conductor(score_dynamics_giorgini): Phase 5 Verification - end-of-track report + state.toml completed

2026-06-21 22:21:05 -04:00
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+# Track Completion: video_analysis_score_dynamics_giorgini_20260621
+
+**Track:** `video_analysis_score_dynamics_giorgini_20260621`
+**Type:** Per-child research track (Pass 1 of 3) — child #4 of 12 in `video_analysis_campaign_20260621`
+**Status:** SHIPPED
+**Tier:** 2 Tech Lead (per-child dispatch)
+**Ship date:** 2026-06-21
+
+## Summary
+
+Fourth child of the video_analysis_campaign_20260621 umbrella shipped. All 5 phases executed successfully. Cluster A #3 (math foundations). Bridges A → E via shared DSM machinery.
+
+## Phase Results
+
+### Phase 1: Acquire
+
+- **Transcript:** yt-dlp VTT recovered 2998 raw segments. Rolling-caption dedup (longest-common-prefix algorithm) produced 1485 unique clean segments (46.5KB plain text).
+- **Video:** yt-dlp downloaded 178MB mp4 in Matroska/WebM container (format 400+251). Required direct `yt-dlp` call (the `download_video.py` script's `scripts.video_analysis.error_types` import fails when run as a top-level module; the umbrella phase1_acquire driver had the same issue — fell back to `uv run --with yt-dlp yt-dlp ...` directly).
+- **Cleanup:** improved `clean_transcript.py` driver added to `scripts/tier2/artifacts/video_analysis_campaign_20260621/` (rolling-caption dedup handles triplicate repeated text from yt-dlp auto-subs).
+
+### Phase 2: Keyframes
+
+ffmpeg scene detection at threshold 0.05 (low-motion math lecture). 91 raw frames extracted; imagehash phash dedup kept 31 unique frames. The lecture has minimal visual motion (mostly blackboard writing), so 31 frames is consistent with the entropy_epiplexity pattern (176 frames for a research talk with more slides).
+
+### Phase 3: OCR
+
+winsdk OCR processed 31 frames in 2.3 seconds (0.07s/frame — faster than entropy's 0.17s/frame due to fewer frames). Output: 693 lines of markdown. Math symbols frequently mangled by OCR (e.g., `* = f (x) + g(x)` instead of `dx = f(x)dt + g(x)dW`); transcript + visual inspection required for symbol recovery.
+
+### Phase 4: Synthesis
+
+Deep-dive report (1325 lines, 93KB) + summary (354 words). 10 appendices (concept map, transcript excerpts, math foundations, expanded connections, open questions, full bibliography, cross-references, synthesis summary, personal notes, glossary).
+
+### Phase 5: Verification
+
+All checks pass:
+- [x] All 7 deliverable artifacts present (transcript.json, transcript_clean.txt, video.log, frames/*.jpg, extraction_meta.json, ocr.md, video.mp4 gitignored)
+- [x] report.md is 1325 lines (within 1000-10000 target)
+- [x] summary.md is 354 words (within 200-400 target)
+- [x] All 8 report sections + 10 appendices populated, no TBDs
+- [x] Per-task commits with git notes
+- [x] video.mp4 properly gitignored
+- [x] VTT auto-sub file gitignored
+
+## Commits in this dispatch
+
+| SHA | Message |
+|---|---|
+| `16fbf561` | Phase 1: Acquire — transcript (1485 clean segments, 46.5KB) + 178MB mp4 |
+| `edd2f181` | Phase 2: Keyframes — 31 unique frames from 91 raw (threshold 0.05) |
+| `077cdf20` | Phase 3: OCR — 31 frames OCR'd via winsdk in 2.3s |
+| `f1d157bf` | Phase 4: Synthesis — report.md (1325 lines, 93KB) + summary.md (354 words) |
+
+## Key Findings
+
+- **Score + GFDT + DSM framework** — the talk's central contribution. Two directions (ansatz calibration via linear response; direct construction via drift decomposition) sharing a common primitive: the stationary score.
+- **Empirical scaling claim** — DSM+GFDT matches finite-difference accuracy at O(1) integrations per iteration vs O(P) for finite-difference. Demonstrated on 12-parameter model (5 iterations to convergence at 12× lower cost) and 5-parameter Lorenz-96 closure.
+- **Cyclo-stationary augmentation** — for periodically forced systems (PlaSim SST with annual cycle), augmenting the state with sin/cos harmonics converts a non-stationary problem to a stationary one in extended state space.
+- **Drift decomposition F = M·s + ∇·M** — any drift satisfying the stationary FP equation can be written as score-driven relaxation plus a free mobility tensor. Symmetric part controls fluctuations; antisymmetric part enables circulation without changing the measure.
+- **Rolling-caption dedup** — yt-dlp auto-subs produce cumulative text where each new event extends the previous. LCS-based dedup algorithm added to `clean_transcript.py`.
+
+## Next Steps
+
+8 child tracks remaining:
+- platonic_intelligence_kumar (B #1 — now unblocked)
+- free_lunches_levin (B #2 — now unblocked)
+- generic_systems_fields (C #1 — needs B done)
+- brain_counterintuitive (C #2 — needs B done)
+- neural_dynamics_miller (C #3 — needs B done)
+- multiscale_hoffman (C #4 — needs B done)
+- cs336_architectures (E — independent but R5 risk)
+- creikey_dl_cv (D — needs E done)
+
+Plus 1 synthesis track after all children ship.
+
+## Forward Connections Identified
+
+This talk informs:
+- **cs336_architectures_20260621**: DSM as training objective for diffusion LMs (same Vincent 2011 loss, different architecture).
+- **creikey_dl_cv_20260621**: DSM as training objective for image diffusion (DDPM).
+- **platonic_intelligence_kumar_20260621**: Speculative cross-modal score — the score function as a representation of the underlying data distribution suggests modality convergence at sufficient scale.
+
+## Backward Connections
+
+This talk builds on:
+- **cs229_building_llms_20260621**: Same DSM mathematics in EBM context.
+- **probability_logic_20260621**: Kolmogorov extension underpins SDE framework; Fokker-Planck is derived from the SDE.
+- **entropy_epiplexity_20260621**: Score is gradient of pointwise Shannon information; DSM fits a neural network to this gradient field.