Add one-step distilled specialist reference

README.md

@@ -52,6 +52,13 @@ conventional neural-weight training.
 > unattended model execution, protected final custody, formal
 > MNCS/MNCDS conformance, or automatic RAVEL promotion.
 
+The first one-step distillation reference is also implemented for the
+`forge.evidence-relevance` role. It records bounded recurrent teacher observations,
+independently checked fixture targets, a tiny affine/tanh student, a no-distillation
+control, explicit calibration/OOD abstention, and lineage-bound teacher fallback. The
+reference is experimental and diagnostic-only; its measurements do not show that the
+student is faster end-to-end or ready to replace the teacher.
+
 The repository also contains a dependency-aware MNCS-family integration reference:
 `mnel family-integration-reference` exposes a Forge Provider Protocol 0.1 adapter, validates
 pinned Forge/Fabric/Commons/RAVEL/Language compatibility shapes, executes a bounded local
@@ -84,6 +91,17 @@ mnel recurrent-specialist-reference --workspace examples/recurrent-specialists
 These artifacts are diagnostic-only and do not issue verifier results, permissions,
 trust, or promotion decisions.
 
+Run the one-step distilled specialist reference with:
+
+```bash
+mnel one-step-specialist-reference --workspace examples/one-step-specialists
+```
+
+The checked-in example contains the recurrent teacher, distilled student and control
+artifacts, retained distillation records, and measured study report. A successful
+student call records one learned forward pass; preprocessing, calibration, OOD checks,
+and recurrent fallback are measured outside that boundary.
+
 ## Core rule
 
 **Investigators and learned providers may propose knowledge. They may not declare it true.**
@@ -158,6 +176,10 @@ copy their authority or silently create substitute implementations.
 - `mnel distill-reference`, a no-network held-out study that writes an evidence ledger
   and exercises controls, hidden-transfer access, strategy transfer, and a tiny
   reloadable transition-frequency learned provider. Its observations remain diagnostic.
+- `mnel one-step-specialist-reference`, a no-network study that distills the bounded
+  recurrent Forge relevance teacher into a one-pass affine/tanh student, compares a
+  classical nearest-centroid baseline and a same-architecture no-distillation control,
+  and records explicit abstention/fallback measurements.
 - `mnel provider-study-reference`, a no-network heterogeneous portfolio study that trains
   a transition-frequency provider and a structurally different tabular nearest-centroid
   provider, calibrates and reloads both, compares seeded-random and heuristic controls,

docs/LEARNED_MICRO_PROVIDERS.md

@@ -53,6 +53,31 @@ A learned result is a diagnostic observation. Agreement among several models doe
 become a verifier verdict. Disagreement is preserved because the pattern of disagreement
 may be more useful than a vote.
 
+## One-step distilled specialist reference
+
+MNEL now contains an experimental one-step reference for `forge.evidence-relevance`.
+The bounded recurrent specialist is the teacher: its provider/model/generation,
+operating-envelope, context-state, query, decision, confidence, iterations, operations,
+and decision identity are retained in `mnel-distillation-record/0.1` records. A fixture
+label is accepted independently only for the checked-in deterministic reference cases;
+teacher rejections, unknown targets, and teacher abstentions remain retained but are not
+converted into positive training rows.
+
+The student is a dependency-free affine/tanh/softmax model. After bounded feature
+preparation it performs one learned forward evaluation and emits a structured diagnostic
+proposal with calibrated confidence. Low confidence, OOD distance, and incompatible
+context cause `ABSTAIN`; the runtime records that abstention before explicitly invoking
+the lineage-compatible recurrent teacher as fallback. The student cannot emit verifier,
+evaluator, conformance, permission, trust, promotion, or verdict authority.
+
+`mnel one-step-specialist-reference` compares the iterative teacher, the one-step
+student, a deterministic nearest-centroid baseline, and a same-architecture control
+trained only from direct fixture labels. It reports correctness against independent
+fixture targets, false accepts, abstentions, OOD behavior, disagreements, model/artifact
+size, operations, one-pass counts, cold/warm latency, preprocessing cost, and escalated
+end-to-end latency. The current synthetic study deliberately does not claim a speed or
+accuracy win.
+
 ## Registry contract
 
 Every declaration identifies:

docs/MNCS_NATIVE_TRAINING_PIPELINE.md

@@ -287,6 +287,12 @@ MNEL
 Some components may move downward over time. For example, tensor operations may prove
 sufficiently general to belong in the standard library rather than MNEL.
 
+The first one-step distilled specialist implementation intentionally remains in MNEL's
+Python/reference-runtime layer. It uses a dependency-free bounded numerical trainer and
+identity-bound JSON artifacts while MNCS-native training representation and semantic
+closure remain future work. No new MNCS syntax, compiler behavior, backend lowering, or
+standard-library primitive is required by this reference slice.
+
 ## Backend boundary
 
 MNCS-native training does **not** require MNCS to own every low-level implementation on

docs/ROADMAP.md

@@ -74,6 +74,11 @@
   measurements, and diagnostic-only evidence ledgers;
 - **Implemented:** provider candidate/admission, transfer-pending, quarantine, retirement,
   and rollback records with explicit evidence checklists;
+- **Implemented:** first executable one-step distilled specialist reference for the
+  Forge evidence-relevance role, including retained recurrent teacher observations,
+  independently checked target status, a tiny reloadable affine/tanh student,
+  no-distillation control, calibrated/OOD abstention, explicit teacher fallback, and
+  measured reference comparisons. This remains diagnostic-only and experimental.
 - **Started:** broader provider portfolios and native export of Python-trained artifacts;
 - **Implemented:** a bounded Rust parser/reference inference surface for the existing
   transition-frequency artifact, with checked-in Python/Rust identity and score-equivalence

examples/one-step-specialists/README.md

@@ -0,0 +1,32 @@
+# One-step distilled specialist reference
+
+This checked-in study is the first executable MNEL one-step distillation slice for
+`forge.evidence-relevance`.
+
+```text
+bounded recurrent teacher
+        -> retained teacher observations
+        -> independently checked fixture targets
+        -> tiny affine/tanh student
+        -> one learned forward pass or ABSTAIN
+        -> explicit recurrent teacher fallback
+```
+
+The files are generated with:
+
+```bash
+mnel one-step-specialist-reference --workspace examples/one-step-specialists
+```
+
+- `forge-teacher-recurrent-g0.json` is the existing bounded recurrent teacher artifact.
+- `forge-student-distilled-g0.json` is the one-step student trained from teacher-derived
+  decision/confidence targets.
+- `forge-student-no-distillation-control-g0.json` uses the same architecture and rows,
+  but trains only from direct fixture labels.
+- `distillation-records.json` retains every teacher observation, including rejected,
+  unknown, and abstaining cases, plus the selected training targets.
+- `reference-study.json` reports the iterative teacher, distilled student, deterministic
+  nearest-centroid baseline, no-distillation control, calibration, and fallback costs.
+
+The measurements are diagnostic evidence, not a correctness or promotion verdict. The
+synthetic fixture does not currently support a claim that distillation wins end-to-end.