code/README.md

Annotation pipeline — code backup (EgoProactive synthetic data)

Code that produced egoconv_dense_* and egolongqa_* in this repo. Backed up 2026-08-17 because the CPU VM that ran it (217.18.55.12) was destroyed and /workspace is not a persistent volume.

⚠️ Known loss

annotate_full.py — the resume-capable driver loop that ran the 235-clip dense pass — existed only on that VM and is gone. It is reconstructible: it iterated egoconv235_manifest.json, called the ambient_agent per clip with annotation_prompt.txt, wrote one JSON per clip, and skipped clips whose output already existed (that skip-on-existing behaviour is how the run was resumed after the OpenRouter 402). launchers/run_dense.sh and resume_dense.sh show exactly how it was invoked. The closest surviving equivalents are scripts/annotate_v2.py / annotate_videos.py.

Contents

• ambient_agent/annotation_prompt.txt — the tuned t3 prompt (the valuable artefact). Policy: setup-phase coverage from t=0, one cue per distinct step (no target count), collapse repetition, fire at onset, −0.5 s shift. Was untracked in the ambient_agent working tree.
• ambient_agent/ANNOTATION_GUIDE.md — operating notes.
• ambient_agent/agent.py, llm.py + UNCOMMITTED.diff, BASE_COMMIT.txt — local modifications on top of ambient-intelligence-hq/ambient (see BASE_COMMIT.txt); never committed upstream.
• scripts/annotate_*.py — annotation variants from /workspace/ambient (a repo with no remote).
• launchers/*.sh — how the dense + egolongqa runs were launched, resumed and transferred.

Prompt-tuning history (do not re-learn this the hard way)

t2 overfit the tuning videos: 0.643 on tuning → 0.494 held-out, worse than baseline. t3 was chosen because setup-coverage + repetition-collapse generalised (~+0.02). Always validate a prompt change on ≥5 held-out videos, never on the videos used to tune it.

Orchestrator

deepseek/deepseek-v4-flash-0731 via OpenRouter (API key from env, never hardcoded) + a self-hosted NVFP4 Qwen3.6-27B vision model behind Docker vLLM.