[str, Path]):
+ del evidence, assets
+ slide = prs.slides.add_slide(prs.slide_layouts[6])
+ header(
+ slide,
+ "真实影响 30%",
+ "空间数据不是问答题，而是带约束的执行题",
+ "同一个 Agent 覆盖高频问数与低频高价值规划，但每一步都必须留下证据。",
+ "02",
+ )
+
+ rect(
+ slide, Inches(0.65), Inches(1.94), Inches(5.78), Inches(2.52), "white", "line", radius=True
+ )
+ pill(
+ slide,
+ Inches(0.92),
+ Inches(2.2),
+ Inches(1.24),
+ Inches(0.34),
+ "高频刚需",
+ "blue",
+ "white",
+ 9.5,
+ )
+ add_text(
+ slide,
+ Inches(0.92),
+ Inches(2.73),
+ Inches(4.95),
+ Inches(0.35),
+ "NL2Semantic2GeoSQL",
+ 18,
+ "ink",
+ True,
+ )
+ add_text(
+ slide,
+ Inches(0.92),
+ Inches(3.2),
+ Inches(5.0),
+ Inches(0.64),
+ (
+ "空间距离、面积、SRID、geometry/geography、空间 join 去重，都会让普通 "
+ "NL2SQL 的“看似正确”在执行时失败。"
+ ),
+ 12.2,
+ "muted",
+ )
+ add_text(
+ slide,
+ Inches(0.92),
+ Inches(4.02),
+ Inches(5.0),
+ Inches(0.2),
+ "用户：GIS 分析师、空间数据库团队",
+ 10.5,
+ "blue",
+ True,
+ )
+
+ rect(slide, Inches(6.9), Inches(1.94), Inches(5.78), Inches(2.52), "white", "line", radius=True)
+ pill(
+ slide,
+ Inches(7.17),
+ Inches(2.2),
+ Inches(1.52),
+ Inches(0.34),
+ "高价值决策",
+ "green",
+ "white",
+ 9.5,
+ )
+ add_text(
+ slide,
+ Inches(7.17),
+ Inches(2.73),
+ Inches(4.95),
+ Inches(0.35),
+ "县域耕地空间布局优化",
+ 18,
+ "ink",
+ True,
+ )
+ add_text(
+ slide,
+ Inches(7.17),
+ Inches(3.2),
+ Inches(5.0),
+ Inches(0.64),
+ "结果必须同时满足耕地面积不减少、坡度下降、连片度提升，并生成可交付的空间成果，而不是文字建议。",
+ 12.2,
+ "muted",
+ )
+ add_text(
+ slide,
+ Inches(7.17),
+ Inches(4.02),
+ Inches(5.0),
+ Inches(0.2),
+ "用户：自然资源、国土规划、县域整治人员",
+ 10.5,
+ "green",
+ True,
+ )
+
+ flow_y = Inches(5.18)
+ steps = [
+ ("目标", "Gemma 4", "blue"),
+ ("运行时", "Google ADK", "green"),
+ ("行动", "PostGIS / MPC 引擎", "yellow"),
+ ("校验", "硬约束校验", "red"),
+ ("复用", "已验证经验库", "cyan"),
+ ]
+ for index, (kind, label, accent) in enumerate(steps):
+ x = Inches(0.62 + index * 2.54)
+ rect(slide, x, flow_y, Inches(2.03), Inches(0.86), "white", "line", radius=True)
+ add_text(
+ slide,
+ x + Inches(0.14),
+ flow_y + Inches(0.12),
+ Inches(1.75),
+ Inches(0.16),
+ kind,
+ 8.5,
+ accent,
+ True,
+ PP_ALIGN.CENTER,
+ )
+ add_text(
+ slide,
+ x + Inches(0.14),
+ flow_y + Inches(0.42),
+ Inches(1.75),
+ Inches(0.2),
+ label,
+ 11.2,
+ "ink",
+ True,
+ PP_ALIGN.CENTER,
+ )
+ if index < len(steps) - 1:
+ flow_arrow(
+ slide,
+ x + Inches(2.08),
+ flow_y + Inches(0.43),
+ x + Inches(2.41),
+ flow_y + Inches(0.43),
+ )
+ add_text(
+ slide,
+ Inches(1.0),
+ Inches(6.38),
+ Inches(11.3),
+ Inches(0.32),
+ "核心边界：模型负责选择和解释，确定性工具负责计算，治理代码决定结果能否保存和复用。",
+ 13,
+ "navy",
+ True,
+ PP_ALIGN.CENTER,
+ )
+ add_notes(
+ slide,
+ (
+ "0:30-1:05。空间问数是高频刚需，难点不是生成 SQL，而是空间语义与真实执行。"
+ "县域规划频率更低，但每次价值更高，必须满足业务硬约束并交付空间成果。架构上 "
+ "Gemma 4 做决策，ADK 承载原生函数调用，PostGIS 与县域耕地空间优化引擎"
+ "做确定性计算，硬约束校验决定结果能否进入已验证经验库。"
+ ),
+ )
+ return slide
+
+
+def slide_nl2sql(prs: Presentation, evidence: dict[str, Any], assets: dict[str, Path]):
+ selected = next(row for row in evidence["benchmark"] if row["model"] == "Gemma4:26b")
+ slide = prs.slides.add_slide(prs.slide_layouts[6])
+ header(
+ slide,
+ "Demo 1 · 0:50",
+ "NL2Semantic2GeoSQL：不是生成 SQL，而是让空间语义真正执行",
+ "同一 CQ-125 基准选模型；现场锁定一个问题、一段 GeoSQL、一个可核查地图结果。",
+ "03",
+ )
+ add_image(
+ slide, assets["nl2sql"], Inches(0.62), Inches(1.86), Inches(4.45), Inches(4.92), "line"
+ )
+ add_image(
+ slide, assets["benchmark"], Inches(5.42), Inches(1.84), Inches(7.25), Inches(2.78), "line"
+ )
+
+ rect(
+ slide, Inches(5.42), Inches(4.91), Inches(7.25), Inches(1.85), "white", "line", radius=True
+ )
+ pill(
+ slide,
+ Inches(5.7),
+ Inches(5.16),
+ Inches(1.2),
+ Inches(0.34),
+ "锁定问题",
+ "blue",
+ "white",
+ 9.5,
+ )
+ add_text(
+ slide,
+ Inches(7.08),
+ Inches(5.08),
+ Inches(5.1),
+ Inches(0.38),
+ "最长桥梁 100 米范围内有多少高德 POI？",
+ 14,
+ "ink",
+ True,
+ )
+ add_text(
+ slide,
+ Inches(5.7),
+ Inches(5.56),
+ Inches(6.45),
+ Inches(0.24),
+ "语义 / Schema Grounding → Gemma 4 生成 GeoSQL → 只读执行 → 地图核验",
+ 9.6,
+ "blue",
+ True,
+ )
+ add_text(
+ slide,
+ Inches(5.7),
+ Inches(5.95),
+ Inches(4.6),
+ Inches(0.27),
+ "ST_DWithin(...::geography, 100)",
+ 12.5,
+ "navy",
+ True,
+ )
+ pill(
+ slide,
+ Inches(10.62),
+ Inches(5.84),
+ Inches(1.55),
+ Inches(0.52),
+ "35 + 地图",
+ "green",
+ "white",
+ 15,
+ )
+ add_text(
+ slide,
+ Inches(5.7),
+ Inches(6.4),
+ Inches(6.45),
+ Inches(0.2),
+ (
+ f"Gemma 4 26B：{selected['full_ex_correct']}/{selected['full_ex_total']} = "
+ f"{float(selected['full_ex_pct']):.1f}% · 只比 31B 少 1 题，约快 37.6%"
+ ),
+ 9.5,
+ "muted",
+ )
+ footer(slide, "03", "证据：CQ-125 模型选型 + 锁定 PostGIS 演示快照")
+ add_notes(
+ slide,
+ (
+ "1:05-1:55。这里先播放约 35 秒锁定视频，或直接展示结果。问题是最长桥梁 100 米"
+ "范围内有多少高德 POI。系统先做语义与 schema grounding，再由 Gemma 4 生成 GeoSQL，"
+ "关键是 geography 米制距离和只读执行；结果为 35，并自动加载同一 PostGIS 查询快照的"
+ "桥梁、百米范围和 35 个 POI。模型不是凭印象选的：同一 CQ-125 上 "
+ "26B 为 113/125，只比 31B 少一题，但完整运行约快 37.6%。"
+ ),
+ )
+ return slide
+
+
+def slide_farmland_planning(
+ prs: Presentation,
+ evidence: dict[str, Any],
+ assets: dict[str, Path],
+):
+ result = evidence["result"]
+ summary = evidence["summary"]
+ spatial_output = summary["shapefile_output"]
+ config = summary["config"]
+ slide = prs.slides.add_slide(prs.slide_layouts[6])
+ header(
+ slide,
+ "Demo 2 · 1:30",
+ "Gemma 4 + 地理空间世界模型领域原型",
+ (
+ "县域耕地空间优化引擎预测行动后果并搜索方案；Gemma 4 + ADK 负责目标理解、"
+ "工具决策与闭环控制。"
+ ),
+ "04",
+ )
+ add_image(slide, assets["map"], Inches(0.62), Inches(1.84), Inches(5.18), Inches(4.95), "line")
+
+ trace = [
+ ("1", "状态检查", "观察版本", "blue"),
+ ("2", "资源检查", "判断复用", "green"),
+ ("3", "经验召回", "注入上下文", "cyan"),
+ ("4", "MPC 规划", "执行空间行动", "yellow"),
+ ("5", "硬约束审计", "独立验收", "red"),
+ ("6", "经验写入 / 人工", "按结果分支", "green"),
+ ]
+ pill(
+ slide,
+ Inches(6.18),
+ Inches(1.84),
+ Inches(3.86),
+ Inches(0.34),
+ "LLM 控制面 · Gemma 4 + ADK · 6 次函数调用",
+ "pale_blue",
+ "blue",
+ 8.8,
+ )
+ for index, (number, tool, meaning, accent) in enumerate(trace):
+ y = Inches(2.35 + index * 0.43)
+ pill(slide, Inches(6.18), y, Inches(0.4), Inches(0.3), number, accent, "white", 9)
+ add_text(
+ slide,
+ Inches(6.75),
+ y - Inches(0.01),
+ Inches(1.82),
+ Inches(0.22),
+ tool,
+ 10.3,
+ "ink",
+ True,
+ )
+ add_text(
+ slide,
+ Inches(8.7),
+ y - Inches(0.01),
+ Inches(1.34),
+ Inches(0.22),
+ meaning,
+ 8.7,
+ "muted",
+ )
+ if index < len(trace) - 1:
+ flow_arrow(
+ slide,
+ Inches(6.38),
+ y + Inches(0.31),
+ Inches(6.38),
+ y + Inches(0.4),
+ accent,
+ )
+
+ rect(
+ slide, Inches(10.38), Inches(1.95), Inches(2.27), Inches(2.95), "navy", "navy", radius=True
+ )
+ add_text(
+ slide,
+ Inches(10.62),
+ Inches(2.2),
+ Inches(1.78),
+ Inches(0.24),
+ "真实 MPC 规划",
+ 12,
+ "white",
+ True,
+ PP_ALIGN.CENTER,
+ )
+ metrics = [
+ (f"{spatial_output['n_input']:,}", "输入空间记录"),
+ (f"{spatial_output['n_in_env']:,}", "环境图斑"),
+ (f"{config['n_blocks']:,}", "空间块"),
+ (f"{result['swaps_completed']:,}", "双向置换"),
+ ]
+ for index, (value, label) in enumerate(metrics):
+ y = Inches(2.72 + index * 0.48)
+ add_text(
+ slide,
+ Inches(10.58),
+ y,
+ Inches(0.84),
+ Inches(0.22),
+ value,
+ 12.5,
+ "white",
+ True,
+ PP_ALIGN.RIGHT,
+ )
+ add_text(
+ slide, Inches(11.55), y + Inches(0.02), Inches(0.78), Inches(0.18), label, 7.9, "white"
+ )
+
+ rect(
+ slide,
+ Inches(6.15),
+ Inches(5.02),
+ Inches(6.5),
+ Inches(0.37),
+ "pale_yellow",
+ "yellow",
+ radius=True,
+ )
+ add_text(
+ slide,
+ Inches(6.3),
+ Inches(5.1),
+ Inches(6.2),
+ Inches(0.18),
+ "GWM 领域原型 · S 空间状态 + A 候选置换 → Tθ 转移预测 → MPC 搜索 → 校验",
+ 9.2,
+ "navy",
+ True,
+ PP_ALIGN.CENTER,
+ )
+
+ changes = [
+ (f"+{result['cultivated_area_change_ha']:.3f} ha", "耕地面积", "green"),
+ (f"{result['slope_change_pct']:.3f}%", "平均坡度", "blue"),
+ (f"+{result['cont_change']:.4f}", "连片度", "cyan"),
+ (f"+{result['baimu_area_change_ha']:.2f} ha", "百亩方", "red"),
+ ]
