diff options
| -rw-r--r-- | ORAL_A_V3.md | 26 | ||||
| -rw-r--r-- | README.md | 4 | ||||
| -rw-r--r-- | RESULTS.md | 24 | ||||
| -rw-r--r-- | REVIEW_SCORECARD.md | 2 | ||||
| -rw-r--r-- | ROADMAP.md | 10 | ||||
| -rw-r--r-- | experiments/analyze_oral_a_failure.py | 33 | ||||
| -rw-r--r-- | results/oral_a_failure_diagnosis.json | 19 | ||||
| -rw-r--r-- | results/oral_a_v3_calibration/channel_subspace_etaA0.01.json | 393 | ||||
| -rw-r--r-- | results/oral_a_v3_calibration/vectorizer_subspace_etaA0.01.json | 393 | ||||
| -rw-r--r-- | results/oral_a_v3_calibration/vectorizer_subspace_etaA0.1.json | 393 | ||||
| -rw-r--r-- | results/oral_a_v3_calibration/vectorizer_subspace_etaA1.0.json | 393 | ||||
| -rw-r--r-- | results/oral_a_v3_calibration_gate.json | 115 |
12 files changed, 1795 insertions, 10 deletions
diff --git a/ORAL_A_V3.md b/ORAL_A_V3.md index b668ea0..2ffc19d 100644 --- a/ORAL_A_V3.md +++ b/ORAL_A_V3.md @@ -84,3 +84,29 @@ Pareto point. V3-0/V3-1 cannot raise the strict ICLR score. A complete V3-2 pass moves the forecast from 5 to 6; oral-level scale requires V3-3. +## Audited outcome (2026-07-22) + +All four V3-1 records were finite and shared clean source commit `612196a`. +The frozen selector chose `eta_A=0.01`. The matched alignment endpoints were: + +| estimator | early-third alignment | all-layer alignment | +|:--|--:|--:| +| V2 channel subspace | 0.007209 | 0.052740 | +| V3 vectorizer subspace | 0.007139 | 0.062579 | + +V3 improves the all-layer mean by `0.009839`, mostly through middle and late +blocks, but does not improve the binding early-layer endpoint. It fails the +`0.01` absolute early threshold, the `+0.01` matched improvement, and the 60% +family-oracle threshold; only the all-layer check passes. Rates `0.1` and `1.0` +reduce early alignment further to `0.004804` and `0.002234`. + +The calibration MSE/power values are not compared across V2 and V3 because +their metric spaces differ. The valid common endpoint is exact hidden-gradient +alignment. V3-1 status is **failed**; V3-2 was not launched, no test endpoint +or confirmation seed was touched, and the reviewer score remains 5/10. + +The negative result rules out coefficient-then-regress variance as the sole +early-layer bottleneck. Direct matrix estimation works exactly and lowers +matched synthetic variance, yet the real early signal remains unchanged. The +next mechanism must add informative hierarchical/high-level context or remove +the remaining cross-layer/parameter-space noise, not repeat this rate grid. @@ -50,6 +50,10 @@ and scaling behavior. See `NOVELTY.md` for the exact prior-art boundary. `0.0011/0.0117` to `0.0072/0.0527` at matched query count. It nevertheless missed the frozen `0.01` early-layer and improvement gates, so no v2 full ResNet run was launched. +- Direct vectorizer-space perturbation then passed its exact mechanics and + synthetic variance audit, but V3-1 again failed: early alignment was + `0.00714` versus matched V2's `0.00721`, although all-layer alignment rose to + `0.06258`. V3 full training therefore remained closed. - Native author-code fidelity is complete. BurstCCN reaches `80.10%` at its validation-selected epoch versus published `82.97 +/- 0.21%`; Dual Prop reaches `92.46%` versus published `92.41 +/- 0.07%`. Their audited walls are @@ -683,6 +683,30 @@ post-failure oracle diagnostics, not trainable-method results. They rule out a naive basis expansion and point toward both better causal sample efficiency and a more informative hierarchical/high-level feedback context. +V3 tests whether the remaining regression gap comes specifically from +estimating per-example coefficients before fitting A/G. It perturbs random A/G +matrices directly, so each induced hidden intervention already contains the +output-error context and the antithetic scalar estimates the exact matrix +statistic needed by the vectorizer delta rule. Mechanics are strong: at batch +128 the frozen synthetic one-query MSE is `0.03381x` the coefficient estimator, +and exact no-BN/BN JVP errors are below `2e-10`. + +That gain does not transfer to early ResNet credit. All four frozen-forward +records are finite and the selected rate is again `eta_A=0.01`: + +| estimator | early-third alignment | all-layer alignment | +|:--|--:|--:| +| channel-subspace V2 reference | 0.007209 | 0.052740 | +| vectorizer-subspace V3 | 0.007139 | 0.062579 | + +The common all-layer endpoint improves, but the early endpoint is unchanged +and misses three frozen advancement checks. Higher rates are worse. Thus the +coefficient-regression stage was not the dominant early-layer limitation under +the real causal signal. V3-2 is not launched. The result redirects development +toward informative hierarchical/high-level feedback context or