Update NOTE.md + EVIDENCE_SUMMARY.md with FA results (2026-04-23)

NOTE.md: added comprehensive current-status section at the top with
the full 6-method audit table (BP/FA/EP/DFA/CB/SB), FA vs DFA key
comparison, depth sweep, penalty rescue comparison, cross-method
functional triangulation, and open items. Old Phase 10A content kept
below as historical reference.

EVIDENCE_SUMMARY.md: added "Vanilla FA vs DFA" section with the
paper-changing finding (FA 0.401 ± 0.009 vs DFA 0.306 ± 0.008,
FA has genuine deep cos +0.33, no Mode 1(b) collapse) and the
d=512 depth sweep table.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

2 files changed, 124 insertions, 0 deletions

diff --git a/NOTE.md b/NOTE.md
index b7ae247..caaab59 100644
--- a/NOTE.md
+++ b/NOTE.md
@@ -1,5 +1,103 @@
 # Experiment Notes
 
+## Current Status (2026-04-23): Paper v2.39 + FA experiments complete
+
+Paper: `paper/main.tex` (v2.39, commit `bcfec1d`). NeurIPS 2026 E&D track.
+FA implementation + results: commit `88ff85c`.
+
+### Paper structure (v2.39)
+
+7 sections with 14 subsections. Key sections:
+- §3 Mode 1: Measurement Degeneracy (residual-stream growth + LN gradient collapse)
+- §4 Mode 2: Credit Quality (the paper's main contribution)
+  - §4.2 Functional triangulation: 3 metrics (accuracy, nudging, training-loss) rank SB ≫ CB ≈ DFA; only deep cos disagrees
+  - §4.3 Mode 2 → Mode 1 causal hypothesis: poor credit quality drives activation growth
+- §5 Intervention + cross-arch evidence
+- §6 4-diagnostic evaluation protocol
+
+### 6-method audit (4-block d=256 pre-LN ResMLP, CIFAR-10, 100ep, 3 seeds)
+
+| Method | Test Acc | Deep Cos | ‖h_L‖ | ‖g_L‖ | Mode 1(b)? |
+|--------|----------|----------|--------|--------|------------|
+| **BP** | 0.615 ± 0.004 | ≈ 1.0 | ~200 | ~4e-4 | no |
+| **FA** (NEW) | **0.401 ± 0.009** | **+0.33** | **~1e5** | **~1e-6** | **no** |
+| EP | 0.316 ± 0.037 | 0.008 | ~3e3 | ~1e-4 | no |
+| DFA | 0.306 ± 0.008 | ~0 | ~5e8 | ~4e-10 | **YES** |
+| CB | 0.289 ± 0.031 | 0.07 | ~7e7 | ~2e-9 | **YES** |
+| SB | 0.205 ± 0.039 | 0.005 | ~2e8 | ~2e-9 | **YES** |
+| Frozen baseline | 0.349 ± 0.003 | — | — | — | — |
+
+### FA vs DFA: the key comparison
+
+**Same local loss form** ⟨f_l, a_l⟩, same architecture, same optimizer.
+**Only difference**: how a_l is computed.
+- DFA: a_l = B_l^T @ e_T (direct projection, d×C random matrix)
+- FA: a_l = B_l @ a_{l+1} (sequential backward, d×d random matrix, starts from exact ∂CE/∂h_L)
+
+| | FA | DFA |
+|---|---|---|
+| Test acc | **0.401** (+5.2 pp above frozen) | 0.306 (-4.3 pp below frozen) |
+| Deep cos 100ep | **+0.33** (genuine) | ~0 (degenerate) |
+| Deep cos 5ep | **+0.32** (from the start) | -0.008 (null) |
+| ‖h_L‖ | ~10⁵ | ~5×10⁸ (3 OOM larger) |
+| ‖g_L‖ | ~10⁻⁶ (meaningful) | ~10⁻¹⁰ (floor) |
+| Mode 1(b) fires? | **NO** | YES |
+| FA random-target h_L | ~1.3e5 | ~1.7e8 (DFA) |
+
+**Interpretation**: FA's sequential propagation preserves enough credit-direction quality to prevent the catastrophic activation growth that DFA exhibits. This is the **strongest empirical support for the Mode 2 → Mode 1 causal hypothesis**: better credit → less growth → no gradient-floor collapse.
+
+### FA depth sweep (d=512, 100ep, s42)
+
+| L | FA acc | DFA acc | FA deep cos | DFA deep cos |
+|---|--------|---------|-------------|--------------|
+| 2 | 0.350 | n/a | +0.96 | n/a |
+| 4 | 0.424 | n/a | +0.29 | n/a |
+| 6 | 0.401 | n/a | +0.16 | n/a |
+| 8 | 0.409 | 0.306 | +0.11 | -0.0001 |
+| 12 | 0.404 | 0.309 | +0.09 | -0.0001 |
+
+FA deep cos decreases with depth but stays positive. DFA is ~0 everywhere.
+
+### Penalty rescue comparison (30ep, d=256, 3 seeds)
+
+| | FA no-pen | FA+pen λ=1e-2 | DFA no-pen | DFA+pen λ=1e-2 |
+|---|---|---|---|---|
+| acc | 0.372 ± 0.007 | 0.369 ± 0.003 | 0.301 ± 0.006 | 0.360 ± 0.002 |
+
+Penalty barely helps FA (it doesn't need rescue) but substantially helps DFA (+5.9 pp).
+
+### Cross-method functional triangulation (§4.2, penalty-rescued d=256 30ep)
+
+| Metric | SB+pen | CB+pen | DFA+pen | Ordering |
+|--------|--------|--------|---------|----------|
+| Accuracy | **0.453** | 0.360 | 0.360 | SB ≫ CB ≈ DFA |
+| Nudging (loss Δ) | **-1.93e-3** | -4.26e-4 | -4.98e-5 | SB ≫ CB ≈ DFA |
+| Training loss decrease | **-0.447** | -0.121 | -0.095 | SB ≫ CB ≈ DFA |
+| Deep cos | +0.322 | **+0.679** | +0.151 | CB > SB > DFA |
+
+3 functional metrics agree SB ≫ CB ≈ DFA. Deep cos is the ONLY one that disagrees.
+
+### Auditable JSON sources
+
+All paper numbers derive from saved files in `results/`. See `reproduce_all.ipynb` for the complete source map. Key files:
+- `results/protocol_audit/audit_table_s42_s123_s456.json` — Table 1
+- `results/fa_main_audit/results_cifar10.json` — FA main results
+- `results/nudging_test_3seed_summary.json` — §4.2 nudging
+- `results/training_loss_decrease_3seed.json` — §4.2 loss trajectory
+- `results/matched_30ep_control_summary.json` — §5.2 BP+DFA controls
+- All ± values use ddof=1 (sample std with Bessel correction) as of v2.38.
+
+### Open items
+
+1. **FA results not yet in the paper** — need to add FA as a 6th method row in Table 1 and discuss in §3-§5.
+2. **FA on ViT-Mini / CNN**: experiment scripts (`snapshot_evolution_vit.py`, `cnn_baseline.py`) don't yet support FA. Need implementation.
+3. **§7 falsification test**: the FA result itself is a strong empirical test of Mode 2 → Mode 1. Could frame FA as the "better credit quality at fixed ‖f‖" control proposed in §7.2.
+4. **Codex restructure** (v2.39): applied codex's subsection proposal. 14 subsections across 7 sections.
+
+---
+
+## Historical Notes (pre-v2.xx, kept for reference)
+
 ## Experiment Phases
 - **debug**: Initial implementation, rapid iteration (commits ce24e36)
 - **pilot**: Controlled iteration (commits 0b9ebb2, 7baf7ae)
diff --git a/protocol/EVIDENCE_SUMMARY.md b/protocol/EVIDENCE_SUMMARY.md
index bab8764..d6d3945 100644
--- a/protocol/EVIDENCE_SUMMARY.md
+++ b/protocol/EVIDENCE_SUMMARY.md
@@ -129,6 +129,32 @@ on deep layers. **Caught by direct per-layer cosine measurement.**
 | BP | 0.585 ± 0.001 | **0.532 ± 0.006** | −5.3 pp (capacity loss) |
 | DFA | 0.301 ± 0.005 | 0.360 ± 0.001 | +5.9 pp (rescue) |
 
