Squash Odysseus development history

website/sft-expansion-launch-ledger-20260830.md

@@ -0,0 +1,220 @@
+# SFT Expansion Launch Ledger
+
+## Immutable Inputs
+
+- Approved seed manifest: `tmp/sft_expansion_20260830/frozen_v1/seed_manifest.json`
+- Approved seed turns: `tmp/sft_expansion_20260830/frozen_v1/approved_trace.jsonl`
+- Environment inventory: `tmp/sft_expansion_20260830/environment_inventories.json`
+- Seed baseline: 561 sessions, 1,372 approved turns
+- Expansion selection: `tmp/sft_expansion_20260830/selected_200_manifest.json`
+- Selection size: 50 owner-bound families, projected to 200 environment cases
+
+## Acceptance Policy
+
+1. Generate only from inventory facts and marker-scoped reversible mutations.
+2. Reject unsupported tools, temporal contradictions, vague calendar mutation times, and compound mutation-plus-verification prompts.
+3. Run each turn through the real 7011 agent surface with Kimi K3.
+4. Require the expected tool and manager action, successful tool output, a nonempty answer, and no internal narration or false-unavailability claim.
+5. Restore owner state after every case and delete mechanically failed sessions.
+6. Review every mechanically passing session with DeepSeek.
+7. Retain only `keep` verdicts scoring at least 80. Delete all other sessions and trace rows; repair by regenerating and replaying, never by inventing missing tool evidence.
+8. Preserve the source seed family's train/validation/test split.
+
+## Pilot Record
+
+- Harness regression fixed: contextual calendar entries no longer route to `manage_tasks`.
+- Harness regression fixed: contextual calendar reads require fresh `manage_calendar` evidence.
+- Runner regression fixed: a manager tool name alone no longer passes; create/list/update/delete actions are checked.
+- Generator regression fixed: calendar mutations require an exact time or `ask_user`.
+- Unsafe global skill-directory rollback replaced with marker-scoped cleanup.
+- Omar calendar lifecycle: deterministic pass, DeepSeek `keep`, 96/100, retained.
+- Ambiguous Omar predecessor: DeepSeek `repair`, 60/100, deleted from app and trace.
+- Maya ambiguous-date lifecycle: deterministic reject, deleted before review.
+
+## Current Corpus
+
+- Build: `tmp/sft_expansion_20260830/corpus_v2`
+- Train: 994 turns
+- Validation: 157 turns
+- Test: 135 turns
+- Sessions: 562
+- Retained expansion sessions: 1
+- Seed-family leakage: 0
+
+## Clean Source Baseline
+
+- Immutable source remains unchanged: `tmp/sft_expansion_20260830/frozen_v1/approved_trace.jsonl`
+- Deterministic hygiene input: 561 sessions, 1,372 turns
+- Deterministic hygiene result: 421 sessions, 859 turns retained; 140 sessions rejected
+- Full DeepSeek semantic review: 336 keep, 77 repair, 8 delete
+- Kimi repair result: 73 validated repaired sessions; 4 additional sessions excluded
+- Clean derivative: `tmp/sft_expansion_20260830/clean_v2/approved_trace.jsonl`
+- Clean derivative size: 409 sessions, 834 turns
+- Post-repair deterministic recheck: 409/409 sessions pass
+- Style contract: `docs/sft-style-contract.md`
+- Assembly report: `tmp/sft_expansion_20260830/clean_v2/assembly_report.json`
+- Semantic verdicts: `tmp/sft_expansion_20260830/clean_v1/deepseek_verdicts.jsonl`
+
+The clean source does not yet support the intended balanced expansion by itself.
+Notes has one clean source session, tasks has two, and session-management tools
+are absent. Add and review natural live workflows for those domains before the
+200-case cross-environment run.
+
+## Commands
+
+```bash
+.venv/bin/python scripts/generate_sft_environment_expansion.py \
+  --seed-manifest tmp/sft_expansion_20260830/selected_200_manifest.json \
+  --inventories tmp/sft_expansion_20260830/environment_inventories.json \
+  --out tmp/sft_expansion_20260830/generated_200_cases.json \
+  --workers 4 --timeout 180 --retries 1
+
+.venv/bin/python scripts/run_sft_environment_expansion.py \
+  --cases tmp/sft_expansion_20260830/generated_200_cases.json \
+  --out tmp/sft_expansion_20260830/generated_200_run.json
+
+.venv/bin/python scripts/review_sft_environment_expansion.py \
+  --run tmp/sft_expansion_20260830/generated_200_run.json \
+  --out tmp/sft_expansion_20260830/generated_200_review.json
+
+.venv/bin/python scripts/build_sft_expansion_splits.py \
+  --manifest tmp/sft_expansion_20260830/frozen_v1/seed_manifest.json \
+  --approved-trace tmp/sft_expansion_20260830/frozen_v1/approved_trace.jsonl \
+  --review tmp/sft_expansion_20260830/pilot_atomic_exact_v1.review.json \
+  --review tmp/sft_expansion_20260830/generated_200_review.json \
+  --out-dir tmp/sft_expansion_20260830/corpus_expanded
+```
+# All-Tools No-Thinking Validation Update
+
+## Matched-control finding
+
+The previously reported email LoRA speed advantage does not reproduce when the
+raw base model and LoRA are served in the same vLLM process with the same GPU,
+tool payload, prompt, cache settings, and `enable_thinking=false`.
