Chaim
4debe9995b
A/B eval (eval_retrieval.py, 86-query gold-set) showed the 0.5 default was mis-tuned: the image side was too heavy and dragged precedent_library recall 0.971 -> 0.885. Sweep 0.5..0.75 — at 0.65 multimodal beats text-only on every overall metric AND every corpus (R@5 0.994 vs 0.989, nDCG@5 0.960 vs 0.944, MRR 0.954 vs 0.936). Dafna approved. - MULTIMODAL_TEXT_WEIGHT=0.65 set in Coolify (legal-ai, runtime) + redeploy. - baseline.json updated to the 0.65 config (future regression reference). - #15 done (premise was stale — multimodal already default on 110 docs; the win was tuning the weight, not the backfill). - #80 opened: the costly 140-doc legacy backfill is deferred until a targeted image-answer gold-set proves the table/image value prop (untested here). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>