+ for index, (value, label, accent) in enumerate(changes):
+ x = Inches(6.15 + (index % 2) * 3.3)
+ y = Inches(5.55 + (index // 2) * 0.66)
+ rect(slide, x, y, Inches(3.02), Inches(0.58), "white", "line", radius=True)
+ add_text(
+ slide,
+ x + Inches(0.16),
+ y + Inches(0.11),
+ Inches(1.6),
+ Inches(0.23),
+ value,
+ 12,
+ accent,
+ True,
+ )
+ add_text(
+ slide,
+ x + Inches(1.84),
+ y + Inches(0.13),
+ Inches(0.92),
+ Inches(0.2),
+ label,
+ 9,
+ "muted",
+ False,
+ PP_ALIGN.RIGHT,
+ )
+ footer(
+ slide,
+ "04",
+ "当前边界：领域化、基于模型的空间规划原型，不等同于完整通用 GWM",
+ )
+ add_notes(
+ slide,
+ (
+ "1:55-3:25。现场运行成功链或播放约 70 秒同版本视频。Gemma 4 通过 Google ADK"
+ "原生函数调用检查版本与资源、召回经验，并根据观察选择快速 MPC 路径；硬约束审计"
+ "通过才写入已验证经验库，失败只允许重规划一次，再失败转人工。空间算法使用学习型"
+ "状态转移模型集成预测候选行动，再由 MPC 搜索，确定性 Python 负责数值计算。Paper9"
+ "不是完整 GWM，而是它的领域原型：图斑与空间块构成状态，地类置换构成行动，学习型"
+ "转移模型预测行动后果，MPC 消费这些预测并搜索方案。县域耕地空间优化引擎的"
+ "内部研发代号是 Paper9，但主路演统一使用业务名称。"
+ f"本次真实运行处理 {spatial_output['n_input']:,} 条输入、"
+ f"{spatial_output['n_in_env']:,} 个环境图斑、{config['n_blocks']:,} 个空间块，"
+ f"完成 {result['swaps_completed']:,} 对置换；耕地面积不减少，坡度下降，"
+ "连片度和百亩方面积提升。A/B/C/D 是算法阶段，不冒充 Gemma 4 动态规划。"
+ ),
+ )
+ return slide
+
+
+def slide_evidence(prs: Presentation, evidence: dict[str, Any], assets: dict[str, Path]):
+ del assets
+ passed = evidence["reliability_passed"]
+ total = evidence["reliability_total"]
+ slide = prs.slides.add_slide(prs.slide_layouts[6])
+ header(
+ slide,
+ "证据 · 1:00",
+ "这不是一次成功 Demo，而是一套分层证据",
+ "模型编排、算法运行、工程质量和历史环境记录分别证明不同问题。",
+ "05",
+ )
+ blocks = [
+ (f"{passed}/{total}", "Gemma 4 + ADK", "三场景各 10 次", "blue"),
+ ("236", "镜像运行时测试", "另有 6 交付 + 52 兼容", "green"),
+ ("3/3", "容器 healthy", "app / PostGIS / Redis", "yellow"),
+ ("硬约束校验", "真实 MPC 规划", "1 次规划 + 空间产物", "red"),
+ ]
+ for index, block in enumerate(blocks):
+ metric_block(slide, Inches(0.66 + index * 3.12), Inches(1.92), Inches(2.72), *block)
+
+ rect(slide, Inches(0.66), Inches(3.36), Inches(7.3), Inches(2.7), "white", "line", radius=True)
+ add_text(
+ slide,
+ Inches(0.96),
+ Inches(3.62),
+ Inches(6.65),
+ Inches(0.28),
+ "可靠性基线：成功、恢复、停止都可重复",
+ 15,
+ "ink",
+ True,
+ )
+ rows = [
+ ("首次通过", "6 次函数调用", "10/10", "7.97 s"),
+ ("版本不兼容", "2 次调用 → 停止", "10/10", "3.24 s"),
+ ("一次重规划", "8 次函数调用", "10/10", "13.57 s"),
+ ]
+ for index, row in enumerate(rows):
+ y = Inches(4.16 + index * 0.47)
+ if index % 2 == 0:
+ rect(
+ slide,
+ Inches(0.92),
+ y - Inches(0.04),
+ Inches(6.76),
+ Inches(0.38),
+ "pale_blue",
+ "pale_blue",
+ )
+ for col, (value, width) in enumerate(zip(row, (1.5, 1.85, 1.2, 1.2), strict=True)):
+ x = Inches(1.02 + sum((1.5, 1.85, 1.2, 1.2)[:col]))
+ add_text(slide, x, y, Inches(width), Inches(0.2), value, 10.2, "ink", col == 0)
+ add_text(
+ slide,
+ Inches(0.96),
+ Inches(5.7),
+ Inches(6.65),
+ Inches(0.2),
+ "30/30 Wilson 95% CI：88.65%–100% · 工具响应为确定性替身",
+ 8.8,
+ "muted",
+ )
+
+ rect(
+ slide,
+ Inches(8.3),
+ Inches(3.36),
+ Inches(4.38),
+ Inches(2.7),
+ "pale_yellow",
+ "yellow",
+ radius=True,
+ )
+ add_text(
+ slide,
+ Inches(8.62),
+ Inches(3.63),
+ Inches(3.72),
+ Inches(0.3),
+ "真实环境可行性：证据分层",
+ 14,
+ "ink",
+ True,
+ )
+ add_text(
+ slide,
+ Inches(8.62),
+ Inches(4.18),
+ Inches(3.72),
+ Inches(0.96),
+ (
+ "历史版本：内部交接材料记录在目标内网使用真实权威数据完成全流程。\n"
+ "当前版本：0.3.3 / 2.2.3 本机 MPC 完成；\n硬约束校验通过。"
+ ),
+ 11.2,
+ "ink",
+ )
+ add_text(
+ slide,
+ Inches(8.62),
+ Inches(5.34),
+ Inches(3.72),
+ Inches(0.4),
+ "历史记录 ≠ 当前部内复测 ≠ GIS Data Agent 生产部署",
+ 9.5,
+ "muted",
+ True,
+ )
+ footer(slide, "05", "证据分类相互独立；详见 claim_register.md")
+ add_notes(
+ slide,
+ (
+ "3:25-4:20。这不是一次跑通就算完成。Gemma 4 与 ADK 在首次成功、版本阻断和"
+ "一次恢复三个分支各跑十次，30/30 通过；Wilson 区间说明我们没有把它夸大成"
+ "普遍 100%。工程侧分为 236 个镜像运行时测试、6 个宿主交付契约、52 个兼容"
+ "测试和三个健康容器；这些范围不同，不能混成一个总数。真实"
+ "环境证据也严格分层：历史内网记录证明底层耕地优化引擎的工程可行性，当前 "
+ "v2.2.3 本机运行证明适配与 MPC 规划，不把两者混成生产验收。"
+ ),
+ )
+ return slide
+
+
+def slide_close(prs: Presentation, evidence: dict[str, Any], assets: dict[str, Path]):
+ del evidence, assets
+ slide = prs.slides.add_slide(prs.slide_layouts[6])
+ base_background(slide, dark=True)
+ add_text(
+ slide,
+ Inches(0.72),
+ Inches(0.78),
+ Inches(11.5),
+ Inches(0.56),
+ "下一代空间智能体：LLM + GWM",
+ 30,
+ "white",
+ True,
+ )
+ add_text(
+ slide,
+ Inches(0.74),
+ Inches(1.46),
+ Inches(11.2),
+ Inches(0.38),
+ "语言智能负责理解与协同，地理空间世界模型负责推演行动后果。",
+ 16.5,
+ "white",
+ )
+ rect(slide, Inches(0.74), Inches(2.25), Inches(11.9), Inches(0.055), "green", "green")
+
+ columns = [
+ ("Gemma 4 + ADK", "理解目标\n选择工具\n人机协同", "blue"),
+ ("GWM", "表示空间状态\n预测行动后果\n反事实推演", "green"),
+ ("规划器", "MPC / 搜索\n方案比较\n行动选择", "yellow"),
+ ("审核控制", "规则校验\n证据边界\n保存 / 人工", "red"),
+ ]
+ for index, (title, body, accent) in enumerate(columns):
+ x = Inches(0.72 + index * 3.13)
+ rect(slide, x, Inches(2.78), Inches(2.72), Inches(2.2), "white", "white", radius=True)
+ pill(
+ slide,
+ x + Inches(0.25),
+ Inches(3.04),
+ Inches(1.55),
+ Inches(0.35),
+ title,
+ accent,
+ "white",
+ 9.5,
+ )
+ add_text(
+ slide,
+ x + Inches(0.28),
+ Inches(3.68),
+ Inches(2.15),
+ Inches(0.92),
+ body,
+ 12.2,
+ "navy",
+ True,
+ PP_ALIGN.LEFT,
+ )
+ if index < len(columns) - 1:
+ add_text(
+ slide,
+ x + Inches(2.78),
+ Inches(3.72),
+ Inches(0.28),
+ Inches(0.3),
+ "→",
+ 16,
+ "white",
+ True,
+ PP_ALIGN.CENTER,
+ )
+
+ rect(
+ slide,
+ Inches(0.88),
+ Inches(5.43),
+ Inches(11.58),
+ Inches(0.76),
+ "white",
+ "white",
+ radius=True,
+ )
+ add_text(
+ slide,
+ Inches(1.12),
+ Inches(5.58),
+ Inches(2.0),
+ Inches(0.22),
+ "今天已验证",
+ 12,
+ "green",
+ True,
+ )
+ add_text(
+ slide,
+ Inches(2.55),
+ Inches(5.56),
+ Inches(9.55),
+ Inches(0.28),
+ "空间问数 + GWM 领域原型 + 硬约束校验 + 已验证经验",
+ 12,
+ "navy",
+ True,
+ )
+ add_text(
+ slide,
+ Inches(1.12),
+ Inches(6.45),
+ Inches(11.1),
+ Inches(0.26),
+ "路线：多尺度状态 · 多行动动力学 · 长期/不确定性推演 · 因果校准 · 证据校验",
+ 12.5,
+ "white",
+ True,
+ PP_ALIGN.CENTER,
+ )
+ add_text(
+ slide,
+ Inches(12.18),
+ Inches(7.05),
+ Inches(0.6),
+ Inches(0.2),
+ "06",
+ 8.5,
+ "white",
+ False,
+ PP_ALIGN.RIGHT,
+ )
+ add_notes(
+ slide,
+ (
+ "4:20-4:40。总结：我们的目标不是不断给 LLM 增加 GIS 工具，而是走向 LLM + GWM。"
+ "Gemma 4 与 ADK 负责理解目标、选择工具和人机协同；GWM 负责表示空间状态、预测"
+ "行动后果和反事实推演；MPC 消费预测做方案选择，审核机制决定能否保存。今天已经"
+ "验证空间问数、GWM 领域原型和受控闭环，下一步扩展多尺度、多行动、长期与"
+ "不确定性推演。谢谢。"
+ ),
+ )
+ return slide
+
+
+def appendix_header(slide, title: str, subtitle: str, page: str):
+ header(slide, "Q&A 附录", title, subtitle, page)
+
+
+def appendix_architecture(prs: Presentation, evidence: dict[str, Any], assets: dict[str, Path]):
+ del evidence, assets
+ slide = prs.slides.add_slide(prs.slide_layouts[6])
+ appendix_header(
+ slide,
+ "Gemma 4 真的在规划，还是 Python 固定流程？",
+ "直接结论：Gemma 4 规划工具调用与恢复分支；确定性代码负责空间数值计算。",
+ "A1",
+ )
+ rect(slide, Inches(0.72), Inches(1.88), Inches(5.9), Inches(3.55), "white", "line", radius=True)
+ pill(
+ slide,
+ Inches(0.98),
+ Inches(2.12),
+ Inches(2.25),
+ Inches(0.38),
+ "Agent 规划 · Gemma 4 + ADK",
+ "blue",
+ "white",
+ 9.3,
+ )
+ add_text(
+ slide,
+ Inches(4.95),
+ Inches(2.17),
+ Inches(1.35),
+ Inches(0.2),
+ "模型动态选择",
+ 9.5,
+ "blue",
+ True,
+ PP_ALIGN.RIGHT,
+ )
+ agent_steps = ["理解目标", "版本资源\n检查", "召回经验", "选择工具", "观察后分支"]
+ for index, label in enumerate(agent_steps):
+ x = Inches(0.98 + index * 1.08)
+ rect(slide, x, Inches(3.0), Inches(0.88), Inches(0.88), "pale_blue", "blue", radius=True)
+ add_text(
+ slide,
+ x + Inches(0.08),
+ Inches(3.19),
+ Inches(0.72),
+ Inches(0.44),
+ label,
+ 8.6,
+ "blue",
+ True,
+ PP_ALIGN.CENTER,
+ MSO_ANCHOR.MIDDLE,
+ )
+ if index < len(agent_steps) - 1:
+ flow_arrow(
+ slide,
+ x + Inches(0.89),
+ Inches(3.44),
+ x + Inches(1.03),
+ Inches(3.44),
+ "blue",
+ )
+ add_text(
+ slide,
+ Inches(1.0),
+ Inches(4.33),
+ Inches(5.3),
+ Inches(0.54),
+ "可停止 · 可保存 · 可重规划一次 · 可转人工",
+ 11.5,
+ "ink",
+ True,
+ PP_ALIGN.CENTER,
+ )
+
+ rect(
