Add FA diagnostic protocol reference implementation

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

1 files changed, 52 insertions, 0 deletions

diff --git a/protocol/__init__.py b/protocol/__init__.py
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+"""
+FA Diagnostic Protocol — reference implementation.
+
+A drop-in toolkit for evaluating Feedback Alignment (and related local-credit)
+methods on modern residual architectures. Implements the four diagnostics
+described in *Beyond Accuracy and Alignment: A Diagnostic Protocol for
+Feedback Alignment Evaluation*:
+
+    (a) per-layer residual stream norm   ||h_l||_2
+    (b) per-layer BP gradient norm       ||g_l||_2
+    (c) cross-batch direction stability  s_l
+    (d) frozen-blocks control accuracy   acc_frozen
+
+The motivation is that headline accuracy and offline Γ (cosine alignment of
+local credit to BP gradient) can both silently fail on modern pre-LayerNorm
+residual networks: residual-stream explosion drives ||g|| ~10^-10 and turns Γ
+into a measurement of cosine to a numerical-floor reference vector. This
+protocol catches that regime change and tells you whether your reported
+numbers are interpretable.
+
+Quick start:
+
+    from protocol import diagnose, DiagnosticReport
+
+    report = diagnose(
+        model=trained_model,
+        eval_batches=list_of_(x, y)_pairs,
+        frozen_baseline_acc=accuracy_of_random_blocks_baseline,
+        method_name="DFA",
+        notes="4-block d=256 ResMLP, CIFAR-10",
+    )
+    print(report)
+
+The report flags any diagnostic that crossed a "degenerate regime" threshold
+and emits a verdict ("trustworthy" / "needs walk-back").
+"""
+
+from .protocol import (
+    residual_norms,
+    bp_grad_norms,
+    cross_batch_direction_stability,
+    diagnose,
+)
+from .report import DiagnosticReport
+
+__all__ = [
+    "residual_norms",
+    "bp_grad_norms",
+    "cross_batch_direction_stability",
+    "diagnose",
+    "DiagnosticReport",
+]