diff options
| author | YurenHao0426 <Blackhao0426@gmail.com> | 2026-07-22 04:51:49 -0500 |
|---|---|---|
| committer | YurenHao0426 <Blackhao0426@gmail.com> | 2026-07-22 04:51:49 -0500 |
| commit | d344f7fa4b8a6b907d364d1900348ca8e4b709ce (patch) | |
| tree | 13fcf933e308c918c8047aa343787a662044e962 | |
| parent | e8d698e9da1e2441d1d137eb8f8ab6cd46b4282c (diff) | |
results: confirm C2 feedback amortization bottleneck
33 files changed, 96 insertions, 5 deletions
@@ -76,11 +76,16 @@ identifies the traffic relationship without observing the teaching component. - The inherited learned-feedback backbone preserves performance with depth better than fixed DFA in the audited flattened-CIFAR MLP setting. -- Controlled pilots show that per-cell residualization can protect learning from soma-coupled and - endogenous top-down traffic; the latter remains a validation-only, single-seed result until C1 is - completed. -- Simultaneous batched perturbations make the inherited calibration practical in this codebase; - a hardware-independent query/FLOP advantage is not yet established. +- Frozen controls show that per-cell residualization protects learning from soma-coupled traffic. + It mitigates but does not fully remove endogenous top-down traffic, so arbitrary contextual + innovation is not an allowed teaching-signal claim. +- Simultaneous batched perturbations have a frozen hardware-independent advantage: K1/every4 + retains 112.9% of the K16/every4 gain over DFA with 16x fewer logical loss queries, 11x less + calibration work, and 5.3x less total forward-equivalent work on CIFAR-10 d20/w64. +- Direct node perturbation solves the frozen useful-depth task while the amortized context + vectorizer does not. This identifies feedback amortization as the current engineering + bottleneck; it is not a novelty claim and the direct estimator's 68.4x work precludes presenting + it as the scalable algorithm. ## Claims currently forbidden @@ -354,6 +354,36 @@ nonfinite loss and chance accuracy. **The independent recovery gate fails and C2 prohibited.** A richer vectorizer can improve mean credit without curing coupled A/W basin instability; this is now a documented limitation rather than an oral-level scaling result. +## C2 direct node-perturbation causal diagnosis (validation only) + +The failed context-vectorizer panel leaves two distinct explanations: the local eligibility/update +rule may be unable to solve the compositional task, or the learned apical vectorizer may fail to +amortize a causal direction that would work if it were available. To separate them, direct node +perturbation replaces every hidden teaching vector by an unamortized antithetic layerwise estimate +on every update. A task-seed-0 development screen selected `eta=0.03` and K16; the independent +panel then reused the exact untouched validation splits for task seeds 3--5 and crossed d1/d4 with +five model seeds. All 30 runs came from clean revision `e8d698e`; test sets remain unevaluated. + +| method | depth | validation acc (%) | d1-to-d4 gain | d4 lesion drop | +|:-------|------:|-------------------:|--------------:|---------------:| +| BP | 1 | 68.633 ± 5.351 | — | — | +| BP | 4 | 97.187 ± 1.532 | +28.553 | +34.170 | +| context-SDIL | 1 | 63.977 ± 8.636 | — | — | +| context-SDIL | 4 | 79.120 ± 22.765 | +15.143 | +27.150 | +| direct NP | 1 | 65.703 ± 8.800 | — | — | +| direct NP | 4 | 96.150 ± 1.719 | +30.447 | +25.743 | + +Direct NP recovers `106.6%` of BP's mean depth gain. All 15 paired runs improve with depth, 14/15 +are harmed by the frozen final-block lesion, all final losses are finite, and its d4 endpoint beats +context-SDIL by `17.030` points and the stronger FA/DFA endpoint by `15.387` points. **The frozen +causal diagnosis passes: the local eligibility rule and a forward-only causal direction are +sufficient here; learning a reliable amortized apical vectorizer is the present bottleneck.** + +This is a diagnosis and upper-cost baseline, not the proposed scalable method. At d4 it uses a mean +`82,560,000` per-example scalar loss evaluations and `43,757,037` forward-equivalent examples, +`68.4x` ordinary training work, with `108.2` seconds mean wall time. The next recovery must retain +the vectorizer and disclose how much of this query burden it avoids. + ## Paper-faithful non-backprop baselines `PETITE` in the initial request is treated as **PEPITA**. Implementations and hyperparameters were @@ -419,6 +449,8 @@ Five-depth scaling: `experiments/figure_scaling_sweep.sh`; canonical local basel `experiments/ep_original_sweep.sh`; exact d1 comparison: `experiments/ep_archmatch_sweep.sh`; canonical d2 comparison: `experiments/ep_depth2_sweep.sh`; near-parameter comparison: `experiments/ep_matched_sweep.sh`. +C2 