+ slide,
+ Inches(6.82),
+ Inches(1.88),
+ Inches(5.78),
+ Inches(3.55),
+ "white",
+ "line",
+ radius=True,
+ )
+ pill(
+ slide,
+ Inches(7.08),
+ Inches(2.12),
+ Inches(2.7),
+ Inches(0.38),
+ "算法规划 · 县域耕地优化引擎",
+ "green",
+ "white",
+ 9.3,
+ )
+ add_text(
+ slide,
+ Inches(11.08),
+ Inches(2.17),
+ Inches(1.22),
+ Inches(0.2),
+ "确定性执行",
+ 9.5,
+ "green",
+ True,
+ PP_ALIGN.RIGHT,
+ )
+ algorithm_steps = [
+ ("S", "空间状态"),
+ ("Tθ", "预测后果"),
+ ("MPC", "搜索方案"),
+ ("校验", "验收成果"),
+ ]
+ for index, (symbol, label) in enumerate(algorithm_steps):
+ x = Inches(7.15 + index * 1.33)
+ rect(slide, x, Inches(2.93), Inches(1.05), Inches(1.02), "pale_green", "green", radius=True)
+ add_text(
+ slide,
+ x,
+ Inches(3.07),
+ Inches(1.05),
+ Inches(0.24),
+ symbol,
+ 13,
+ "green",
+ True,
+ PP_ALIGN.CENTER,
+ )
+ add_text(
+ slide,
+ x,
+ Inches(3.48),
+ Inches(1.05),
+ Inches(0.22),
+ label,
+ 8.8,
+ "ink",
+ True,
+ PP_ALIGN.CENTER,
+ )
+ if index < len(algorithm_steps) - 1:
+ flow_arrow(
+ slide,
+ x + Inches(1.06),
+ Inches(3.44),
+ x + Inches(1.27),
+ Inches(3.44),
+ "green",
+ )
+ add_text(
+ slide,
+ Inches(7.12),
+ Inches(4.33),
+ Inches(5.1),
+ Inches(0.54),
+ "A/B/C/D 是算法阶段，不冒充 LLM 动态规划",
+ 11.5,
+ "ink",
+ True,
+ PP_ALIGN.CENTER,
+ )
+
+ branches = [
+ ("2 次调用", "版本不兼容 → 停止", "red"),
+ ("6 次调用", "首次审计通过 → 保存", "green"),
+ ("8 次调用", "审计失败 → 一次恢复", "yellow"),
+ ]
+ for index, (count, result, accent) in enumerate(branches):
+ x = Inches(0.82 + index * 4.16)
+ rect(slide, x, Inches(5.78), Inches(3.72), Inches(0.76), "white", "line", radius=True)
+ pill(
+ slide,
+ x + Inches(0.18),
+ Inches(5.99),
+ Inches(0.9),
+ Inches(0.34),
+ count,
+ accent,
+ "white",
+ 8.5,
+ )
+ add_text(
+ slide,
+ x + Inches(1.25),
+ Inches(6.02),
+ Inches(2.22),
+ Inches(0.24),
+ result,
+ 9.3,
+ "ink",
+ True,
+ )
+ footer(slide, "A1", "边界：Gemma 4 决策工具与分支；GIS 数值由确定性工具计算")
+ add_notes(
+ slide,
+ (
+ "结论先说：两层规划。Gemma 4 通过 ADK 根据版本、资源、经验和审计反馈选择"
+ "下一工具与停止条件；县域耕地优化引擎则用状态转移模型和 MPC 做空间数值规划。"
+ "三个 2/6/8 次调用分支已分别重复验证，但 A/B/C/D 仍是确定性算法阶段。"
+ ),
+ )
+ return slide
+
+
+def appendix_reliability(prs: Presentation, evidence: dict[str, Any], assets: dict[str, Path]):
+ del evidence, assets
+ slide = prs.slides.add_slide(prs.slide_layouts[6])
+ appendix_header(
+ slide,
+ "县域耕地优化引擎是不是地理空间世界模型？",
+ "直接结论：它是领域化 GWM 原型，不是完整通用地理空间世界模型。",
+ "A2",
+ )
+ stages = [
+ ("空间状态 S", "图斑 · 空间块\n地类 · 坡度", "blue"),
+ ("转移模型 Tθ", "学习型模型集成\n预测行动后果", "green"),
+ ("规划器 MPC", "消费预测\n比较候选方案", "yellow"),
+ ("治理外环", "硬约束审计\n保存 / 转人工", "red"),
+ ]
+ for index, (title, body, accent) in enumerate(stages):
+ x = Inches(0.72 + index * 3.13)
+ rect(slide, x, Inches(2.02), Inches(2.72), Inches(1.78), "white", "line", radius=True)
+ pill(
+ slide,
+ x + Inches(0.2),
+ Inches(2.22),
+ Inches(1.48),
+ Inches(0.36),
+ title,
+ accent,
+ "white",
+ 9.2,
+ )
+ add_text(
+ slide,
+ x + Inches(0.2),
+ Inches(2.88),
+ Inches(2.28),
+ Inches(0.58),
+ body,
+ 10.7,
+ "ink",
+ True,
+ PP_ALIGN.CENTER,
+ )
+ if index < len(stages) - 1:
+ flow_arrow(
+ slide,
+ x + Inches(2.73),
+ Inches(2.91),
+ x + Inches(3.04),
+ Inches(2.91),
+ accent,
+ )
+
+ rect(
+ slide,
+ Inches(0.82),
+ Inches(4.28),
+ Inches(5.72),
+ Inches(1.7),
+ "pale_green",
+ "green",
+ radius=True,
+ )
+ add_text(
+ slide,
+ Inches(1.1),
+ Inches(4.56),
+ Inches(5.12),
+ Inches(0.28),
+ "今天已经具备",
+ 14,
+ "green",
+ True,
+ )
+ add_text(
+ slide,
+ Inches(1.1),
+ Inches(5.05),
+ Inches(5.12),
+ Inches(0.56),
+ "状态表示 · 学习型转移预测 · MPC 搜索\n硬约束校验 · 已验证经验闭环",
+ 10.8,
+ "ink",
+ True,
+ )
+ rect(
+ slide,
+ Inches(6.8),
+ Inches(4.28),
+ Inches(5.72),
+ Inches(1.7),
+ "pale_red",
+ "red",
+ radius=True,
+ )
+ add_text(
+ slide,
+ Inches(7.08),
+ Inches(4.56),
+ Inches(5.12),
+ Inches(0.28),
+ "距离完整 GWM 还缺",
+ 14,
+ "red",
+ True,
+ )
+ add_text(
+ slide,
+ Inches(7.08),
+ Inches(5.05),
+ Inches(5.12),
+ Inches(0.56),
+ "多尺度 / 多行动 · 长期推演与不确定性\n因果校准 · 跨区域验证",
+ 10.8,
+ "ink",
+ True,
+ )
+ footer(slide, "A2", "内部研发代号 Paper9；MPC 是世界模型的消费者，不是世界模型本身")
+ add_notes(
+ slide,
+ (
+ "严格地说，它不是完整 GWM，而是领域化原型。图斑和空间块构成状态，学习型"
+ "状态转移模型集成预测候选行动的后果，这一部分是 GWM 动力学内核的雏形；MPC"
+ "消费预测并搜索方案，硬约束校验负责验收。下一步还要补齐多尺度、多行动、长期"
+ "不确定性推演、因果校准和跨区域验证。"
+ ),
+ )
+ return slide
+
+
+def appendix_claims(prs: Presentation, evidence: dict[str, Any], assets: dict[str, Path]):
+ del assets
+ slide = prs.slides.add_slide(prs.slide_layouts[6])
+ appendix_header(
+ slide,
+ "结果为什么可信，失败时怎么办？",
+ "直接结论：模型编排、真实算法和约束校验分别验证；任何一层失败都不会冒充成功。",
+ "A3",
+ )
+ result = evidence["result"]
+ cards = [
+ (
+ "模型编排证据",
+ "30/30",
+ "首次成功 / 版本阻断 / 一次恢复\nWilson 95% CI：88.65%–100%",
+ "blue",
+ ),
+ (
+ "真实算法证据",
+ f"{result['swaps_completed']} 对置换",
+ "101,657 条输入 · 真实 MPC\n面积不减 · 坡度下降 · 连片度提升",
+ "green",
+ ),
+ (
+ "治理证据",
+ "硬约束校验通过",
+ "产物完整才保存经验\n失败 → 重规划一次 → 转人工",
+ "red",
+ ),
+ ]
+ for index, (title, value, body, accent) in enumerate(cards):
+ x = Inches(0.72 + index * 4.18)
+ rect(slide, x, Inches(2.0), Inches(3.72), Inches(3.15), "white", "line", radius=True)
+ pill(
+ slide,
+ x + Inches(0.25),
+ Inches(2.26),
+ Inches(1.7),
+ Inches(0.38),
+ title,
+ accent,
+ "white",
+ 9.3,
+ )
+ add_text(
+ slide,
+ x + Inches(0.25),
+ Inches(3.0),
+ Inches(3.2),
+ Inches(0.42),
+ value,
+ 18,
+ accent,
+ True,
+ )
+ add_text(
+ slide,
+ x + Inches(0.25),
+ Inches(3.82),
+ Inches(3.2),
+ Inches(0.8),
+ body,
+ 10.5,
+ "ink",
+ )
+ rect(
+ slide,
+ Inches(0.95),
+ Inches(5.72),
+ Inches(11.42),
+ Inches(0.74),
+ "pale_yellow",
+ "yellow",
+ radius=True,
+ )
+ add_text(
+ slide,
+ Inches(1.22),
+ Inches(5.96),
+ Inches(10.88),
+ Inches(0.26),
+ "边界：30/30 验证的是 Gemma 4 + ADK 编排；真实 MPC 运行另行验证，二者不混算。",
+ 11.2,
+ "ink",
+ True,
+ PP_ALIGN.CENTER,
+ )
+ footer(slide, "A3", "任何版本不兼容、二次审计失败或空间产物缺失均安全停止")
+ add_notes(
+ slide,
+ (
+ "可信性不是一个数字。三十次真实 Gemma 4 加 ADK 运行验证工具编排；本机真实 MPC"
+ "运行验证算法和空间产物；硬约束校验决定能否保存。三十次使用确定性工具替身，"
+ "不等于三十次算法重跑。任何版本、审计或产物失败都会停止，最多恢复一次后转人工。"
+ ),
+ )
+ return slide
+
+
+def appendix_google(prs: Presentation, evidence: dict[str, Any], assets: dict[str, Path]):
+ del assets
+ slide = prs.slides.add_slide(prs.slide_layouts[6])
+ appendix_header(
+ slide,
+ "真实落地证据是什么，哪些还没有证明？",
+ "直接结论：需求、规模、历史环境与当前运行均有证据；生产成效仍需继续量化。",
+ "A4",
+ )
+ spatial_output = evidence["summary"]["shapefile_output"]
+ items = [
+ (
+ "真实需求",
+ "GIS 分析师的高频空间问数；自然资源与县域整治人员的高价值布局优化",
+ "受众清晰",
+ "blue",
+ ),
+ (
+ "当前规模",
+ f"{spatial_output['n_input']:,} 条输入空间记录；CQ-125 可复核空间 SQL 基准",
+ "可量化",
+ "green",
+ ),
+ (
+ "历史环境",
+ "底层引擎旧版本的内部交接材料记录：曾在目标内网以真实权威数据完成全流程",
+ "工程可行性",
+ "yellow",
+ ),
+ (
+ "当前运行",
+ "0.3.3 / 2.2.3 本机真实 MPC、地图、图文 PDF、硬约束校验和经验保存",
+ "当前可运行",
+ "red",
+ ),
+ ]
+ for index, (kind, fact, meaning, accent) in enumerate(items):
+ y = Inches(1.9 + index * 1.02)
+ pill(slide, Inches(0.75), y, Inches(1.22), Inches(0.38), kind, accent, "white", 9.3)
+ rect(
+ slide,
+ Inches(2.2),
+ y - Inches(0.08),
+ Inches(10.25),
+ Inches(0.82),
+ "white",
+ "line",
+ radius=True,
+ )
+ add_text(
+ slide,
+ Inches(2.47),
+ y + Inches(0.05),
+ Inches(7.92),
+ Inches(0.35),
+ fact,
+ 10.5,
+ "ink",
+ True,
+ )
+ add_text(
+ slide,
+ Inches(10.58),
+ y + Inches(0.09),
+ Inches(1.5),
+ Inches(0.22),
+ meaning,
+ 9.3,
+ accent,
+ True,
+ PP_ALIGN.RIGHT,
+ )
+ rect(
+ slide,
+ Inches(0.95),
+ Inches(6.02),
+ Inches(11.42),
+ Inches(0.58),
+ "pale_red",
+ "red",
+ radius=True,
+ )
+ add_text(
+ slide,
+ Inches(1.22),
+ Inches(6.18),
+ Inches(10.88),
+ Inches(0.24),
+ "尚未证明：v2.2.3 部内复测或生产验收、全国泛化、公开用户数、节省工时与人工修订率。",
+ 10.5,
+ "red",
+ True,
+ PP_ALIGN.CENTER,
+ )
+ footer(slide, "A4", "历史内网记录 ≠ 当前版本部内复测 ≠ GIS Data Agent 生产部署")
+ add_notes(
+ slide,
+ (
+ "已证明的是问题真实、受众明确、规模可量化、底层旧版本有历史内网工程记录，"
+ "以及当前版本能在本机完成真实 MPC 闭环。没有证明的是当前版本已部内复测或"
+ "生产验收，也没有公开用户数、节省工时和人工修订率，所以这些指标不会编造。"
+ ),
