Recursive Experiential–Working Memory Evolution for Long-Horizon Agent Harnesses

National University of Singapore  ·  Stanford University  ·  University of Oxford  ·  Princeton University

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💡 Introduction

Recuris is a recursive self-improvement framework that improves a long-horizon agent by evolving its memory instead of its weights or its prompt. A frozen agent is paired with a Skill Memory M = (E, W, ρ, C). A meta-agent reads structured execution traces, locates each failure in one component of that memory, and patches only that component. A deterministic validation gate then decides, on paired held-out evidence, whether the patch survives. Recuris has the following key features:

• State-grounded memory use. Working memory drives skill invocation, so retrieval is conditioned on verified task state rather than on a chat history that grows until the state is buried.
• Targeted memory evolution. Structured trajectories (w_t, E_t, a_t, o_t) locate a failure in a specific component, instead of nudging a monolithic prompt from outcomes alone.
• Bounded by a validation gate. Candidates are admitted by paired held-out arithmetic and nothing else. No model votes on its own patch.
• Training-free and model-agnostic. The downstream agent stays frozen, and a memory evolved on one model transfers to others unchanged.

Overall, Recuris achieves higher task success, larger gains on longer horizons, and substantially fewer long-horizon failures, on both frontier and open-source agents.

🔔 News

• [2026-08] 🎉 Initial release: evaluation and evolution code, the evolved Skill Memory packages, and the frozen evaluation splits.

📊 Results

Task success (avg@4, %), each model run with the benchmark's own reference agent alone and with that same agent plus Recuris. Bold marks the better of each pair, the subscript is Δ, † marks a paired task-clustered bootstrap 95% CI excluding zero, and n/a means the model was not run on that benchmark.

Model	Cross-task evolution			Within-task adaptation
	τ²-Retail	τ²-Airline	SkillFlow	Terminal-Bench 2.1
Open-source models
Granite-4.1-3B	9.7	34.3	0.3	0.6
+ Recuris	23.0 +13.4†	39.8 +5.5	0.0 −0.3	3.1 +2.5
Qwen3.5-4B	68.0	75.3	6.0	10.1
+ Recuris	68.3 +0.3	79.0 +3.8	7.1 +1.1	13.0 +2.9
Qwen3.5-9B	77.6	75.5	15.1	17.4
+ Recuris	79.6 +2.0	78.4 +2.9	18.4 +3.4	20.5 +3.1
GPT-OSS-20B	50.6	54.8	7.8	3.9
+ Recuris	60.8 +10.2†	59.3 +4.5†	10.4 +2.6†	6.7 +2.8
Qwen3.6-27B	62.8	79.0	42.2	38.8
+ Recuris	71.2 +8.3†	80.0 +1.0	58.7 +16.6†	42.1 +3.3
Qwen3.6-35B	78.2	80.3	35.3	33.1
+ Recuris	78.5 +0.3	81.5 +1.3	48.8 +13.5†	36.4 +3.3
Frontier models
Gemini 3.7 Flash	73.5	86.5	n/a	79.8
+ Recuris	78.3 +4.8	85.0 −1.5	n/a	82.4 +2.6
GPT-5.6 Sol	58.3	79.0	n/a	83.2
+ Recuris	76.1 +17.8†	86.0 +7.0†	n/a	86.4 +3.2
Claude Opus 5	72.4	89.5	n/a	84.6
+ Recuris	87.9 +15.6†	90.5 +1.0	n/a	88.4 +3.8
Doubao-2.0-Pro (deployment)	58.1	75.5	34.6	46.1
+ Recuris	81.4 +23.3†	80.5 +5.0	51.4 +16.8†	48.9 +2.9

Recuris improves task success in 35 of the 37 completed model and benchmark pairs, from a 3B open-source agent up to the strongest frontier models. The largest gains reach +23.3 on τ²-Retail and +16.8 on SkillFlow. Gains grow with the interaction horizon, reaching +32.2 on the longest tasks, and common long-horizon failure modes drop by up to 80%.

🛠️ Getting Started

This repository provides the code for running Recuris on τ²-Bench, SkillFlow and Terminal-Bench 2.1, the Skill Memory packages produced by the evolution loop, and the frozen evaluation splits.

