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ResMLP (4-block d=256, with out_ln, CIFAR-10):
s42: DFA (a) ep 8, (b) ep 4, acc 0.308
s123: DFA (a) ep 11, (b) ep 4, acc 0.320
s456: DFA (a) ep 8, (b) ep 3, acc 0.300
ViT-Mini (4-block d=128, cls token + terminal LN, CIFAR-10):
s42: DFA (a) ep 1, (b) ep 3, acc 0.256
s123: DFA (a) ep 1, (b) ep 2, acc 0.202
s456: DFA (a) ep 1, (b) ep 3, acc 0.253
StudentNet (4-block d=128, NO terminal LN, synthetic alpha=1.0):
s42: DFA (a) ep 18, (b) NEVER, acc 0.332
s123: DFA (a) ep 14, (b) NEVER, acc 0.314
s456: DFA (a) ep 25, (b) NEVER, acc 0.336
BP: never fires on any seed x any architecture (9/9 sanity passes).
Key cross-architecture finding: diagnostic (b) is specifically the LN-
driven failure mode. Without out_ln, the BP grad never crosses the 1e-7
floor, even though (a) still fires (the residual stream still grows, just
without the LN-cancellation pathology that drives the BP grad to the
floor). This is the causal architectural control: (b) specifically tests
'is terminal-LN gradient cancellation active?' and (a) tests 'is the
residual stream growing without bound?'. They are linked but separable.
This is the §3 cross-architecture validation evidence.
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Old metric: max(||h||) / max(||h_0||, eps). False-positives on ViT-style
architectures because the cls token at layer 0 (right after patch_embed)
has anomalously small magnitude (~0.3-1.5), inflating the ratio even on
healthy BP-trained ViTs.
New metric: max_l(||h_{l+1}|| / ||h_l||) — the largest single-block
residual amplification. Architecture-invariant.
Calibration:
- BP-trained, late training: <5x per block
- BP ViT, early epochs (cls token resolving): 13-25x max
- DFA-trained ResMLP/ViT: 100-4000x per block
Threshold raised from 10 to 50 to sit cleanly between healthy-early-
training (max 25) and failure-regime (min 100).
Re-verifications:
- smoke test (BP/DFA/EP): all 3 verdicts unchanged
- random init (3 seeds): trustworthy on all 3
- 5-method audit table single-seed: identical verdicts
- decision-utility ablation: identical (still 0/5 by S1, 3/5 by S_full)
- temporal evolution 3-seed: (b) now fires first at ep 3-4, (a) at ep
8-11. Both well before training ends. The 'protocol fires ~92 epochs
early' story still holds.
- ViT temporal evolution: BP no longer false-fires; DFA fires (a) ep 1,
(b) ep 3 — protocol works on the second architecture.
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Each bug from the catalog has a synthetic reproducer that runs in <1 sec
without GPU:
Bug 1: x.norm(-1) on a 2x2 tensor returns 1.143 (L_{-1} of whole tensor)
instead of [5, 10] (per-row L_2 along dim=-1).
Bug 2: F.cosine_similarity(a, b) with ||b||=5e-10 returns +0.000905
instead of the true +0.018101. The clamp (eps=1e-8) underestimates
the divisor 20x.
Bug 3: 5e-10 in fp16 -> 0 (underflows smallest subnormal ~6e-8).
Downstream F.cosine_similarity returns NaN. bf16 works because it
shares fp32's exponent range.
Bugs 4-6 (Bs reproducibility, aggregation, layer-0 dominance) require a
trained network and are demonstrated inside audit_table and
ablation_decision_utility.
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s42: (a)+(b) fire at epoch 4, DFA final acc 0.3076
s123: (a)+(b) fire at epoch 4, DFA final acc 0.3203
s456: (a)+(b) fire at epoch 3, DFA final acc 0.2998
BP never fires on any seed (final acc 0.61-0.63).
The 'protocol catches it 96 epochs early' finding is fully reproducible
across seeds.
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Replays per-epoch logged data from results/snapshot_evolution_v2/ through
the protocol thresholds.