+ )
+ return slide
+
+
+def appendix_quality(prs: Presentation, evidence: dict[str, Any], assets: dict[str, Path]):
+ del evidence, assets
+ slide = prs.slides.add_slide(prs.slide_layouts[6])
+ appendix_header(
+ slide,
+ "如何落地、扩展，并保证工程质量？",
+ "直接结论：主链本地可运行、接口按区域解耦、质量检查和演示降级均已文档化。",
+ "A5",
+ )
+ gates = [
+ ("236", "镜像运行时测试", "green"),
+ ("6", "宿主交付契约", "blue"),
+ ("52", "接口兼容测试", "yellow"),
+ ("3/3", "核心容器 healthy", "red"),
+ ]
+ for index, (value, label, accent) in enumerate(gates):
+ metric_block(
+ slide,
+ Inches(0.7 + index * 3.13),
+ Inches(1.92),
+ Inches(2.72),
+ value,
+ label,
+ "2026-07-30 决赛基线",
+ accent,
+ )
+
+ columns = [
+ (
+ "本地落地",
+ ["Gemma 4 + ADK 主链", "PostGIS + 优化引擎离线", "地图与 5 页图文 PDF"],
+ "blue",
+ ),
+ (
+ "按区域扩展",
+ [
+ "数据契约 / 坐标 / 编码检查",
+ "区域适配器与工具接口解耦",
+ "新区域独立硬约束校验",
+ ],
+ "green",
+ ),
+ (
+ "质量与演示",
+ ["Ruff / 编译 / 构建 / CI", "版本不兼容与缺产物安全停止", "实时 → 视频 → 封存产物"],
+ "red",
+ ),
+ ]
+ for index, (title, lines, accent) in enumerate(columns):
+ x = Inches(0.8 + index * 4.13)
+ rect(slide, x, Inches(3.42), Inches(3.5), Inches(2.45), "white", "line", radius=True)
+ add_text(
+ slide,
+ x + Inches(0.25),
+ Inches(3.7),
+ Inches(2.98),
+ Inches(0.26),
+ title,
+ 14,
+ accent,
+ True,
+ )
+ add_text(
+ slide,
+ x + Inches(0.25),
+ Inches(4.28),
+ Inches(2.98),
+ Inches(1.0),
+ "\n".join(f"• {line}" for line in lines),
+ 11,
+ "ink",
+ )
+ add_text(
+ slide,
+ Inches(1.05),
+ Inches(6.25),
+ Inches(11.0),
+ Inches(0.3),
+ "Google 栈口径：ADK 是已验证主运行时；AlphaEarth 仅 Tech Preview；OKF 仅知识交换 sidecar。",
+ 10,
+ "navy",
+ True,
+ PP_ALIGN.CENTER,
+ )
+ footer(slide, "A5", "Google 技术的深度、必要性和证据比 Logo 数量更重要")
+ add_notes(
+ slide,
+ (
+ "当前主链可本地运行，并按数据契约、区域适配器和工具接口解耦扩展。工程证据"
+ "分为 236 个镜像运行时测试、6 个宿主交付契约和 52 个接口兼容测试，不能直接"
+ "混成一个总数。ADK 已进入主链；AlphaEarth 和 OKF 只作为未来扩展，不冒充现有能力。"
+ ),
+ )
+ return slide
+
+
+def build_deck(template: Path | None, output: Path) -> None:
+ evidence = finals_evidence()
+ assets = ensure_assets(evidence)
+ if template and template.exists():
+ reference = Presentation(str(template))
+ slide_width = reference.slide_width
+ slide_height = reference.slide_height
+ else:
+ slide_width = WIDE
+ slide_height = HIGH
+ # Reusing a template after deleting every slide can create duplicate part
+ # names in python-pptx. Start clean and inherit only its 16:9 canvas.
+ prs = Presentation()
+ prs.slide_width = slide_width
+ prs.slide_height = slide_height
+
+ builders = [
+ slide_cover,
+ slide_problem_solution,
+ slide_nl2sql,
+ slide_farmland_planning,
+ slide_evidence,
+ slide_close,
+ appendix_architecture,
+ appendix_reliability,
+ appendix_claims,
+ appendix_google,
+ appendix_quality,
+ ]
+ for builder in builders:
+ builder(prs, evidence, assets)
+
+ output.parent.mkdir(parents=True, exist_ok=True)
+ prs.save(output)
+ print(json.dumps({"output": str(output), "slides": len(prs.slides)}, ensure_ascii=False))
+
+
+def parse_args() -> argparse.Namespace:
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--output", type=Path, default=DEFAULT_OUTPUT)
+ parser.add_argument("--template", type=Path, default=DEFAULT_TEMPLATE)
+ parser.add_argument("--no-template", action="store_true")
+ return parser.parse_args()
+
+
+def main() -> None:
+ args = parse_args()
+ build_deck(None if args.no_template else args.template, args.output)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/scripts/check_gemma4_finals_preflight.py b/scripts/check_gemma4_finals_preflight.py
new file mode 100755
index 00000000..8fd2dc52
--- /dev/null
+++ b/scripts/check_gemma4_finals_preflight.py
@@ -0,0 +1,88 @@
+#!/usr/bin/env python3
+"""Run read-only host checks required by the Gemma 4 finals demo."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import sys
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[1]
+if str(ROOT) not in sys.path:
+ sys.path.insert(0, str(ROOT))
+
+from data_agent.finals_preflight import ( # noqa: E402
+ fetch_ollama_tags,
+ inspect_finals_host,
+)
+
+DEFAULT_REPO = Path("/app/paper9-demo")
+DEFAULT_BISHAN_RUNS = Path("/app/bishan-runs")
+
+
+def _default_repo() -> Path:
+ return Path(
+ os.environ.get("PAPER9_HOST_REPO")
+ or os.environ.get("PAPER9_FARMLAND_MPC_REPO")
+ or DEFAULT_REPO
+ )
+
+
+def _default_bishan_runs() -> Path:
+ host_runs = os.environ.get("PAPER9_BISHAN_RUNS_HOST")
+ if host_runs:
+ return Path(host_runs)
+ prepared_dir = os.environ.get("PAPER9_FARMLAND_MPC_DEFAULT_PREPARED_DIR")
+ if prepared_dir:
+ prepared = Path(prepared_dir)
+ return prepared.parent if prepared.name == "prepared" else prepared
+ return DEFAULT_BISHAN_RUNS
+
+
+def main() -> int:
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--paper9-repo",
+ type=Path,
+ default=_default_repo(),
+ )
+ parser.add_argument(
+ "--bishan-runs",
+ type=Path,
+ default=_default_bishan_runs(),
+ )
+ parser.add_argument(
+ "--ollama-api-base",
+ default=os.environ.get("OLLAMA_API_BASE", "http://localhost:11434"),
+ )
+ parser.add_argument("--model-tag", default="Gemma4:26b")
+ parser.add_argument("--output", type=Path)
+ args = parser.parse_args()
+
+ try:
+ tags = fetch_ollama_tags(args.ollama_api_base)
+ ollama_error = None
+ except Exception as exc:
+ tags = []
+ ollama_error = f"{type(exc).__name__}: {exc}"
+
+ report = inspect_finals_host(
+ paper9_repo=args.paper9_repo,
+ bishan_runs=args.bishan_runs,
+ model_tag=args.model_tag,
+ ollama_tags=tags,
+ )
+ if ollama_error:
+ report["ollama_error"] = ollama_error
+ rendered = json.dumps(report, ensure_ascii=False, indent=2, sort_keys=True) + "\n"
+ if args.output:
+ args.output.parent.mkdir(parents=True, exist_ok=True)
+ args.output.write_text(rendered, encoding="utf-8")
+ print(rendered, end="")
+ return 0 if report["ready"] else 1
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/scripts/format_finals_technical_handbook.py b/scripts/format_finals_technical_handbook.py
new file mode 100644
index 00000000..3404cfc4
--- /dev/null
+++ b/scripts/format_finals_technical_handbook.py
@@ -0,0 +1,276 @@
+"""Apply the final A4 layout to the Gemma 4 technical handbook DOCX."""
+
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+
+from docx import Document
+from docx.enum.style import WD_STYLE_TYPE
+from docx.enum.text import WD_ALIGN_PARAGRAPH
+from docx.oxml import OxmlElement
+from docx.oxml.ns import qn
+from docx.shared import Cm, Pt, RGBColor
+
+
+def _set_east_asia_font(style, name: str) -> None:
+ style.font.name = name
+ r_pr = style.element.get_or_add_rPr()
+ r_fonts = r_pr.get_or_add_rFonts()
+ r_fonts.set(qn("w:ascii"), name)
+ r_fonts.set(qn("w:hAnsi"), name)
+ r_fonts.set(qn("w:eastAsia"), name)
+
+
+def _clear_paragraph(paragraph) -> None:
+ for child in list(paragraph._p):
+ paragraph._p.remove(child)
+
+
+def _add_page_number(paragraph) -> None:
+ run = paragraph.add_run()
+ begin = OxmlElement("w:fldChar")
+ begin.set(qn("w:fldCharType"), "begin")
+ instruction = OxmlElement("w:instrText")
+ instruction.set(qn("xml:space"), "preserve")
+ instruction.text = " PAGE "
+ separate = OxmlElement("w:fldChar")
+ separate.set(qn("w:fldCharType"), "separate")
+ end = OxmlElement("w:fldChar")
+ end.set(qn("w:fldCharType"), "end")
+ run._r.extend([begin, instruction, separate, end])
+
+
+def _heading_level(paragraph) -> int | None:
+ style_name = paragraph.style.name
+ if style_name == "Heading 1":
+ return 1
+ if style_name == "Heading 2":
+ return 2
+ return None
+
+
+def _heading_bookmark(paragraph) -> str | None:
+ for bookmark in paragraph._p.iter(qn("w:bookmarkStart")):
+ name = bookmark.get(qn("w:name"))
+ if name and not name.startswith("_"):
+ return name
+
+ # Pandoc emits heading bookmarks as body-level siblings immediately before
+ # the paragraph, while Word may move the same bookmark inside it.