📦 Install Packages

Python 3.12 and git. SkillFlow and Terminal-Bench 2.1 also need Docker with the Compose V2 plugin (docker compose version must work; harbor shells out to it for every task).

git clone https://github.com/Gen-Verse/Recuris.git recuris
cd recuris

uv sync --extra all          # or: pip install -e ".[all]"

⚙️ Setup Environment Variables

Put your endpoint in a .env file at the repository root, or export it:

OPENAI_API_KEY=...
OPENAI_BASE_URL=...

Any OpenAI-compatible endpoint works. This is needed even when the agent itself is an open-source model, because τ²-Bench scores every episode with an LLM user simulator and an LLM assertion judge, and both stay pinned to a reference model.

🚀 Quick Start

Each benchmark is run twice, once with a Skill Memory loaded and once without. The two runs differ only in the flags shown below. Both are needed, because the number that matters is the difference between them.

🔹 τ²-Bench (retail and airline)

Set up the benchmark:

bash third_party/tau2/setup.sh
uv pip install -e external/tau2-bench
recuris check-data --benchmark tau2

Install tau2 after uv sync, not before: uv sync resolves the environment to exactly what pyproject.toml declares, so running it again removes anything added with uv pip install.

Serve an open-source model locally. τ²-Bench drives the agent through tool calls, so the two tool-calling flags are required, not optional: without them vLLM rejects every request and every episode ends ungraded.

vllm serve <model-id> --port 8000 --served-model-name qwen3.6-27b \
    --enable-auto-tool-choice --tool-call-parser hermes

hermes is the parser for Qwen; other families need their own (see vLLM's tool-calling docs). A frontier model served by a provider needs none of this.

Now point the agent at it:

export TAU2_GATE_TERM=1 TAU2_GATE_TERM_WM=1 TAU2_STATUS_BOARD=1

# open-source example
export MODEL=openai/qwen3.6-27b
export ARGS='{"api_base":"http://127.0.0.1:8000/v1","api_key":"dummy","temperature":0.0,"timeout":360,"num_retries":2}'

# frontier example
# export MODEL=openai/<provider-model>
# export ARGS='{"api_base":"'"$OPENAI_BASE_URL"'","api_key":"'"$OPENAI_API_KEY"'","temperature":0.0,"timeout":360,"num_retries":2,"reasoning_effort":"high","allowed_openai_params":["reasoning_effort"]}'

Run both configurations and compare them:

# with Skill Memory
recuris tau2 --domain retail --agent recuris_agent --skill-memory tau2_retail \
    --open-downstream --agent-llm "$MODEL" --agent-llm-args "$ARGS" \
    --num-trials 4 --max-concurrency 4 --save-to retail_skill

# without
recuris tau2 --domain retail --agent llm_agent \
    --open-downstream --agent-llm "$MODEL" --agent-llm-args "$ARGS" \
    --num-trials 4 --max-concurrency 4 --save-to retail_bare

recuris compare --a retail_skill --b retail_bare

Notes:

• --domain is retail or airline. For airline, use --skill-memory tau2_airline.
• --agent-llm-args must be identical in both runs. It is validated rather than merged, so an unknown key raises an error instead of being dropped silently.
• Switching models means changing $MODEL and $ARGS. Nothing else changes. Some servers need extras, for example "extra_body":{"chat_template_kwargs":{"enable_thinking":false}} for Qwen.

🔹 SkillFlow

Set up the benchmark and build the task images once:

pip install huggingface_hub
bash third_party/skillflow/setup.sh

./external/SkillFlow/docker/harbor-cli-base/build.sh
python external/SkillFlow/utils/prebuild_task_images.py \
    --tasks-root external/SkillFlow/test_tasks

Generate the configs for both runs, then execute them:

export MODEL=openai/qwen3.6-27b
export BASE=http://127.0.0.1:8000/v1

recuris skillflow render-configs --arm bare \
    --model "$MODEL" --base-url "$BASE" --out configs/skillflow/generated
recuris skillflow render-configs --arm skill --routing default \
    --model "$MODEL" --base-url "$BASE" \
    --skill-memory skillflow --out configs/skillflow/generated

for cfg in configs/skillflow/generated/bare_*.yaml;  do harbor run -c "$cfg" --yes; done
for cfg in configs/skillflow/generated/skill_*.yaml; do harbor run -c "$cfg" --yes; done

recuris skillflow score --bare jobs/bare --skill jobs/skill

Notes:

• Run the jobs one at a time. Concurrent harbor jobs exhaust the Docker IPv4 address pool, and the resulting failure looks like something else entirely.
• Configs are generated rather than committed, so the two runs cannot drift apart and no credential is ever written to disk.
• --routing default is model-agnostic and is the right choice for new work. --routing frozen_insample reproduces our reported numbers and applies six per-family overrides that were chosen in-sample.

