Record universal detector v3-v8: diverse training shifts the curve

Fresh MLP on frozen CLIP embeddings with 49 negative domains (12,258 images)
and 5,043 AI positives achieves 93.0% AI-test recall with 39/49 domains at
zero false positives. Digital art FP dropped from 99.3% (Model 1) to 24.8%,
fashion from 62-71% to 12%, UI from 99.3% to 12%. Photo FPR 0.7%. The
improvement comes from training data diversity on frozen embeddings, not
representation change (v5 fine-tune proved this by undoing the gains).
Also adds .local-eval to ruff exclude so the gate covers only tracked files.

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

pyproject.toml

@@ -218,7 +218,7 @@ addopts = "-v --tb=short"
 [tool.ruff]
 target-version = "py311"
 line-length = 120
-exclude = ["_refs"]
+exclude = ["_refs", ".local-eval"]
 extend-exclude = ["*.md"]
 
 [tool.ruff.lint]
@@ -241,7 +241,7 @@ indent-style = "space"
 [tool.pyright]
 pythonVersion = "3.11"
 typeCheckingMode = "strict"
-exclude = ["_refs"]
+exclude = ["_refs", ".local-eval"]
 
 [[tool.pyright.executionEnvironments]]
 root = "tests"