+ sibling = paragraph._p.getprevious()
+ while sibling is not None and sibling.tag == qn("w:bookmarkStart"):
+ name = sibling.get(qn("w:name"))
+ if name and not name.startswith("_"):
+ return name
+ sibling = sibling.getprevious()
+ return None
+
+
+def _ensure_toc_style(doc, name: str, level: int) -> None:
+ if name in doc.styles:
+ style = doc.styles[name]
+ else:
+ style = doc.styles.add_style(name, WD_STYLE_TYPE.PARAGRAPH)
+ style.base_style = doc.styles["Normal"]
+
+ _set_east_asia_font(style, "PingFang SC")
+ style.font.size = Pt(9.5 if level == 1 else 8.75)
+ style.font.bold = level == 1
+ style.font.color.rgb = RGBColor(0x20, 0x2A, 0x35)
+ style.paragraph_format.left_indent = Cm(0 if level == 1 else 0.65)
+ style.paragraph_format.space_before = Pt(1.5 if level == 1 else 0)
+ style.paragraph_format.space_after = Pt(0)
+ style.paragraph_format.line_spacing = Pt(10.5)
+
+
+def _add_internal_hyperlink(paragraph, text: str, anchor: str) -> None:
+ hyperlink = OxmlElement("w:hyperlink")
+ hyperlink.set(qn("w:anchor"), anchor)
+ hyperlink.set(qn("w:history"), "1")
+
+ run = paragraph.add_run(text)
+ run.font.name = "PingFang SC"
+ run.font.color.rgb = RGBColor(0x1F, 0x4E, 0x79)
+ run.font.underline = False
+ run._element.get_or_add_rPr().get_or_add_rFonts().set(
+ qn("w:eastAsia"), "PingFang SC"
+ )
+ hyperlink.append(run._r)
+ paragraph._p.append(hyperlink)
+
+
+def _insert_clickable_toc(doc) -> None:
+ has_toc = any(
+ paragraph.text.strip() == "目录"
+ and paragraph.style.name == "Heading 1"
+ for paragraph in doc.paragraphs
+ )
+ if has_toc:
+ _ensure_toc_style(doc, "TOC 1", 1)
+ _ensure_toc_style(doc, "TOC 2", 2)
+ return
+
+ targets = []
+ for paragraph in doc.paragraphs:
+ level = _heading_level(paragraph)
+ if level is None:
+ continue
+ text = paragraph.text.strip()
+ bookmark = _heading_bookmark(paragraph)
+ if text and bookmark:
+ targets.append((paragraph, level, text, bookmark))
+
+ if not targets:
+ raise ValueError("No bookmarked Heading 1/2 paragraphs found for the TOC")
+
+ _ensure_toc_style(doc, "TOC 1", 1)
+ _ensure_toc_style(doc, "TOC 2", 2)
+
+ first_heading = targets[0][0]
+ toc_heading = first_heading.insert_paragraph_before("目录", style="Heading 1")
+ toc_heading.paragraph_format.page_break_before = True
+ toc_heading.paragraph_format.space_after = Pt(4)
+
+ note = first_heading.insert_paragraph_before()
+ note.alignment = WD_ALIGN_PARAGRAPH.LEFT
+ note.paragraph_format.space_after = Pt(8)
+ note_run = note.add_run(
+ "点击目录条目可跳转至正文；在 Word 中打开“导航窗格”可使用完整章节树。"
+ )
+ note_run.font.name = "PingFang SC"
+ note_run.font.size = Pt(9)
+ note_run.font.color.rgb = RGBColor(0x66, 0x66, 0x66)
+ note_run._element.get_or_add_rPr().get_or_add_rFonts().set(
+ qn("w:eastAsia"), "PingFang SC"
+ )
+
+ chapter_starts = [
+ index for index, (_, level, _, _) in enumerate(targets) if level == 1
+ ]
+ split_index = min(
+ (index for index in chapter_starts if index > 0),
+ key=lambda index: abs(index - len(targets) / 2),
+ )
+
+ for index, (_, level, text, bookmark) in enumerate(targets):
+ entry = first_heading.insert_paragraph_before(style=f"TOC {level}")
+ _add_internal_hyperlink(entry, text, bookmark)
+ entry.paragraph_format.page_break_before = index == split_index
+ next_level = targets[index + 1][1] if index + 1 < len(targets) else None
+ entry.paragraph_format.keep_with_next = level == 1 and next_level == 2
+
+ first_heading.paragraph_format.page_break_before = True
+
+
+def format_docx(path: Path) -> None:
+ doc = Document(path)
+
+ body = doc.styles["Normal"]
+ _set_east_asia_font(body, "PingFang SC")
+ body.font.size = Pt(10.5)
+ body.paragraph_format.space_after = Pt(4)
+ body.paragraph_format.line_spacing = 1.15
+
+ style_sizes = {
+ "Title": 24,
+ "Subtitle": 13,
+ "Author": 10.5,
+ "Date": 10.5,
+ "Heading 1": 18,
+ "Heading 2": 14,
+ "Heading 3": 12,
+ }
+ for style_name, size in style_sizes.items():
+ if style_name not in doc.styles:
+ continue
+ style = doc.styles[style_name]
+ _set_east_asia_font(style, "PingFang SC")
+ style.font.size = Pt(size)
+ if style_name.startswith("Heading"):
+ style.paragraph_format.keep_with_next = True
+
+ if "Image Caption" in doc.styles:
+ caption = doc.styles["Image Caption"]
+ _set_east_asia_font(caption, "PingFang SC")
+ caption.font.size = Pt(9)
+ caption.font.italic = False
+ caption.font.color.rgb = RGBColor(0x66, 0x66, 0x66)
+ caption.paragraph_format.alignment = WD_ALIGN_PARAGRAPH.CENTER
+ caption.paragraph_format.space_before = Pt(2)
+ caption.paragraph_format.space_after = Pt(6)
+
+ if "Captioned Figure" in doc.styles:
+ figure = doc.styles["Captioned Figure"]
+ figure.paragraph_format.alignment = WD_ALIGN_PARAGRAPH.CENTER
+ figure.paragraph_format.space_before = Pt(4)
+ figure.paragraph_format.space_after = Pt(0)
+
+ _insert_clickable_toc(doc)
+
+ for section in doc.sections:
+ section.page_width = Cm(21.0)
+ section.page_height = Cm(29.7)
+ section.top_margin = Cm(1.7)
+ section.bottom_margin = Cm(1.7)
+ section.left_margin = Cm(1.7)
+ section.right_margin = Cm(1.7)
+ section.header_distance = Cm(0.7)
+ section.footer_distance = Cm(0.7)
+
+ header = section.header.paragraphs[0]
+ _clear_paragraph(header)
+ header.alignment = WD_ALIGN_PARAGRAPH.RIGHT
+ header_run = header.add_run("GIS Data Agent | Gemma 4 决赛技术答辩手册")
+ header_run.font.name = "PingFang SC"
+ header_run.font.size = Pt(8)
+ header_run.font.color.rgb = RGBColor(0x66, 0x66, 0x66)
+
+ footer = section.footer.paragraphs[0]
+ _clear_paragraph(footer)
+ footer.alignment = WD_ALIGN_PARAGRAPH.CENTER
+ footer_run = footer.add_run("第 ")
+ footer_run.font.name = "PingFang SC"
+ footer_run.font.size = Pt(8)
+ _add_page_number(footer)
+ tail = footer.add_run(" 页")
+ tail.font.name = "PingFang SC"
+ tail.font.size = Pt(8)
+
+ for table in doc.tables:
+ table.autofit = True
+ header_text = table.cell(0, 0).text.strip() if table.rows else ""
+ compact_scoring_table = header_text == "评审维度"
+ for row_index, row in enumerate(table.rows):
+ tr_pr = row._tr.get_or_add_trPr()
+ if tr_pr.find(qn("w:cantSplit")) is None:
+ tr_pr.append(OxmlElement("w:cantSplit"))
+ for cell in row.cells:
+ for paragraph in cell.paragraphs:
+ paragraph.paragraph_format.space_after = Pt(
+ 0 if compact_scoring_table else 1
+ )
+ if compact_scoring_table:
+ paragraph.paragraph_format.line_spacing = 1.0
+ for run in paragraph.runs:
+ run.font.name = "PingFang SC"
+ run._element.get_or_add_rPr().get_or_add_rFonts().set(
+ qn("w:eastAsia"), "PingFang SC"
+ )
+ if compact_scoring_table:
+ run.font.size = Pt(7.5 if row_index else 8)
+ else:
+ run.font.size = Pt(8.5 if row_index else 9)
+
+ doc.save(path)
+
+
+def main() -> None:
+ parser = argparse.ArgumentParser()
+ parser.add_argument("docx", type=Path)
+ args = parser.parse_args()
+ format_docx(args.docx.resolve())
+
+
+if __name__ == "__main__":
+ main()
diff --git a/scripts/run_paper9_adk_reliability_eval.py b/scripts/run_paper9_adk_reliability_eval.py
new file mode 100755
index 00000000..37a1fb03
--- /dev/null
+++ b/scripts/run_paper9_adk_reliability_eval.py
@@ -0,0 +1,423 @@
+#!/usr/bin/env python3
+"""Measure Gemma 4 + ADK Paper9 orchestration with deterministic tool doubles."""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import hashlib
+import json
+import os
+import time
+import uuid
+from dataclasses import dataclass
+from datetime import UTC, datetime
+from pathlib import Path
+from typing import Any
+
+from google.adk.agents.llm_agent import LlmAgent
+from google.adk.runners import Runner
+from google.adk.sessions import InMemorySessionService
+from google.adk.tools import FunctionTool
+from google.genai import types
+
+from data_agent.model_gateway import create_model
+from data_agent.paper9_agent_evaluation import (
+ evaluate_paper9_tool_trajectory,
+ summarize_repeated_agent_runs,
+)
+from data_agent.paper9_agent_prompt import PAPER9_AGENT_INSTRUCTION
+
+ROOT = Path(__file__).resolve().parents[1]
+DEFAULT_OUTPUT = (
+ ROOT
+ / "data_agent"
+ / "demo_evidence"
+ / "paper9"
+ / "finals_20260730"
+ / "adk_reliability_report.json"
+)
+
+
+@dataclass(frozen=True)
+class Scenario:
+ name: str
+ prompt: str
+ version_compatible: bool
+ planning_ready: bool
+ audit_outcomes: tuple[bool, ...]
+ expected_terminal: str
+
+
+SCENARIOS = (
+ Scenario(
+ name="verified_first_pass",
+ prompt="使用 bishan 数据集运行一次快速县域 MPC 规划，并按约束审计后处理结果。",
+ version_compatible=True,
+ planning_ready=True,
+ audit_outcomes=(True,),
+ expected_terminal="verified_commit",
+ ),
+ Scenario(
+ name="version_mismatch_stops",
+ prompt="使用 bishan 数据集运行一次快速县域 MPC 规划。",
+ version_compatible=False,
+ planning_ready=False,
+ audit_outcomes=(),
+ expected_terminal="preflight_stop",
+ ),
+ Scenario(
+ name="single_replan_then_verified",
+ prompt="使用 bishan 数据集完成县域 MPC 规划；若硬约束失败，按允许范围恢复。",
+ version_compatible=True,
+ planning_ready=True,
+ audit_outcomes=(False, True),
+ expected_terminal="verified_commit",
+ ),
+)
+
+
+class ToolDoubleState:
+ def __init__(self, scenario: Scenario, run_id: str):
+ self.scenario = scenario
+ self.run_id = run_id
+ self.audit_calls = 0
+
+ def audit(self, attempt: int) -> dict[str, Any]:
+ index = min(self.audit_calls, max(0, len(self.scenario.audit_outcomes) - 1))
+ passed = bool(self.scenario.audit_outcomes[index])
+ self.audit_calls += 1
+ retryable = not passed and attempt == 0 and len(self.scenario.audit_outcomes) > 1
+ return {
+ "attempt": attempt,
+ "hard_constraint_passed": passed,
+ "retryable": retryable,
+ "next_action": (
+ "commit_verified_episode"
+ if passed
+ else "replan_once"
+ if retryable
+ else "stop_and_request_human_review"
+ ),
+ "out_dir": f"/evidence/{self.run_id}",
+ "all_expected_outputs_exist": True,
+ }
+
+
+def _named_tool(function, name: str, description: str) -> FunctionTool:
+ function.__name__ = name
+ function.__qualname__ = name
+ function.__doc__ = description
+ return FunctionTool(function)
+
+
+def build_tool_doubles(scenario: Scenario, run_id: str) -> list[FunctionTool]:
+ """Expose the production tool names while keeping algorithm output deterministic."""