+
+- Original 24-case email suite, 14 compact tools:
+  - raw Qwen3.5-9B base: 23/24, 0.914s average
+  - all-tools iteration-2 LoRA: 23/24, 0.874s average
+- Earlier base result of 9.655s was therefore confounded by serving conditions.
+- The validated latency win is **thinking off + compact schemas**. LoRA has not
+  yet shown a material independent speed gain.
+
+## All-tools iterations
+
+- Iteration 1: 365 train sessions, 40 validation sessions, 92 steps, LR 5e-7,
+  one epoch. Eval loss 0.8364.
+- Iteration 2: same corpus, 184 steps, LR 2e-6, two epochs. Eval loss 0.7433.
+- Matched 40-case held-out routing result for base, v67, and iteration 2 was
+  effectively identical: 85% exact tool selection, 85% required-field
+  presence, 70% constrained required-argument accuracy, and no reasoning
+  leakage.
+- Iteration 2 adapter weights differ from v67 (relative L2 delta 1.02%), so the
+  tie is not caused by an unchanged adapter file.
+
+## Decision
+
+Do not promote iteration 1 or 2 as a speed improvement. Before another train:
+
+1. Build a larger, balanced, genuinely unseen eval across sparse tool families.
+2. Expand sparse training families with multi-turn, context-dependent traces.
+3. Evaluate whether LoRA preserves accuracy under more aggressive schema
+   pruning than base; that is the plausible route to an indirect latency win.
+4. Keep no-thinking routing and compact schema selection as harness features,
+   independent of model promotion.
+
+## Critical Qwen3.5 LoRA Serving Failure
+
+### Symptom
+
+vLLM 0.22 accepted `--enable-lora`, listed each adapter under `/v1/models`, and
+logged `Loaded new LoRA adapter`, but Qwen3.5 text-tool adapters were not applied
+to inference. Base, v67, iteration 3, and iteration 4 produced byte-identical tool
+calls across 177 cases. A direct log-probability probe also returned numerically
+identical values for base and every dynamic adapter.
+
+Do **not** treat model registration, successful HTTP responses, different adapter
+files, or latency differences as evidence that a LoRA is active.
+
+### Minimal activation check
+
+Before any benchmark, send the same deterministic request to base and adapter
+models with `temperature=0`, `logprobs=true`, and thinking disabled. Compare the
+token log-probabilities, not only generated text. Identical probabilities across
+several prompts mean the adapter path is inactive.
+
+Observed inactive result:
+
+```text
+base:       cob -0.0484729, alt -0.0000684
+v67:        cob -0.0484729, alt -0.0000684
+iteration4: cob -0.0484729, alt -0.0000684
+```
+
+Disabling prefix caching and enabling eager execution did not fix this.
+
+### Merge trap
+
+The tool adapters were trained with `AutoModelForCausalLM`, which gives Qwen3.5
+text keys under:
+
+```text
+base_model.model.model.layers.*
+```
+
+The old merge script auto-selected `AutoModelForImageTextToText`, whose language
+tower expects:
+
+```text
+base_model.model.model.language_model.layers.*
+```
+
+PEFT warned about missing adapter keys, then still wrote a model. That output was
+effectively unmodified. A successful `save_pretrained` is therefore not proof of
+a successful merge. Treat any missing-adapter-key warning as a hard failure.
+
+Merging with the causal loader applied the adapter, but produced a
+`Qwen3_5TextConfig` model that vLLM's production Qwen3.5 multimodal loader refused.
+
+### Working merge path
+
+`scripts/merge_hf_lora.py` now supports the production-safe bridge:
+
+1. Load the full Qwen3.5 model with the image/multimodal loader.
+2. Remap causal adapter keys from `model.layers` to
+   `model.language_model.layers`.
+3. Exclude the visual tower from PEFT target matching.
+4. Merge and save the full model plus tokenizer and processor metadata.
+
+```bash
+python scripts/merge_hf_lora.py \
+  --model-loader image \
+  --remap-qwen35-causal-adapter \
+  --base /mnt/HADES/models/Qwen3.5-9B \
+  --adapter /path/to/final_adapter \
+  --output /path/to/merged-full-bf16
+```
+
+Verify there are no missing adapter keys, serve the merged model as a standalone
+model, and repeat the log-probability activation check.
+
+Observed active merged result:
+
+```text
+v67 merged:        cob -0.471937001
+iteration4 merged: cob -0.319356978
+```
+
+### Corrected valid benchmark
+
+The first valid merged-model all-tools benchmark showed:
+
+- v67 merged: 96.05% tool accuracy, 93.79% constrained argument accuracy,
+  2.669s average latency, zero reasoning rows.
+- iteration 4 merged: 96.05% tool accuracy, 93.79% constrained argument
+  accuracy, 2.390s average latency, zero reasoning rows.
+- Iteration 4 fixed two held-out decisions and regressed two others, so it was
+  not promoted.
+
+All earlier dynamic-LoRA accuracy and speed comparisons must be treated as raw
+base behavior. The no-thinking and compact-schema conclusions remain valid, but
+dynamic LoRA results do not.