+### Vanilla FA vs DFA (2026-04-22, commit 88ff85c)
+
+**PAPER-CHANGING FINDING.** FA (Lillicrap 2016 sequential backward with d×d random matrices) is dramatically different from DFA on the same architecture.
+
+| | FA | DFA |
+|---|---|---|
+| Test acc (100ep, 3-seed, d=256) | **0.401 ± 0.009** | 0.306 ± 0.008 |
+| vs frozen 0.349 | **+5.2 pp above** | -4.3 pp below |
+| Deep cos | **+0.33** | ~0 (degenerate) |
+| ‖h_L‖ | ~10⁵ | ~5×10⁸ |
+| ‖g_L‖ | ~10⁻⁶ (meaningful) | ~10⁻¹⁰ (floor) |
+| Mode 1(b) fires? | **NO** | YES |
+
+Same local loss ⟨f_l, a_l⟩, same architecture, same optimizer. Only difference: how a_l is computed (sequential vs direct projection). FA's sequential backward preserves credit quality → prevents catastrophic Mode 1 growth. **Strongest empirical support for Mode 2 → Mode 1 causal hypothesis.**
+
+Source: `results/fa_main_audit/results_cifar10.json`
+
+FA depth sweep (d=512, 100ep, s42):
+| L | FA acc | FA deep cos | DFA acc | DFA deep cos |
+|---|---|---|---|---|
+| 2 | 0.350 | +0.96 | — | — |
+| 4 | 0.424 | +0.29 | — | — |
+| 6 | 0.401 | +0.16 | — | — |
+| 8 | 0.409 | +0.11 | 0.306 | ~0 |
+| 12 | 0.404 | +0.09 | 0.309 | ~0 |
+
 ### Round 20 phrasing for the gap
 
 **Lower bound on non-capacity gap**: matched penalty controls show that only part of DFA's deficit is attributable to the representational/optimization cost of the penalty itself; a substantial residual remains and is consistent with poorer credit assignment.