+
+ state = ToolDoubleState(scenario, run_id)
+
+ def status() -> dict[str, Any]:
+ return {
+ "status": "ready" if scenario.version_compatible else "unavailable",
+ "version": "2.1.0",
+ "paper9": {
+ "package_version": "0.3.3" if scenario.version_compatible else "0.2.1",
+ "algorithm_version": "2.2.3" if scenario.version_compatible else "2.1.0",
+ },
+ "finals": {"version_compatible": scenario.version_compatible},
+ }
+
+ def inspect(
+ dataset: str = "bishan",
+ prepared_dir: str = "",
+ ensemble_dir: str = "",
+ ) -> dict[str, Any]:
+ return {
+ "dataset": dataset,
+ "planning_ready": scenario.planning_ready,
+ "reusable_stages": ["prepare", "sample", "train"]
+ if scenario.planning_ready
+ else [],
+ "required_stages": [] if scenario.planning_ready else ["repair_version"],
+ "prepared_dir": prepared_dir or "/app/bishan-runs/prepared",
+ "ensemble_dir": ensemble_dir or "/app/bishan-runs/prepared/ensemble_seed0",
+ }
+
+ def recall(dataset: str = "bishan", limit: int = 3) -> dict[str, Any]:
+ return {"status": "ok", "dataset": dataset, "limit": limit, "count": 0, "episodes": []}
+
+ def prepare(
+ dltb_path: str = "",
+ dem_path: str = "",
+ prepared_dir: str = "",
+ ) -> dict[str, Any]:
+ return {"status": "ok", "mode": "tool1_prepare", "prepared_dir": prepared_dir}
+
+ def sample(prepared_dir: str = "") -> dict[str, Any]:
+ return {"status": "ok", "mode": "tool2_sample", "prepared_dir": prepared_dir}
+
+ def train(prepared_dir: str = "") -> dict[str, Any]:
+ return {
+ "status": "ok",
+ "mode": "tool3_train",
+ "ensemble_dir": f"{prepared_dir}/ensemble_seed0",
+ }
+
+ def plan(
+ dataset: str = "bishan",
+ prepared_dir: str = "",
+ ensemble_dir: str = "",
+ horizon: int = 1,
+ top_k: int = 1,
+ n_episodes: int = 1,
+ cultivated_area_floor_delta_ha: float = 0.0,
+ ) -> dict[str, Any]:
+ return {
+ "status": "ok",
+ "mode": "tool4_plan",
+ "dataset": dataset,
+ "out_dir": f"/evidence/{run_id}",
+ "summary": {"horizon": horizon, "top_k": top_k, "n_episodes": n_episodes},
+ "cultivated_area_floor_delta_ha": cultivated_area_floor_delta_ha,
+ "prepared_dir": prepared_dir,
+ "ensemble_dir": ensemble_dir,
+ }
+
+ def pipeline(
+ dataset: str = "bishan",
+ prepared_dir: str = "",
+ ensemble_dir: str = "",
+ reuse_existing: bool = True,
+ horizon: int = 1,
+ top_k: int = 1,
+ n_episodes: int = 1,
+ ) -> dict[str, Any]:
+ return {
+ "status": "ok",
+ "mode": "pipeline_a_to_d",
+ "dataset": dataset,
+ "out_dir": f"/evidence/{run_id}",
+ "steps": [
+ {"step": "prepare", "status": "skipped_reused"},
+ {"step": "sample", "status": "skipped_reused"},
+ {"step": "train", "status": "skipped_reused"},
+ {"step": "plan", "status": "ok"},
+ ],
+ "reuse_existing": reuse_existing,
+ "summary": {"horizon": horizon, "top_k": top_k, "n_episodes": n_episodes},
+ "prepared_dir": prepared_dir,
+ "ensemble_dir": ensemble_dir,
+ }
+
+ def audit(
+ out_dir: str,
+ attempt: int = 0,
+ cultivated_area_floor_delta_ha: float = 0.0,
+ ) -> dict[str, Any]:
+ result = state.audit(int(attempt))
+ result["cultivated_area_floor_delta_ha"] = cultivated_area_floor_delta_ha
+ result["out_dir"] = out_dir
+ return result
+
+ def commit(
+ out_dir: str,
+ dataset: str = "bishan",
+ goal: str = "",
+ plan_args: str = "{}",
+ ) -> dict[str, Any]:
+ return {
+ "status": "committed",
+ "out_dir": out_dir,
+ "dataset": dataset,
+ "goal": goal,
+ "plan_args": plan_args,
+ "episode": {"episode_id": f"eval-{run_id}"},
+ }
+
+ return [
+ _named_tool(status, "world_model_v21_status", "Check Paper9 versions before any work."),
+ _named_tool(inspect, "paper9_inspect_resources", "Inspect reusable planning resources."),
+ _named_tool(recall, "paper9_recall_verified_episodes", "Recall verified prior episodes."),
+ _named_tool(prepare, "world_model_v21_prepare", "Run Paper9 Tool 1 preparation."),
+ _named_tool(sample, "world_model_v21_sample", "Run Paper9 Tool 2 sampling."),
+ _named_tool(train, "world_model_v21_train", "Run Paper9 Tool 3 training."),
+ _named_tool(plan, "world_model_v21_plan", "Run only Paper9 Tool 4 planning."),
+ _named_tool(pipeline, "world_model_v21_pipeline", "Run or reuse Paper9 A/B/C/D stages."),
+ _named_tool(audit, "paper9_audit_run", "Audit a planning output against hard gates."),
+ _named_tool(
+ commit,
+ "paper9_commit_verified_episode",
+ "Commit an audited successful run to verified episodic memory.",
+ ),
+ ]
+
+
+def _normalize_response(value: Any) -> dict[str, Any]:
+ if isinstance(value, dict):
+ if set(value) == {"result"} and isinstance(value["result"], dict):
+ return value["result"]
+ return value
+ if isinstance(value, str):
+ try:
+ parsed = json.loads(value)
+ except json.JSONDecodeError:
+ return {"result": value}
+ return parsed if isinstance(parsed, dict) else {"result": parsed}
+ return {"result": value}
+
+
+async def run_once(model: Any, scenario: Scenario, run_number: int, timeout: int) -> dict[str, Any]:
+ run_id = f"{scenario.name}-{run_number}-{uuid.uuid4().hex[:8]}"
+ agent = LlmAgent(
+ name="Paper9FinalsReliabilityAgent",
+ model=model,
+ instruction=PAPER9_AGENT_INSTRUCTION,
+ tools=build_tool_doubles(scenario, run_id),
+ )
+ session_service = InMemorySessionService()
+ runner = Runner(
+ agent=agent,
+ app_name="paper9_finals_reliability",
+ session_service=session_service,
+ auto_create_session=True,
+ )
+ message = types.Content(role="user", parts=[types.Part(text=scenario.prompt)])
+ events: list[dict[str, Any]] = []
+ final_text: list[str] = []
+ input_tokens = 0
+ output_tokens = 0
+ started = time.perf_counter()
+ error = None
+ try:
+ async with asyncio.timeout(timeout):
+ stream = runner.run_async(
+ user_id="finals-evaluator",
+ session_id=run_id,
+ new_message=message,
+ )
+ async for event in stream:
+ usage = getattr(event, "usage_metadata", None)
+ if usage:
+ input_tokens += getattr(usage, "prompt_token_count", 0) or 0
+ output_tokens += getattr(usage, "candidates_token_count", 0) or 0
+ content = getattr(event, "content", None)
+ for part in getattr(content, "parts", None) or []:
+ if part.function_response:
+ events.append(
+ {
+ "tool": part.function_response.name,
+ "response": _normalize_response(
+ part.function_response.response
+ ),
+ }
+ )
+ if part.text:
+ final_text.append(part.text)
+ except Exception as exc:
+ error = f"{type(exc).__name__}: {exc}"
+
+ latency_ms = (time.perf_counter() - started) * 1000
+ contract = evaluate_paper9_tool_trajectory(events)
+ passed = bool(
+ not error
+ and contract["passed"]
+ and contract["terminal"] == scenario.expected_terminal
+ )
+ return {
+ "scenario": scenario.name,
+ "run_number": run_number,
+ "run_id": run_id,
+ "passed": passed,
+ "expected_terminal": scenario.expected_terminal,
+ "terminal": contract["terminal"],
+ "tool_count": contract["tool_count"],
+ "tool_trace": contract["tool_trace"],
+ "violations": contract["violations"],
+ "latency_ms": latency_ms,
+ "input_tokens": input_tokens,
+ "output_tokens": output_tokens,
+ "error": error,
+ "final_response": "".join(final_text)[-2000:],
+ }
+
+
+async def run(args: argparse.Namespace) -> dict[str, Any]:
+ os.environ.setdefault("MODEL_CONFIG_FORCE_ENV", "true")
+ os.environ.setdefault("OLLAMA_API_BASE", args.ollama_api_base)
+ model = create_model(args.model)
+ rows = []
+ selected_scenarios = [
+ scenario
+ for scenario in SCENARIOS
+ if not args.scenario or scenario.name in args.scenario
+ ]
+ for scenario in selected_scenarios:
+ for run_number in range(1, args.runs_per_scenario + 1):
+ result = await run_once(model, scenario, run_number, args.timeout)
+ rows.append(result)
+ status = "PASS" if result["passed"] else "FAIL"
+ print(
+ f"{scenario.name} run={run_number} {status} "
+ f"terminal={result['terminal']} tools={result['tool_count']} "
+ f"latency_ms={result['latency_ms']:.0f}",
+ flush=True,
+ )
+
+ statistical = summarize_repeated_agent_runs(
+ rows, pass_rate_threshold=args.pass_rate_threshold
+ )
+ return {
+ "schema_version": "paper9.adk_reliability_report.v1",
+ "generated_at": datetime.now(UTC).isoformat(),
+ "scope": "live_gemma4_adk_orchestration_with_deterministic_tool_doubles",
+ "model": args.model,
+ "ollama_api_base": args.ollama_api_base,
+ "runs_per_scenario": args.runs_per_scenario,
+ "scenarios": [scenario.name for scenario in selected_scenarios],
+ "prompt_sha256": hashlib.sha256(
+ PAPER9_AGENT_INSTRUCTION.encode("utf-8")
+ ).hexdigest(),
+ "limitations": [
+ "This report measures live Gemma 4 and Google ADK tool orchestration.",
+ "Tool responses are deterministic doubles; this report does not rerun "
+ "Paper9 computation.",
+ "Real Paper9 algorithm and artifact evidence is maintained as a separate "
+ "evidence class.",
+ ],
+ "statistics": statistical,
+ "runs": rows,
+ }
+
+
+def main() -> int:
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--model", default="gemma4-26b-ollama")
+ parser.add_argument("--ollama-api-base", default="http://localhost:11434")
+ parser.add_argument("--runs-per-scenario", type=int, default=10)
+ parser.add_argument("--timeout", type=int, default=300)
+ parser.add_argument("--pass-rate-threshold", type=float, default=0.8)
+ parser.add_argument(
+ "--scenario",
+ action="append",
+ choices=[scenario.name for scenario in SCENARIOS],
+ help="Run only the named scenario; repeat the flag to select more than one.",
+ )
+ parser.add_argument("--output", type=Path, default=DEFAULT_OUTPUT)
+ args = parser.parse_args()
+ if args.runs_per_scenario < 1:
+ parser.error("--runs-per-scenario must be at least 1")
+ if not 0 <= args.pass_rate_threshold <= 1:
+ parser.error("--pass-rate-threshold must be between 0 and 1")
+
+ report = asyncio.run(run(args))
+ args.output.parent.mkdir(parents=True, exist_ok=True)
+ args.output.write_text(
+ json.dumps(report, ensure_ascii=False, indent=2, sort_keys=True) + "\n",
+ encoding="utf-8",
+ )
+ print(json.dumps(report["statistics"], ensure_ascii=False, indent=2))
+ return 0 if report["statistics"]["release_gate"]["passed"] else 1
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/scripts/run_paper9_finals_contract_eval.py b/scripts/run_paper9_finals_contract_eval.py
new file mode 100755
index 00000000..8fd26cb7
--- /dev/null
+++ b/scripts/run_paper9_finals_contract_eval.py
@@ -0,0 +1,72 @@
+#!/usr/bin/env python3
+"""Run deterministic Paper9 behavior contracts and write a JSON report."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from datetime import UTC, datetime
+from pathlib import Path
+
+from data_agent.paper9_agent_evaluation import evaluate_paper9_tool_trajectory
+
+ROOT = Path(__file__).resolve().parents[1]
+DEFAULT_CASES = ROOT / "data_agent" / "evals" / "paper9_finals" / "behavior_contract_cases.json"
+DEFAULT_OUTPUT = (
+ ROOT
+ / "data_agent"
+ / "demo_evidence"
+ / "paper9"
+ / "finals_20260730"
+ / "behavior_contract_report.json"
+)
+
+
+def main() -> int:
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--cases", type=Path, default=DEFAULT_CASES)
+ parser.add_argument("--output", type=Path, default=DEFAULT_OUTPUT)
+ args = parser.parse_args()
+
+ payload = json.loads(args.cases.read_text(encoding="utf-8"))
+ results = []
+ for case in payload.get("cases", []):
+ actual = evaluate_paper9_tool_trajectory(case.get("events", []))
+ matched = actual["passed"] is bool(case.get("expected_pass"))
+ results.append(
+ {
+ "id": case.get("id"),
+ "expected_pass": bool(case.get("expected_pass")),
+ "matched_expectation": matched,
+ "evaluation": actual,
+ }
+ )
+
+ passed = sum(1 for result in results if result["matched_expectation"])
+ report = {
+ "schema_version": "paper9.behavior_contract_report.v1",
+ "generated_at": datetime.now(UTC).isoformat(),
+ "scope": "deterministic_offline_behavior_contracts",
+ "limitations": [
+ "This report validates control-flow invariants, not stochastic Gemma 4 "