direct causal diagnosis: `experiments/c2_nodepert_validation.sh` followed by +`python experiments/analyze_c2_nodepert_validation.py`. Audited aggregate tables: `experiments/analyze_verified.py`. The claim-locked finalizer is `experiments/finalize_claims.sh`; it rejects missing/dirty inputs, regenerates both main figures, their captions, source-hash manifest, and `results/audited_tables.md`, then runs all smoke checks. @@ -148,6 +148,30 @@ secondary conditions passed at the boundary. Context conditioning improves subst linear SDIL's 21.0% recovery but does not make causal-feedback learning reliable enough. The predeclared mechanism-recovery branch is closed and no C2 test panel will run. +The follow-up causal diagnosis replaces the learned vectorizer with direct layerwise antithetic +node-perturbation targets on every update. Task seed 0 and model seeds 0--2 were development-only: +an estimator/LR screen selected `eta=0.03`, and a predeclared smaller-K-within-one-point rule chose +K16 over K4. The frozen diagnosis then used task seeds 3--5, model seeds 0--4, d1/d4, the same +training-only validation splits, and no intermediate held-out metrics. It diagnoses an +amortization bottleneck only if it recovers at least 70% of BP's depth gain, has at least 10/15 +positive paired gains, beats context-SDIL d4 by at least 2 points, has a mean lesion drop of at +least 2 points with at least 10/15 positive lesions, and has no nonfinite final loss. + +**Direct causal diagnosis status: passed on 2026-07-22.** Direct NP recovered `106.6%` of BP's +depth gain (`+30.447` versus `+28.553` points), reached `96.150 ± 1.719%` at d4, improved in 15/15 +depth pairs, and had positive lesions in 14/15 pairs. It beat context-SDIL d4 by `17.030` points; +all losses were finite. The result rules out the local eligibility rule and causal direction as +the primary C2 failure, but costs `68.4x` ordinary forward-equivalent work and is not a scalable +solution. + +One calibration-quality recovery is now allowed on development task seed 0 only. It keeps the +bounded context vectorizer, `eta=0.03`, `eta_A=0.01`, and every-four-step calibration fixed, and +compares layerwise K4 against K16 at d1/d4 over model seeds 0--2. Select the higher mean d4 +validation accuracy, choosing K4 if it lies within one point of K16; any nonfinite candidate is +ineligible. No result from task seeds 3--5 may enter this selection. A selected protocol must be +frozen before a new independent panel on task seeds 6--8, and that panel must satisfy the original +C2 gain/comparator/lesion conditions plus at least 10/15 positive depth gains and finite losses. + ### C3. Causal calibration is genuinely amortized **Status: passed on 2026-07-22.** The frozen five-seed confirmation retained 112.9% of the K16/e4 diff --git a/results/c2_nodepert_val_v1_tent_l2_w8_nodepert_d1_t3_s0.json b/results/c2_nodepert_val_v1_tent_l2_w8_nodepert_d1_t3_s0.json new file mode 100644 index 0000000..2bcfbc8 --- /dev/null +++ b/results/c2_nodepert_val_v1_tent_l2_w8_nodepert_d1_t3_s0.json @@ -0,0 +1 @@ +{"args": {"mode": "nodepert", "dataset": "tentmap", "depth": 1, "width": 8, "act": "relu", "residual": 1, "residual_lesion_fraction": 0.0, "epochs": 80, "batch_size": 256, "train_examples": 0, "val_examples": 2000, "split_seed": 2027, "eval_split": "validation", "task_seed": 3, "task_train_examples": 10000, "task_test_examples": 5000, "task_levels": 2, "task_n_in": 1, "task_classes": 10, "teacher_depth": 8, "teacher_width": 64, "teacher_residual": 1, "eta": 0.03, "eta_A": 0.02, "eta_P": 0.002, "momentum": 0.9, "w_scale": 1.0, "a_scale": 1.0, "feedback_scale": 1.0, 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\ No newline at end of file diff --git a/results/c2_nodepert_val_v1_tent_l2_w8_nodepert_d1_t3_s1.json b/results/c2_nodepert_val_v1_tent_l2_w8_nodepert_d1_t3_s1.json new file mode 100644 index 0000000..e9f8ee2 --- /dev/null +++ b/results/c2_nodepert_val_v1_tent_l2_w8_nodepert_d1_t3_s1.json @@ -0,0 +1 @@ +{"args": {"mode": "nodepert", "dataset": "tentmap", "depth": 1, "width": 8, "act": "relu", "residual": 1, "residual_lesion_fraction": 0.0, "epochs": 80, "batch_size": 256, "train_examples": 0, "val_examples": 2000, "split_seed": 2027, "eval_split": "validation", "task_seed": 3, "task_train_examples": 10000, "task_test_examples": 5000, "task_levels": 2, "task_n_in": 1, "task_classes": 10, "teacher_depth": 8, "teacher_width": 64, "teacher_residual": 1, "eta": 0.03, "eta_A": 0.02, "eta_P": 0.002, "momentum": 0.9, "w_scale": 1.0, "a_scale": 1.0, "feedback_scale": 1.0, "pert_sigma": 0.01, "pert_every": 1, "pert_ndirs": 16, "pert_mode": "layerwise", "use_residual": 0, "raw_scale_control": 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