stage_0: add prop-specificity audit + note in README

stage_0/README.md

@@ -36,6 +36,10 @@ Three-step process, documented in the artifacts below.
 
 Dim 48 is the canonical anthropogenic-scene axis in EUPE-ViT-B. It activates strongly on person scenes and on person-associated objects (sports equipment, wearable accessories, handheld items), and is suppressed on non-human animals and non-anthropogenic structures. Alone it delivers F1 = 0.83 as a 2-parameter classifier. The additional 39 dims stack on mostly orthogonal axes to reach F1 = 0.89 at 1 free parameter.
 
+## Prop-specificity audit
+
+Artifacts `prop_specificity_audit.json` and `prop_audit_image_list.json` record a later audit of how well the classifier distinguishes "person-scene" from "scene containing person-associated objects but no person." 8,479 ImageNet training images were filtered with YOLO26l at confidence 0.25 to retain only images where no person was detected, drawn from 20 person-associated synsets (racket, tie, backpack, helmet, ski, remote, musical instruments, etc.). The Stage 0 classifier fires on **5.9%** of those truly person-free images at its operating threshold — tight enough that the "fires on tennis rackets and backpacks" concern in the per-class activation analysis is mostly a co-occurrence artifact in COCO (tennis racket images almost always contain the player). Adding up to 15 prop-specific negative dims lowers the false-positive rate to 2.7% but costs F1 on the primary person task.
+
 ## Hardware footprint estimate
 
 At INT8 precision, the classifier synthesizes to an estimated 2,500–4,100 gates: two Wallace-tree adders (50-input each), one subtractor, one comparator. For reference, a 768-dim INT8 MAC unit is roughly 65,000 gates, and the prior 4,614-parameter multi-output person detector synthesizes to roughly 391,000 gates.

stage_0/prop_specificity_audit.json

@@ -0,0 +1,706 @@
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