+ "tool-selection reliability.",
+ "Live model reliability must be measured separately with repeated ADK runs.",
+ ],
+ "summary": {
+ "total": len(results),
+ "matched": passed,
+ "pass_rate": passed / len(results) if results else 0.0,
+ },
+ "results": results,
+ }
+ args.output.parent.mkdir(parents=True, exist_ok=True)
+ args.output.write_text(
+ json.dumps(report, ensure_ascii=False, indent=2, sort_keys=True) + "\n",
+ encoding="utf-8",
+ )
+ print(json.dumps(report["summary"], ensure_ascii=False))
+ return 0 if passed == len(results) else 1
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/scripts/verify_gemma4_finals_demo.py b/scripts/verify_gemma4_finals_demo.py
new file mode 100755
index 00000000..0815dc7e
--- /dev/null
+++ b/scripts/verify_gemma4_finals_demo.py
@@ -0,0 +1,317 @@
+#!/usr/bin/env python3
+"""Verify the exact Gemma 4 finals demo prompts against the running system."""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import json
+import sys
+import time
+import uuid
+from datetime import UTC, datetime
+from pathlib import Path
+from typing import Any
+
+ROOT = Path(__file__).resolve().parents[1]
+if str(ROOT) not in sys.path:
+ sys.path.insert(0, str(ROOT))
+
+from google.adk.agents.run_config import RunConfig # noqa: E402
+from google.adk.runners import Runner # noqa: E402
+from google.adk.sessions import InMemorySessionService # noqa: E402
+from google.genai import types # noqa: E402
+
+from data_agent.agent import _make_agent_by_name # noqa: E402
+from data_agent.nl2sql_executor import run_nl2semantic2sql # noqa: E402
+from data_agent.toolsets.nl2sql_tools import execute_safe_sql # noqa: E402
+from data_agent.toolsets.world_model_v21_tools import ( # noqa: E402
+ paper9_inspect_resources,
+ world_model_v21_status,
+)
+
+NL2SQL_UI_PROMPT = "@NL2SQL 统计距离道路网络中最长桥梁100米范围内的高德POI数量。"
+NL2SQL_QUESTION = NL2SQL_UI_PROMPT.split(" ", 1)[1]
+PAPER9_UI_PROMPT = (
+ "@WorldModelV21 请使用 bishan 数据集运行一次快速县域 MPC 规划，"
+ "完成硬约束审计，并仅在通过后保存已验证经验。"
+)
+PAPER9_QUESTION = PAPER9_UI_PROMPT.split(" ", 1)[1]
+
+REFERENCE_SQL = """
+WITH longest_bridge AS (
+ SELECT geometry
+ FROM cq_osm_roads_2021
+ WHERE bridge = 'T'
+ ORDER BY ST_Length(geometry::geography) DESC
+ LIMIT 1
+)
+SELECT COUNT(DISTINCT p."ID") AS poi_count
+FROM cq_amap_poi_2024 AS p
+CROSS JOIN longest_bridge AS lb
+WHERE ST_DWithin(p.geometry::geography, lb.geometry::geography, 100)
+""".strip()
+
+EXPECTED_PAPER9_TRACE = [
+ "world_model_v21_status",
+ "paper9_inspect_resources",
+ "paper9_recall_verified_episodes",
+ "world_model_v21_pipeline",
+ "paper9_audit_run",
+ "paper9_commit_verified_episode",
+]
+
+
+def _decode(value: Any) -> Any:
+ current = value
+ for _ in range(3):
+ if isinstance(current, str):
+ try:
+ current = json.loads(current)
+ except json.JSONDecodeError:
+ return current
+ continue
+ if isinstance(current, dict) and set(current) == {"result"}:
+ current = current["result"]
+ continue
+ break
+ return current
+
+
+def _single_value(execution: dict[str, Any]) -> Any:
+ data = execution.get("data") or []
+ if len(data) != 1 or not isinstance(data[0], dict) or len(data[0]) != 1:
+ return None
+ return next(iter(data[0].values()))
+
+
+def _validate_nl2sql_payload(payload: dict[str, Any]) -> dict[str, Any]:
+ execution = payload.get("execution") or {}
+ sql = str(payload.get("sql") or "")
+ value = _single_value(execution)
+ checks = {
+ "status_ok": payload.get("status") == "ok",
+ "single_result_is_35": value == 35,
+ "uses_st_dwithin": "ST_DWITHIN" in sql.upper(),
+ "uses_geography": "GEOGRAPHY" in sql.upper(),
+ "uses_100_meters": "100" in sql,
+ "uses_expected_tables": all(
+ table in sql for table in ("cq_osm_roads_2021", "cq_amap_poi_2024")
+ ),
+ }
+ return {
+ "passed": all(checks.values()),
+ "checks": checks,
+ "status": payload.get("status"),
+ "error": payload.get("error"),
+ "value": value,
+ "sql": sql,
+ "corrections": payload.get("corrections") or [],
+ }
+
+
+def run_nl2sql_checks(repeats: int) -> dict[str, Any]:
+ reference_payload = _decode(execute_safe_sql(REFERENCE_SQL))
+ reference_value = _single_value(reference_payload)
+ runs: list[dict[str, Any]] = []
+ for run_number in range(1, repeats + 1):
+ started = time.perf_counter()
+ payload = _decode(run_nl2semantic2sql(NL2SQL_QUESTION))
+ result = _validate_nl2sql_payload(payload if isinstance(payload, dict) else {})
+ result["run"] = run_number
+ result["latency_s"] = round(time.perf_counter() - started, 3)
+ runs.append(result)
+ return {
+ "ui_prompt": NL2SQL_UI_PROMPT,
+ "reference_sql": REFERENCE_SQL,
+ "reference_value": reference_value,
+ "reference_passed": reference_value == 35,
+ "repeats": repeats,
+ "runs": runs,
+ "passed": reference_value == 35 and all(run["passed"] for run in runs),
+ }
+
+
+def _paper9_data_checks(
+ pipeline: dict[str, Any],
+ audit: dict[str, Any],
+ commit: dict[str, Any],
+) -> tuple[dict[str, bool], dict[str, Any]]:
+ plan = pipeline.get("plan_result") or pipeline
+ committed_episode = commit.get("episode") or {}
+ final_out_dir = (
+ committed_episode.get("out_dir")
+ or audit.get("out_dir")
+ or plan.get("out_dir")
+ )
+ summary: dict[str, Any] = {}
+ summary_error = None
+ if final_out_dir:
+ summary_path = Path(str(final_out_dir)) / "mpc_summary.json"
+ try:
+ loaded = json.loads(summary_path.read_text(encoding="utf-8"))
+ if isinstance(loaded, dict):
+ summary = loaded
+ else:
+ summary_error = "mpc_summary.json is not a JSON object"
+ except (OSError, json.JSONDecodeError) as exc:
+ summary_error = f"{type(exc).__name__}: {exc}"
+ result_rows = summary.get("results") or []
+ result = result_rows[0] if result_rows else {}
+ shape = summary.get("shapefile_output") or {}
+ audit_artifacts = audit.get("artifacts") or {}
+ optimized_artifact = audit_artifacts.get("optimized_spatial_result") or {}
+ checks = {
+ "pipeline_ok": pipeline.get("status") == "ok",
+ "tool4_plan_ok": plan.get("status") == "ok",
+ "final_summary_loaded": summary_error is None and bool(result_rows),
+ "hard_gate_passed": audit.get("hard_constraint_passed") is True,
+ "all_outputs_exist": audit.get("all_expected_outputs_exist") is True,
+ "verified_memory_committed": commit.get("status") == "committed",
+ "cultivated_area_not_reduced": float(
+ result.get("cultivated_area_change_ha", -1)
+ )
+ >= 0,
+ "slope_reduced": float(result.get("slope_change_pct", 1)) < 0,
+ "contiguity_improved": float(result.get("cont_change", 0)) > 0,
+ "optimized_spatial_artifact": optimized_artifact.get("exists") is True,
+ }
+ metrics = {
+ "out_dir": final_out_dir,
+ "summary_error": summary_error,
+ "steps_run": result.get("steps_run"),
+ "swaps_completed": result.get("swaps_completed"),
+ "cultivated_area_change_ha": result.get("cultivated_area_change_ha"),
+ "slope_change_pct": result.get("slope_change_pct"),
+ "cont_change": result.get("cont_change"),
+ "baimu_area_change_ha": result.get("baimu_area_change_ha"),
+ "n_input": shape.get("n_input"),
+ "n_in_env": shape.get("n_in_env"),
+ "n_farm_to_forest": shape.get("n_farm_to_forest"),
+ "n_forest_to_farm": shape.get("n_forest_to_farm"),
+ "land_use_code_scheme": shape.get("land_use_code_scheme"),
+ "episode_id": (commit.get("episode") or {}).get("episode_id"),
+ }
+ return checks, metrics
+
+
+async def run_live_paper9(timeout_s: int) -> dict[str, Any]:
+ agent = _make_agent_by_name("WorldModelV21")
+ if agent is None:
+ return {"passed": False, "error": "WorldModelV21 agent is not registered"}
+ session_id = f"finals-demo-{uuid.uuid4().hex[:10]}"
+ runner = Runner(
+ agent=agent,
+ app_name="gemma4_finals_demo_verification",
+ session_service=InMemorySessionService(),
+ auto_create_session=True,
+ )
+ message = types.Content(role="user", parts=[types.Part(text=PAPER9_QUESTION)])
+ trace: list[str] = []
+ responses: dict[str, dict[str, Any]] = {}
+ final_text: list[str] = []
+ started = time.perf_counter()
+ error = None
+ try:
+ async with asyncio.timeout(timeout_s):
+ events = runner.run_async(
+ user_id="finals-demo-verifier",
+ session_id=session_id,
+ new_message=message,
+ run_config=RunConfig(max_llm_calls=12),
+ )
+ async for event in events:
+ content = getattr(event, "content", None)
+ for part in getattr(content, "parts", None) or []:
+ if part.function_response:
+ name = part.function_response.name
+ trace.append(name)
+ decoded = _decode(part.function_response.response)
+ responses[name] = decoded if isinstance(decoded, dict) else {
+ "result": decoded
+ }
+ if part.text:
+ final_text.append(part.text)
+ except Exception as exc:
+ error = f"{type(exc).__name__}: {exc}"
+
+ pipeline = responses.get("world_model_v21_pipeline") or {}
+ audit = responses.get("paper9_audit_run") or {}
+ commit = responses.get("paper9_commit_verified_episode") or {}
+ checks, metrics = _paper9_data_checks(pipeline, audit, commit)
+ checks["exact_six_tool_trace"] = trace == EXPECTED_PAPER9_TRACE
+ return {
+ "ui_prompt": PAPER9_UI_PROMPT,
+ "session_id": session_id,
+ "latency_s": round(time.perf_counter() - started, 3),
+ "trace": trace,
+ "expected_trace": EXPECTED_PAPER9_TRACE,
+ "checks": checks,
+ "metrics": metrics,
+ "error": error,
+ "final_text": "".join(final_text)[-4000:],
+ "passed": error is None and all(checks.values()),
+ }
+
+
+def run_paper9_preflight() -> dict[str, Any]:
+ status = _decode(world_model_v21_status())
+ inspect = _decode(paper9_inspect_resources(dataset="bishan"))
+ checks = {
+ "status_ready": status.get("status") == "ready",
+ "package_0_3_3": (status.get("paper9") or {}).get("package_version") == "0.3.3",
+ "algorithm_2_2_3": (status.get("paper9") or {}).get("algorithm_version") == "2.2.3",
+ "version_compatible": (status.get("finals") or {}).get("version_compatible") is True,
+ "planning_ready": inspect.get("planning_ready") is True,
+ "three_onnx_members": (
+ ((inspect.get("stages") or {}).get("train") or {}).get("onnx_member_count")
+ == 3
+ ),
+ }
+ return {
+ "checks": checks,
+ "status": status,
+ "inspect": inspect,
+ "passed": all(checks.values()),
+ }
+
+
+def main() -> int:
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--nl2sql-repeats", type=int, default=3)
+ parser.add_argument("--skip-nl2sql", action="store_true")
+ parser.add_argument("--skip-paper9-live", action="store_true")
+ parser.add_argument("--paper9-timeout", type=int, default=240)
+ parser.add_argument("--output", type=Path)
+ args = parser.parse_args()
+ if args.nl2sql_repeats < 1:
+ parser.error("--nl2sql-repeats must be at least 1")
+
+ preflight = run_paper9_preflight()
+ nl2sql = None if args.skip_nl2sql else run_nl2sql_checks(args.nl2sql_repeats)
+ paper9 = (
+ None
+ if args.skip_paper9_live
+ else asyncio.run(run_live_paper9(args.paper9_timeout))
+ )
+ sections = [preflight, nl2sql, paper9]
+ passed = all(section is None or section.get("passed") for section in sections)
+ report = {
+ "schema_version": "gemma4.finals_demo_verification.v1",
+ "generated_at": datetime.now(UTC).isoformat(),
+ "scope": "exact_production_prompts_against_running_data_and_tools",
+ "passed": passed,
+ "paper9_preflight": preflight,
+ "nl2sql": nl2sql,
+ "paper9_live_agent": paper9,
+ }
+ rendered = json.dumps(report, ensure_ascii=False, indent=2, sort_keys=True) + "\n"
+ if args.output:
+ args.output.parent.mkdir(parents=True, exist_ok=True)
+ args.output.write_text(rendered, encoding="utf-8")
+ print(rendered, end="")
+ return 0 if passed else 1
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/tests/e2e/nl2sql_longest_bridge_map_real_e2e.py b/tests/e2e/nl2sql_longest_bridge_map_real_e2e.py
new file mode 100644
index 00000000..6d65e0ce
--- /dev/null
+++ b/tests/e2e/nl2sql_longest_bridge_map_real_e2e.py
@@ -0,0 +1,120 @@
+"""Real browser E2E for the longest-bridge 100m AMap POI map handoff."""
