# Project Status — 2026-03-30

## What's Done

### Data Pipeline
- [x] 72,045 paragraphs extracted from ~9,000 10-K filings + 207 8-K filings
- [x] 14 filing generators identified, quality metrics per generator
- [x] 6 surgical patches applied (orphan words + heading stripping)
- [x] Quality tier system: clean (80.7%), headed (10.3%), degraded (6.0%), minor (3.0%)
- [x] Embedded bullet detection (2,163 paragraphs flagged degraded, 0.5x sample weight)
- [x] All data integrity rules formalized (frozen originals, UUID-linked patches)

### GenAI Labeling (Stage 1)
- [x] Prompt v2.5 locked after 12+ iterations
- [x] 3-model panel: gemini-flash-lite + mimo-v2-flash + grok-4.1-fast
- [x] 150,009 annotations completed ($115.88, 0 failures)
- [x] Orphan word re-annotation: 1,537 paragraphs re-run ($3.30), merged into `stage1.patched.jsonl`
- [x] Codebook v3.0 with 3 major rulings

### DAPT + TAPT Pre-Training
- [x] DAPT corpus: 14,568 documents, ~1.056B tokens, cleaned (XBRL, URLs, page numbers stripped)
- [x] DAPT training complete: eval loss 0.7250, perplexity 1.65. 1 epoch on 500M tokens, ~14.5h on RTX 3090.
- [x] DAPT checkpoint at `checkpoints/dapt/modernbert-large/final/`
- [x] TAPT training complete: eval loss 1.0754, perplexity 2.11. 5 epochs, whole-word masking, ~50 min on RTX 3090. Loss: 1.46 → 1.08.
- [x] TAPT checkpoint at `checkpoints/tapt/modernbert-large/final/`
- [x] Custom `WholeWordMaskCollator` (upstream `transformers` collator broken for BPE tokenizers)
- [x] Python 3.14 → 3.13 rollback (dill/datasets pickle incompatibility)
- [x] Procedure documented in `docs/DAPT-PROCEDURE.md`

### Documentation
- [x] `docs/DATA-QUALITY-AUDIT.md` — full audit with all patches and quality tiers
- [x] `docs/EDGAR-FILING-GENERATORS.md` — 14 generators with signatures and quality profiles
- [x] `docs/DAPT-PROCEDURE.md` — pre-flight checklist, commands, monitoring guide
- [x] `docs/NARRATIVE.md` — 11 phases documented through TAPT completion

## What's In Progress

### Human Labeling (139/1,200)
- 3 of 6 annotators started: 68 + 50 + 21 paragraphs completed
- Deployed via labelapp with quiz gating + warmup
- Each annotator needs 600 paragraphs (BIBD assignment)

## What's Next (in dependency order)

### 1. Fine-tuning pipeline (no blockers — can build now)
Build the dual-head classifier (7-class category + 4-class specificity) with:
- Shared ModernBERT backbone + 2 linear classification heads
- Sample weighting from quality tiers (1.0 clean/headed/minor, 0.5 degraded)
- Confidence-stratified label assembly (unanimous → majority → judge)
- Train/val/test split with stratification
- Ablation configs: base vs +DAPT vs +DAPT+TAPT

### 3. Judge prompt v3.0 update (no blockers — can do now)
Update `buildJudgePrompt()` with codebook v3.0 rulings:
- Materiality disclaimers → Strategy Integration
- SPACs → None/Other
- Person-vs-function test for Management↔RMP
Then re-bench against gold labels.

### 4. Training data assembly (blocked on judge + human labels)
Combine all annotation sources into final training dataset:
- Unanimous Stage 1 labels (35,204 paragraphs, ~97% accuracy)
- Calibrated majority labels (~9-12K, ~85-90%)
- Judge high-confidence labels (~2-3K, ~84%)
- Judge low-confidence → downweight or exclude
- Quality tier sample weights applied

### 4. Judge production run (blocked on human gold labels)
Run judge on ~409 unresolved + flagged majority cases. Validate against expanded gold set from human labels.

### 5. Fine-tuning + ablations (blocked on steps 1-3)
7 experiments: {base, +DAPT, +DAPT+TAPT} × {with/without SCL} + best config.

### 6. Evaluation + paper (blocked on everything above)
Full GenAI benchmark (9 models) on 1,200 holdout. Comparison tables. Write-up.

## Parallel Tracks

```
Track A (GPU):  DAPT ✓ → TAPT ✓ → Fine-tuning → Eval
                                                ↑
Track B (API):  Judge v3 → Judge run ───────────┤
                                                ↑
Track C (Human): Labeling (139/1200) → Gold set validation
                                                ↑
Track D (Code): Fine-tune pipeline build ───────┘
```

DAPT + TAPT complete. Track D (fine-tune pipeline) can proceed now. Track B can start (prompt update) but production run waits for Track C. Everything converges at fine-tuning.

## Key File Locations

| What | Where |
|------|-------|
| Patched paragraphs | `data/paragraphs/training.patched.jsonl` (49,795) |
| Patched annotations | `data/annotations/stage1.patched.jsonl` (150,009) |
| Quality scores | `data/paragraphs/quality/quality-scores.jsonl` (72,045) |
| DAPT corpus | `data/dapt-corpus/shard-*.jsonl` (14,756 docs) |
| DAPT config | `python/configs/dapt/modernbert.yaml` |
| TAPT config | `python/configs/tapt/modernbert.yaml` |
| DAPT checkpoint | `checkpoints/dapt/modernbert-large/final/` |
| Training CLI | `python/main.py dapt --config ...` |