Verified by extracting per-layer gamma_dfa from existing ViT-Mini snapshot
JSON (3 seeds, final epoch). On ViT all 4 layers have per-layer cosine
near zero (~0.001 with eps clamp); no layer dominates. Compare to ResMLP
where layer 0 has +0.42 and layers 1-4 are essentially zero.

The pitfall is real on ResMLP but the specific 'layer 0 dominates' framing
doesn't generalize to ViT. Reframed as 'aggregation hides per-layer
structure'; lesson is to always report per-layer values regardless of
which architecture-specific pattern you might be hiding.

Discovered in our own cnn_baseline.py: when the random feedback Bs (for
DFA) or bridge predictor (for SB/CB) are not persisted alongside the
model checkpoint, post-hoc Gamma computation cannot reconstruct the
local credit signal. Instead of erroring, the script falls back to
cos(BP_grad, BP_grad) = 1.0 and records that as Gamma. Reader who
doesn't notice the small 'Gamma_note' field interprets 1.0 as perfect
alignment.

Recommendation: always save aux nets alongside checkpoints; if they're
missing, report Gamma as N/A, not 1.0.

Codex round 15 #1 priority for the E&D-track paper:
  - protocol/protocol.py: 4 diagnostics (residual norms, BP grad norms,
    cross-batch direction stability, and a frozen-baseline comparator)
  - protocol/report.py: DiagnosticReport with per-diagnostic verdicts and
    pretty-printer
  - protocol/smoke_test.py: validates BP/DFA/EP checkpoints produce the
    expected verdicts (BP/EP trustworthy; DFA walked back via residual
    explosion + BP grad at floor)
  - protocol/README.md: usage, audit cases, threshold rationale
  - protocol/CHECKLIST.md: 6 evaluation pipeline pitfalls (norm(-1),
    cosine_similarity eps clamp, fp16 underflow, Bs reproducibility,
    aggregation, layer-0 dominance)
  - protocol/REPORTING_TEMPLATE.md: per-method fillable form for FA papers