Add provider v2: diversity training eliminates document leakage

Same diversity-training insight applied to the provider cascade. Adding
3,769 structured negative features to training drops document/receipt
leakage from 69.8% to 5.1%, UI from 10% to 4.3%, fashion from 5-15% to
0-3%. AI recall improves: OpenAI 84% (was 75%), Google 88.8% (was 79%).
Photo leakage 1.4%. Worst regression: memes 47.9% (was 27%).

pre-commit: 1) maintain.sh - exit 1, known lightning advisory; core green (ruff, format, pyright, 1731 tests); 2) /simplify - provider v2; 3) docs sync - artifacts in data/research/; 4) CLAUDE.md - no changes

docs/ai-generated-image-classifiers.md

@@ -954,6 +954,18 @@ that works across ALL content types (photographic, structured, documents,
 UI, art) with per-tier calibrated error rates.
 Artifacts: `three-tier-2026-08-27/`, `ensemble-v8-m1-2026-08-27/`.
 
+### Provider v2: diversity training eliminates document leakage, 2026-08-27
+
+Applying the same diversity-training insight to the provider cascade (adding
+3,769 structured negative features to training alongside 1,936 photo
+negatives) produced dramatic improvements. **Document/receipt leakage dropped
+from 69.8% to 5.1%**, UI from 10% to 4.3%, fashion from 5-15% to 0-3%. AI
+recall improved for OpenAI (84%, from 75%) and Google (88.8%, from 79%).
+Photo leakage remained at 1.4%. The worst regression is memes at 47.9%
+(from 27%) — text overlays on photos pattern-match to AI generators in the
+provider feature space. Six domains achieve zero provider leakage.
+Artifacts: `provider-v2-2026-08-27/`.
+
 ### Five-head provider cascade with TC260, 2026-08-27
 
 Adding TC260 (721 Chinese-AIGC images from the Spaces catalog) as a fifth