🔹 Terminal-Bench 2.1 (test-time adaptation)

bash third_party/tb21/setup.sh
recuris check-data --benchmark tb21

On this benchmark a task may be attempted several times in a row, and it stops as soon as one attempt succeeds. --rounds sets how many attempts each task gets. There are three configurations:

Configuration	What the agent carries	After a failed attempt
bare	nothing, the stock agent	nothing changes, the next attempt starts over
m0	a fixed Skill Memory, the seed package	nothing changes, the next attempt gets the same package
tta	the same package, as a per-task copy	the meta-agent reads the failed trajectory and writes a new card into that copy, which the next attempt carries

# smoke test: one task, one attempt
recuris tta run --taskset splits/tb21/tta_taskset_v3.json \
    --run-id smoke --arm m0 --limit 1 --rounds 1

# all three configurations, four attempts each
for cfg in bare m0 tta; do
    recuris tta run --taskset splits/tb21/tta_taskset_v3.json \
        --run-id demo --arm "$cfg" --rounds 4 --concurrency 3
done

Notes:

• Give all three configurations the same --rounds. Comparing tta at four attempts against bare at one mostly measures the extra attempts rather than adaptation.
• m0 against bare isolates the value of having a Skill Memory at all. tta against m0 isolates the value of updating it between attempts, since both carry a package and both get the same number of attempts.
• At four attempts, that second comparison is worth +2.3 points, 60.9% against 58.6%, which is not significant at this sample size. We report it that way rather than as a headline number.

🔹 Evolving a Skill Memory

This is the recursive loop. A meta-agent, the upstream model, reads failed trajectories from the agent being improved, the downstream model. It patches one component of the memory, and a gate admits the patch only on paired held-out evidence.

npm install -g @anthropic-ai/claude-code

RECURIS_META_MODEL=...        # the upstream meta-agent's model
RECURIS_META_BASE_URL=...
RECURIS_META_API_KEY=...

# one scoped session, zero simulations: checks the plumbing first
recuris metaagent qualify --run-id qsmoke --proxy-port 4047

recuris metaagent run --domain retail --run-id retail_v1 \
    --splits splits/tau2/retail_from0_v1_k4.json \
    --rounds 4 --k 4 --arm autonomous --base neutral \
    --round-gate progressive --power-gate warn --reg-cap 1 \
    --meta-workflow hierarchical --diagnosis-workers 3 \
    --max-concurrency 6 --max-sims 1400 --proxy-port 4047

To evolve a memory for an open-source downstream model, unfreeze the worker only. The user simulator stays pinned, so rounds remain comparable:

recuris metaagent run --domain retail --run-id retail_gptoss_v1 \
    --splits splits/tau2/retail_from0_v1_k4.json \
    --rounds 4 --k 4 --arm autonomous --base neutral \
    --open-worker --worker-model openai/gpt-oss-20b \
    --worker-llm-args '{"api_base":"http://127.0.0.1:8000/v1","api_key":"dummy","temperature":0.0,"timeout":360,"num_retries":2,"stop_token_ids":[200002,200012]}' \
    --round-gate progressive --power-gate warn --reg-cap 1 \
    --meta-workflow hierarchical --max-concurrency 6 --proxy-port 4047

Notes:

• --meta-model is the upstream meta-agent and --worker-model is the downstream agent being improved. Both default to Doubao.
• --base neutral starts from a deterministic seed package, so no hand-written domain profile enters the loop.
• Start with qualify and then a single round. Each round writes a full record: the evidence the session was given, the plan it produced, the gate arithmetic, and the ledger entry. A round that admits nothing is a valid outcome.
• Evolving a package for a specific model beats reusing one evolved elsewhere. On GPT-OSS-20B a rebuilt package gained +10.2, while the general-purpose package transferred negatively.

📖 Citation

@article{yu2026recuris,
  title   = {Recursive Experiential--Working Memory Evolution for Long-Horizon Agent Harnesses},
  author  = {Yu, Zhaochen and Wu, Yingcheng and Yin, Zhenfei and Chen, Kaiyuan and Zhao, Zhe and Wang, Mengdi and Yan, Shuicheng and Yang, Ling},
  journal = {arXiv preprint arXiv:2608.24876},
  year    = {2026}
}

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