+
+import json
+import os
+from pathlib import Path
+
+from playwright.sync_api import sync_playwright
+
+BASE_URL = os.environ.get("GIS_AGENT_E2E_URL", "http://127.0.0.1:8000")
+USERNAME = os.environ.get("GIS_AGENT_E2E_USER", "admin")
+PASSWORD = os.environ.get("GIS_AGENT_E2E_PASSWORD", "")
+PROMPT = "@NL2SQL 统计距离道路网络中最长桥梁100米范围内的高德POI数量。"
+ARTIFACT_DIR = Path("tests/e2e/artifacts/nl2sql_longest_bridge_map_2026_07_30")
+
+
+def main() -> None:
+ if not PASSWORD:
+ raise RuntimeError("GIS_AGENT_E2E_PASSWORD is required for real browser E2E")
+ ARTIFACT_DIR.mkdir(parents=True, exist_ok=True)
+ with sync_playwright() as playwright:
+ browser = playwright.chromium.launch(headless=True)
+ page = browser.new_page(viewport={"width": 1920, "height": 1080})
+ page.goto(BASE_URL, wait_until="domcontentloaded")
+ page.locator("#username").fill(USERNAME)
+ page.locator("#password").fill(PASSWORD)
+ page.get_by_role("button", name="登录").click()
+ page.locator(".app-header").wait_for(state="visible", timeout=30_000)
+ page.wait_for_timeout(12_000)
+
+ box = page.get_by_placeholder("输入消息... (Enter 发送)")
+ box.fill(PROMPT)
+ box.press("Enter")
+
+ response = page.locator(
+ ".chat-message.assistant",
+ has_text="POI数量：35",
+ ).last
+ response.wait_for(timeout=120_000)
+ response_text = response.inner_text()
+ assert "已默认加载地图：高德 POI 35 个" in response_text
+ assert "同一 PostGIS 查询快照" in response_text
+
+ map_update = page.wait_for_function(
+ """() => {
+ const update = window.__lastMapUpdate;
+ return update?.summary?.query_type === 'longest_bridge_poi_100m'
+ ? update
+ : null;
+ }""",
+ timeout=30_000,
+ ).json_value()
+ assert map_update["summary"]["scalar_poi_count"] == 35
+ assert map_update["summary"]["poi_feature_count"] == 35
+ assert map_update["summary"]["bridge_feature_count"] == 1
+ assert map_update["summary"]["buffer_feature_count"] == 1
+ assert map_update["summary"]["geometry_snapshot"] == "single_postgis_statement"
+ assert [layer["type"] for layer in map_update["layers"]] == [
+ "polygon",
+ "line",
+ "point",
+ ]
+
+ feature_counts = page.evaluate(
+ """async (update) => {
+ const counts = {};
+ for (const layer of update.layers) {
+ const response = await fetch(`/api/user/files/${layer.geojson}`, {
+ credentials: 'include',
+ });
+ if (!response.ok) throw new Error(`${layer.name}: ${response.status}`);
+ const geojson = await response.json();
+ counts[layer.type] = geojson.features.length;
+ }
+ return counts;
+ }""",
+ map_update,
+ )
+ assert feature_counts == {"polygon": 1, "line": 1, "point": 35}
+
+ page.wait_for_function(
+ """() => Array.from(
+ document.querySelectorAll('.leaflet-overlay-pane path.leaflet-interactive')
+ ).filter(path => path.getAttribute('fill') === '#2563eb').length === 35""",
+ timeout=30_000,
+ )
+ page.locator(".layer-control-toggle").click()
+ for layer in map_update["layers"]:
+ page.get_by_text(layer["name"], exact=True).wait_for(timeout=10_000)
+
+ (ARTIFACT_DIR / "map_update.json").write_text(
+ json.dumps(map_update, ensure_ascii=False, indent=2),
+ encoding="utf-8",
+ )
+ (ARTIFACT_DIR / "verification.json").write_text(
+ json.dumps(
+ {
+ "prompt": PROMPT,
+ "response_contains_count_35": True,
+ "feature_counts": feature_counts,
+ "rendered_poi_paths": 35,
+ },
+ ensure_ascii=False,
+ indent=2,
+ ),
+ encoding="utf-8",
+ )
+ page.screenshot(path=str(ARTIFACT_DIR / "result.png"), full_page=True)
+ browser.close()
+
+ print(json.dumps({
+ "status": "passed",
+ "prompt": PROMPT,
+ "feature_counts": feature_counts,
+ "bridge_osm_id": map_update["summary"]["bridge_osm_id"],
+ "artifacts": str(ARTIFACT_DIR),
+ }, ensure_ascii=False))
+
+
+if __name__ == "__main__":
+ main()
diff --git a/tests/e2e/world_model_v21_finals_real_e2e.py b/tests/e2e/world_model_v21_finals_real_e2e.py
new file mode 100644
index 00000000..b2255cc4
--- /dev/null
+++ b/tests/e2e/world_model_v21_finals_real_e2e.py
@@ -0,0 +1,268 @@
+"""Real browser E2E for the Gemma 4 finals county planning prompt."""
+
+from __future__ import annotations
+
+import json
+import os
+import re
+import time
+from pathlib import Path
+from urllib.parse import quote
+
+from playwright.sync_api import sync_playwright
+
+BASE_URL = os.environ.get("GIS_AGENT_E2E_URL", "http://127.0.0.1:8000")
+USERNAME = os.environ.get("GIS_AGENT_E2E_USER", "admin")
+PASSWORD = os.environ.get("GIS_AGENT_E2E_PASSWORD", "")
+PROMPT = (
+ "@WorldModelV21 请使用 bishan 数据集运行一次快速县域 MPC 规划，"
+ "完成硬约束审计，并仅在通过后保存已验证经验。"
+)
+ARTIFACT_DIR = Path("tests/e2e/artifacts/world_model_v21_finals_2026_07_30")
+
+EXPECTED_TOOLS = [
+ "world_model_v21_status",
+ "paper9_inspect_resources",
+ "paper9_recall_verified_episodes",
+ "world_model_v21_pipeline",
+ "paper9_audit_run",
+ "paper9_commit_verified_episode",
+]
+
+REPORT_FILENAME = "County_Farmland_Planning_Report.pdf"
+
+
+def _browse_user_files(page) -> list[dict]:
+ payload = page.evaluate(
+ """async () => {
+ const response = await fetch('/api/user/files/browse?path=', {
+ credentials: 'include',
+ });
+ if (!response.ok) {
+ throw new Error(`file browse failed: HTTP ${response.status}`);
+ }
+ return response.json();
+ }"""
+ )
+ return payload.get("entries", [])
+
+
+def _wait_for_new_report(page, previous_modified: float) -> dict:
+ handle = page.wait_for_function(
+ """async ({filename, previousModified}) => {
+ const response = await fetch('/api/user/files/browse?path=', {
+ credentials: 'include',
+ });
+ if (!response.ok) return false;
+ const payload = await response.json();
+ const entry = (payload.entries || []).find(
+ item => item.name === filename && item.type === 'pdf'
+ );
+ return entry && entry.size > 0 && entry.modified > previousModified
+ ? entry
+ : false;
+ }""",
+ arg={
+ "filename": REPORT_FILENAME,
+ "previousModified": previous_modified,
+ },
+ polling=500,
+ timeout=180_000,
+ )
+ return handle.json_value()
+
+
+def _duration_after_tool(response_text: str, tool_name: str) -> float:
+ offset = response_text.index(tool_name)
+ match = re.search(r"(\d+(?:\.\d+)?)\s*秒", response_text[offset : offset + 120])
+ assert match, f"missing duration after {tool_name}"
+ return float(match.group(1))
+
+
+def _summary_duration(response_text: str, label: str) -> float:
+ match = re.search(rf"{re.escape(label)}\s*(\d+(?:\.\d+)?)\s*秒", response_text)
+ assert match, f"missing summary duration: {label}"
+ return float(match.group(1))
+
+
+def main() -> None:
+ if not PASSWORD:
+ raise RuntimeError("GIS_AGENT_E2E_PASSWORD is required for real browser E2E")
+ ARTIFACT_DIR.mkdir(parents=True, exist_ok=True)
+ with sync_playwright() as playwright:
+ browser = playwright.chromium.launch(headless=True)
+ page = browser.new_page(viewport={"width": 1920, "height": 1080})
+ page.goto(BASE_URL, wait_until="domcontentloaded")
+ page.locator("#username").fill(USERNAME)
+ page.locator("#password").fill(PASSWORD)
+ page.get_by_role("button", name="登录").click()
+ page.locator(".app-header").wait_for(state="visible", timeout=30_000)
+ page.wait_for_timeout(5_000)
+
+ box = page.get_by_placeholder("输入消息... (Enter 发送)")
+ box.fill(PROMPT)
+ box.press("Enter")
+
+ response = page.locator(
+ ".chat-message.assistant",
+ has_text="县域耕地受控规划完成",
+ ).last
+ response.wait_for(timeout=240_000)
+ response_text = response.inner_text()
+
+ required_text = [
+ "Gemma 4 + Google ADK",
+ "6 次原生函数调用",
+ "受控自主决策轨迹",
+ "县域耕地空间优化引擎",
+ "学习型状态转移模型集成 + MPC",
+ "A / 数据准备：已复用（未重复执行）",
+ "B / 样本生成：已复用（未重复执行）",
+ "C / 状态转移模型训练：已复用（未重复执行）",
+ "D / MPC 规划执行：完成",
+ "完成双向置换：406 对",
+ "耕地面积变化：1.9359 ha",
+ "平均坡度变化：-0.6078%",
+ "连片度变化：0.0202",
+ "百亩方面积变化：35.13 ha",
+ "硬约束校验：通过",
+ "已验证经验库：已写入",
+ ]
+ for text in required_text:
+ assert text in response_text, f"missing UI evidence: {text}"
+
+ forbidden_text = [
+ "Status:",
+ "Adapter Version:",
+ "Paper9 Package:",
+ "Paper9 Algorithm:",
+ "Tool 4",
+ "Hard Gate",
+ "Verified Memory",
+ "Prepared Dir:",
+ "Ensemble Dir:",
+ "/app/bishan-runs",
+ "/app/data_agent/uploads",
+ ]
+ for text in forbidden_text:
+ assert text not in response_text, f"internal presentation leaked: {text}"
+
+ positions = [response_text.index(tool) for tool in EXPECTED_TOOLS]
+ assert positions == sorted(positions)
+ tool_durations = {
+ tool: _duration_after_tool(response_text, tool) for tool in EXPECTED_TOOLS
+ }
+ total_duration = _summary_duration(response_text, "总用时")
+ planning_duration = _summary_duration(response_text, "MPC 规划")
+ governance_duration = _summary_duration(response_text, "审计与经验提交")
+ assert total_duration >= sum(tool_durations.values()) - 0.7
+ assert abs(planning_duration - tool_durations["world_model_v21_pipeline"]) <= 0.2
+ assert (
+ abs(
+ governance_duration
+ - tool_durations["paper9_audit_run"]
+ - tool_durations["paper9_commit_verified_episode"]
+ )
+ <= 0.3
+ )
+ (ARTIFACT_DIR / "response.txt").write_text(
+ response_text + "\n",
+ encoding="utf-8",
+ )
+
+ map_update = page.wait_for_function(
+ """() => {
+ const update = window.__lastMapUpdate;
+ return update?.layers?.some(layer => layer.type === 'fgb')
+ ? update
+ : null;
+ }""",
+ timeout=60_000,
+ ).json_value()
+ assert len(map_update["layers"]) == 1
+ layer = map_update["layers"][0]
+ assert layer["type"] == "fgb"
+ assert layer["category_column"] == "CHG_FLAG"
+ assert set(layer["category_labels"].values()) == {
+ "保持不变",
+ "耕地 -> 林地",
+ "林地 -> 耕地",
+ }
+
+ map_panel = page.locator(".map-3d-container")
+ map_panel.wait_for(state="visible", timeout=60_000)
+ page.wait_for_timeout(18_000)
+ canvases = map_panel.locator("canvas")
+ assert canvases.count() >= 1
+
+ existing_report = next(
+ (
+ entry
+ for entry in _browse_user_files(page)
+ if entry.get("name") == REPORT_FILENAME
+ ),
+ None,
+ )
+ previous_modified = float((existing_report or {}).get("modified") or 0.0)
+ report_started = time.monotonic()
+ page.get_by_role("button", name="导出 PDF 报告").last.click()
+ report_entry = _wait_for_new_report(page, previous_modified)
+ report_duration = time.monotonic() - report_started
+ assert page.get_by_text("生成失败:").count() == 0
+
+ report_response = page.context.request.get(
+ f"{BASE_URL}/api/user/files/{quote(REPORT_FILENAME)}"
+ )
+ assert report_response.ok, f"report download failed: {report_response.status}"
+ report_bytes = report_response.body()
+ assert report_bytes.startswith(b"%PDF-"), "downloaded report is not a PDF"
+ assert len(report_bytes) == report_entry["size"]
+ (ARTIFACT_DIR / REPORT_FILENAME).write_bytes(report_bytes)
+
+ verification = {
+ "status": "passed",
+ "prompt": PROMPT,
+ "public_agent_name": "县域耕地规划 Agent",
+ "public_engine_name": "县域耕地空间优化引擎",
+ "tool_trace": EXPECTED_TOOLS,
+ "tool_durations_s": tool_durations,
+ "total_duration_s": total_duration,
+ "planning_duration_s": planning_duration,
+ "governance_duration_s": governance_duration,
+ "map_layer": {
+ "name": layer["name"],
+ "type": layer["type"],
+ "category_column": layer["category_column"],
+ "category_labels": layer["category_labels"],
+ "canvas_count": canvases.count(),
+ },
+ "report": {
+ "file_name": REPORT_FILENAME,
+ "generation_duration_s": round(report_duration, 3),
+ "size_bytes": report_entry["size"],
+ "modified": report_entry["modified"],
+ "download_verified": True,
+ },
+ }
+ (ARTIFACT_DIR / "map_update.json").write_text(
+ json.dumps(map_update, ensure_ascii=False, indent=2),
+ encoding="utf-8",
+ )
+ (ARTIFACT_DIR / "verification.json").write_text(
+ json.dumps(verification, ensure_ascii=False, indent=2),
+ encoding="utf-8",
+ )
+ chat = page.locator(".chat-messages")
+ chat.evaluate("element => { element.scrollTop = 0; }")
+ page.wait_for_timeout(500)
+ page.screenshot(
+ path=str(ARTIFACT_DIR / "response_top.png"),
+ timeout=120_000,
+ )
+ browser.close()
+
+ print(json.dumps(verification, ensure_ascii=False))
+
+
+if __name__ == "__main__":
+ main()