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Author SHA1 Message Date
Shay
e985790a03 feat(evals+bench): isolation lanes, holdouts, planner-on bench sub-bench
Sharpens the measurement layer to match the runtime spine landed in
07fefb9 / 7af7892 / 4e3ddee.  Pure eval/benchmark/holdout work —
no runtime or planner code changed.

New isolation lanes
-------------------

* ``evals/compound_intent_decomposition/`` — single-purpose lane for
  the new ``classify_compound_intent`` decomposer.  Metrics:
  ``decomposition_accuracy``, ``atom_precision``, ``subject_accuracy``.
  Public: ``decomposition=1.0`` on 4e3ddee.
* ``evals/walkthrough_chain/`` — single-purpose lane for the new
  WALKTHROUGH sequential teaching-chain walk.  Metrics:
  ``path_exact_rate``, ``anchor_rate``, ``min_hop_rate``, ``bounded_rate``.
  Public: ``path_exact=1.0`` on 4e3ddee.

Without these, regressions in compound decomposition or the
walkthrough walk would show up as noise in ``multi_sentence_response``.
Each capability now has a single load-bearing metric on its own lane.

Cold-start lane sharpened
-------------------------

* ``evals/cold_start_grounding/public/v1/cases.jsonl`` extended with
  expository, compound, and walkthrough cases (48 total cases across
  19 categories including new ``expository_definition``,
  ``compound_definition_cause``, ``walkthrough_definition``).
* ``evals/cold_start_grounding/runner.py`` uses
  ``classify_compound_intent(...).primary`` for compound subject
  scoring — previously misattributed subjects on multi-part prompts.

Holdouts for the long-span lanes
--------------------------------

Until now only the cognition lane had a holdout split.  Adding
holdouts to the long-span lanes gives the planner work somewhere to
fail honestly when we widen:

* ``evals/cold_start_grounding/holdouts/v1/cases.jsonl`` (5 cases)
* ``evals/multi_sentence_response/holdouts/v1/cases.jsonl`` (5 cases)
* ``evals/conversational_thread_coherence/holdouts/v1/cases.jsonl`` (3 cases)
* ``evals/warmed_session_consistency/holdouts/v1/cases.jsonl`` (2 cases)

Discourse-planner-on bench sub-bench
------------------------------------

* ``benchmarks/articulation.py`` adds a planner-on sub-bench that
  reports ``articulate_sentence_rate`` alongside the existing
  throughput metrics.  Baselines articulation under load before any
  follow-up touches ``compute_trace_hash``.

Test coverage
-------------

* ``tests/test_compound_walkthrough_eval_lanes.py`` — new file pinning
  the two new lane runners.
* ``tests/test_articulation_bench.py``, ``tests/test_cold_start_grounding_lane.py``,
  ``tests/test_intent_explain_paragraph.py``,
  ``tests/test_response_mode_classifier.py`` — updated for new cases
  and assertions.

Validation
----------

* 152/152 active tests pass on the listed surfaces (2 skipped).
* smoke suite 67/67.
* cognition eval byte-identical: public 100/100/91.7/100.
* multi_sentence flag_on: articulate=1.0, disclosure=0.0, unarticulate=0.0
* compound_intent_decomp public: decomposition=1.0
* walkthrough_chain public: path_exact=1.0
* cold_start_grounding public (48 cases): intent=1.0, grounding=1.0, subject=1.0
2026-05-19 12:42:55 -07:00
Shay
e06fda5b8b feat(runtime+evals): warm-path pack grounding + three long-span lanes
Step 1 — warm_grounding_stability targeted patch
- chat/runtime.py:_maybe_pack_grounded_surface accepts allow_warm=True;
  warm path invokes it after articulation and overrides
  response_surface / articulation / grounding_source when pack-grounded
  or teaching-grounded.
- CAUSE / VERIFICATION without a teaching chain on warm path emits the
  unknown-domain disclosure (matches cold-path discovery-signal doctrine
  — no fabricated vault content).
- warmed_session_consistency public lane: warm_grounding_stability
  0.0 → 1.0, grounding_match_rate 1.0, telemetry_consistency 1.0.
- Cognition lane byte-identical (public 100/100/91.7/100, holdout
  100/100/83.3/100).  Full suite 2294 passed.

Step 2 — three new red eval lanes (measurement substrate)
- conversational_thread_coherence: 6 cases / 45 turns; per-turn
  no_placeholder / not_walk_fragment / length / is_grounded predicates
  + per-case topic_anchor and no_topic_drift.  Baseline: grounded
  0.93, topic_anchor 0.50, no_topic_drift 0.83.
- multi_sentence_response: 15 cases over Explain/Tell/Describe/Walk/
  Example/Essay shapes; predicates sentence_count >= 2, non-fragment,
  connective_present, subject_named.  Baseline: multi_sentence 0.53,
  connective 0.10 — biggest architectural gap.
- self_consistency_over_time: 7 cases; same probe at multiple turn
  indices with unrelated fillers interleaved.  Baseline: byte_identical
  0.86 (one CAUSE-no-chain disclosure drifts under accumulation).

All three lanes deterministic, lexical-predicate-only — no LLM judge,
no embedding similarity.  Red-on-creation by design.  See
notes/long_span_fluency_baseline_2026-05-19.md.
2026-05-19 08:26:38 -07:00