Result: diagnostics (a) ||h_l|| explosion AND (b) ||g_L|| at floor BOTH
first fire at epoch 4 of DFA training. At that point, DFA test acc is
0.308 — its final value at epoch 100 is also 0.308. The protocol could
have walked back the headline 96 epochs before training finished.
DFA's gamma hovers at 0.087-0.107 for all 100 epochs. A reviewer looking
at acc+gamma would conclude 'DFA is hovering at 31% acc with ~0.10
alignment, both reasonable'. Wrong on both counts.
BP never fires any diagnostic at any epoch. Stays bounded at ||h_L||~200,
||g_L||~3-5e-5, accuracy climbs to 0.61.
This is the temporal validation of decision utility: the protocol catches
the pathology AS IT HAPPENS, not just retrospectively.
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3-seed random init ResMLP gives chance accuracy (~10%) but the protocol
verdict is 'trustworthy' on all 3 seeds:
- residual norms ~8.7 across all layers (no growth, bounded)
- BP gradient norms ~8e-3 (healthy, well above 1e-7 floor)
- cross-batch stability 0.08-0.18 (in the BP/EP range)
This is the answer to the likely reviewer question: 'is your protocol just
flagging anything that doesn't perform well?' Answer: no. Random init is
at chance and the protocol passes it. The walked-back trained methods are
walked back because of the *measurements*, not because of the accuracy.
Notable: random init g-norms (8e-3) are actually HIGHER than BP-trained
ones (4e-4) — BP training reduces the gradient magnitude as loss decreases.
So the protocol distinguishes 3 distinct regimes: (1) untrained healthy,
(2) trained-and-still-healthy (BP/EP), (3) trained-into-pathology (DFA/SB/CB).
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3 seeds × 5 methods × 4 diagnostics = 60 measurements. Key reproducibility
findings:
- BP: trustworthy on all 3 seeds (acc 0.61-0.62, h_L ~200, g_L ~3-4e-4)
- EP: trustworthy on all 3 seeds (acc 0.29-0.36, h_L 3-8e3, g_L ~1e-4)
- DFA, SB, CB: walked back on all 3 seeds × all 3 of (a)/(b)/(d)
Diagnostic (c) is bimodal across seeds — confirms the prior memory finding:
- DFA s42=0.047 (noise), s123=0.436 (drift), s456=-0.005 (noise)
- SB s42=0.992 (drift), s123=0.561 (drift), s456=0.035 (noise)
- CB s42=0.352 (drift), s123=0.250 (~edge), s456=0.518 (drift)
(c) catches different methods on different seeds. (a)/(b)/(d) catch all 3
failing methods on all 3 seeds — robust binary detection.
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Builds on the 5-method audit JSON. For each method, evaluates 7 reporting
strategies (S0=acc only, S1=+Γ field standard, S2-S5=+single diagnostic,
S_full=full protocol), and emits the verdict each strategy would have
reached.
Result: 3 of 5 methods (DFA/SB/CB) are walked back by S_full but NOT by S1.
Each of (a)scale, (b)floor, (d)frozen is independently sufficient for
binary detection of those 3 failures. Diagnostic (c)stability adds
sub-mode discrimination (drift vs noise) but not new positive detections.
This is the §3 protocol decision-utility evidence.
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5-method audit table on 4-block d=256 ResMLP CIFAR-10 seed 42:
- BP: trustworthy (acc 0.615, h_L=2e2, g_L=4e-4, stab 0.099)
- DFA: walked back via (a)+(b)+(d) — h_L=4e8, g_L=4e-9, undercuts frozen
- State Bridge: walked back via all 4 diagnostics — stability 0.992 is the
cleanest possible drift-dominated case
- Credit Bridge: walked back via all 4 — stability 0.352, also drift mode
- EP: trustworthy (acc 0.359, h_L=3e3, g_L=2e-4, stab -0.036) — paper's
internal control case
This is the §2 audit evidence for the main-track paper. Confirms that
standard headline acc + Γ silently fails on 3 of 5 methods on this
architecture, while the 4-diagnostic protocol catches all three.
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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
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