a mechanism +that addresses remaining cross-layer/parameter-space noise; it does not +justify another rate or warmup sweep. + ## How to run `experiments/run.py --mode {bp,fa,dfa,sdil} --dataset {mnist,fmnist,cifar10} --depth D --residual {0,1} --act {tanh,gelu,silu,relu}` Batteries: `experiments/run_v2.sh <ds> "<depths>" <res> <act> "<seeds>" <ep> <pfx>`. diff --git a/REVIEW_SCORECARD.md b/REVIEW_SCORECARD.md index 3bbb0c8..59fe99e 100644 --- a/REVIEW_SCORECARD.md +++ b/REVIEW_SCORECARD.md @@ -59,6 +59,7 @@ Every formal result report records: | Oral-A A1/A2 | 5 | BP reached 91.62%; short channel-gated SDIL reached 41.98% versus tuned DFA at 37.16% | Development screening alone cannot raise the score | | Oral-A A3 fails | 5 | Full ResNet-20 SDIL became nonfinite at epoch 89 and ended at 10%; DFA ended finite at 33.06% | Standard-scale and oral-A claims are closed; A4 remains untouched | | Oral-A-v2 causal capture fails | 5 | Structured perturbation improves early/all-layer alignment 6.5x/4.5x but misses the frozen early gate | Mechanism diagnosis sharpens; no full standard-scale endpoint opens | +| Oral-A-v3 causal capture fails | 5 | Direct A/G perturbation lowers synthetic estimator variance but leaves real early alignment at 0.0071 | Coefficient regression is not the dominant early bottleneck; full scale remains closed | | Oral-A A4 | not opened | The prerequisite A3 gate failed | No oral-A confirmation claim is available | These are conditional reviewer forecasts, not promised scores. A failed stage leaves its negative @@ -75,6 +76,7 @@ retroactively reopened by success on standard vision benchmarks. | 2026-07-22 / native C4 | BurstCCN and Dual Prop author-code records pass the strict audit; Dual Prop reproduces 92.46% test in 23119.8 s | 5 → 5 | Closes a baseline-fidelity objection and confirms a strong expensive comparator, but does not repair SDIL's failed A3 evidence | | 2026-07-22 / Oral-A-v2 V2-1 | Six clean frozen-forward records: structured calibration raises early/all-layer alignment to 0.0072/0.0527 but fails two frozen advancement checks | 5 → 5 | Confirms representable-subspace variance was real, while showing that early-layer causal credit remains below the standard-depth gate; V2-2 and confirmation stay closed | | 2026-07-22 / post-failure representation oracle | Current family has a 0.0240 cross-validated early-alignment ceiling on the fixed probe versus 0.0072 learned; unconstrained basis coefficients reach 0.0549 | 5 → 5 | Localizes both a causal-regression gap and an output-error-only capacity gap, but an oracle audit supplies no task-performance evidence | +| 2026-07-22 / Oral-A-v3 V3-1 | Four clean frozen-forward records: vectorizer-space estimation raises all-layer alignment to 0.0626 but leaves early alignment at 0.0071 and fails three advancement checks | 5 → 5 | Exact lower-variance mechanics do not repair early credit; no full ResNet or confirmation evidence is opened | Future rows are appended only after an audited frozen stage. A score staying flat is informative: engineering, theory exposition, or visualization may make the paper more defensible without @@ -373,6 +373,16 @@ reaches `0.00721`. Naive local-average/channel-context fields fall slightly to sample efficiency and feedback context; simply lowering `eta_A`, adding the tested fields, or reopening V2-2 is not justified. +**Vectorizer-space V3 status: causal-capture gate failed.** Directly estimating +the A/G matrix target passed exact mechanics and reduced matched synthetic +one-query MSE to `0.03381x`, but its selected real early alignment was +`0.007139` versus the matched V2 reference's `0.007209`. All-layer alignment +rose from `0.052740` to `0.062579`; three early/oracle checks failed and only +the all-layer check passed. V3 full training was not launched, and test access +remains sealed. This closes learning-rate tuning of the same output-error-only +channel-gated family; a further branch must make a substantive feedback-context +or cross-layer-noise change. + Prepare convolutional local-update primitives and ResNet-20/32/56 protocols early. Queue frozen runs opportunistically on authorized idle GPUs. Because BurstCCN already reports CIFAR-10 and ImageNet scaling, dataset scale alone is not novel. The oral-level target is a memorable joint diff --git a/experiments/analyze_oral_a_failure.py b/experiments/analyze_oral_a_failure.py index ce2e2b0..03549b8 100644 --- a/experiments/analyze_oral_a_failure.py +++ b/experiments/analyze_oral_a_failure.py @@ -29,6 +29,8 @@ def main(): "--representation", default="results/oral_a_representation_diagnosis.json") parser.add_argument( + "--v3_gate", default="results/oral_a_v3_calibration_gate.json") + parser.add_argument( "--out", default="results/oral_a_failure_diagnosis.json") args = parser.parse_args() @@ -37,6 +39,7 @@ def main(): gate = load(args.gate) v2_gate = load(args.v2_gate) representation = load(args.representation) + v3_gate = load(args.v3_gate) expected = { "mode": "sdil", "depth": 20, "epochs": 200, "vectorizer_mode": "channel_gated", "a_scale": 1.0, @@ -54,6 +57,9 @@ def main(): raise ValueError("expected a failed v2 gate with untouched confirmation") if representation["probe"]["test_examples_touched"] != 0: raise ValueError("representation diagnosis touched test examples") + if (v3_gate["status"] != "failed" + or v3_gate["confirmation_test_seeds_touched"]): + raise ValueError("expected a failed v3 gate with untouched confirmation") calibration_rows = [] first_nonfinite_epoch = None @@ -97,10 +103,11 @@ def main(): unit = v2_gate["selected"]["unit_targets"] structured = v2_gate["selected"]["channel_subspace"] output = { - "protocol": "oral_a_A3_failure_diagnosis_v2", + "protocol": "oral_a_A3_failure_diagnosis_v3", "source_paths": { "full": args.full, "short": args.short, "gate": args.gate, "v2_gate": args.v2_gate, "representation": args.representation, + "v3_gate": args.v3_gate, }, "source_commits": { "full": full["provenance"]["git_commit"], @@ -154,6 +161,19 @@ def main(): representation["cosine"]["local_context_cv"] ["early_third_mean"]), }, + "post_failure_v3_refinement": { + "vectorizer_subspace_early_third_alignment": ( + v3_gate["selected_v3"]["metrics"]["early_third_alignment"]), + "vectorizer_subspace_all_layer_alignment": ( + v3_gate["selected_v3"]["metrics"]["all_layer_alignment"]), + "matched_v2_early_third_alignment": ( + v3_gate["matched_v2_reference"]["metrics"] + ["early_third_alignment"]), + "matched_v2_all_layer_alignment": ( + v3_gate["matched_v2_reference"]["metrics"] + ["all_layer_alignment"]), + "vectorizer_subspace_gate_status": v3_gate["status"], + }, "diagnosis": { "classification": ( "early_credit_limited_by_estimator_efficiency_and_feedback_capacity"), @@ -164,18 +184,21 @@ def main(): "matched frozen-forward unit targets later yield only 0.0011 early-layer alignment", "structured learning reaches 0.0072 versus a 0.0240 cross-validated family oracle", "unconstrained coefficients raise the same spatial basis oracle only to 0.0549", + "direct A/G estimation leaves early alignment unchanged at 0.0071", ], "interpretation_limit": ( "instantaneous target metrics alone do not prove zero conditional " "learning; structured v2 has low instantaneous cosine yet positive " "exact-gradient alignment"), "next_test_outcome": ( - "representable-subspace perturbation improved exact alignment but " - "failed the frozen early-layer causal-capture gate"), + "representable-subspace perturbation improved alignment but failed; " + "direct vectorizer-space estimation then improved only middle/late " + "layers and also failed the frozen early-layer gate"), "next_design_constraint": ( "do not add the tested naive local-average/channel-mean bases; " - "improve causal sample efficiency and use a more informative " - "hierarchical or high-level contextual feedback signal"), + "do not repeat the closed A/G rate grid; use a more informative " + "hierarchical or high-level contextual feedback signal and " + "audit remaining cross-layer noise"), }, } os.makedirs(os.path.dirname(os.path.abspath(args.out)), exist_ok=True) diff --git a/results/oral_a_failure_diagnosis.json b/results/oral_a_failure_diagnosis.json index 1b142df..b26d81e 100644 --- a/results/oral_a_failure_diagnosis.json +++ b/results/oral_a_failure_diagnosis.json @@ -51,11 +51,12 @@ "target power grows by orders of magnitude before nonfiniteness", "matched frozen-forward unit targets later yield only 0.0011 early-layer alignment", "structured learning reaches 0.0072 versus a 0.0240 cross-validated family oracle", - "unconstrained coefficients raise the same spatial basis oracle only to 0.0549" + "unconstrained coefficients raise the same spatial basis oracle only to 0.0549", + "direct A/G estimation leaves early alignment unchanged at 0.0071" ], "interpretation_limit": "instantaneous target metrics alone do not prove zero conditional learning; structured v2 has low instantaneous cosine yet positive exact-gradient alignment", - "next_design_constraint": "do not add the tested naive local-average/channel-mean bases; improve causal sample efficiency and use a more informative hierarchical or high-level contextual feedback signal", - "next_test_outcome": "representable-subspace perturbation improved exact alignment but failed the frozen early-layer causal-capture gate" + "next_design_constraint": "do not add the tested naive local-average/channel-mean bases; do not repeat the closed A/G rate grid; use a more informative hierarchical or high-level contextual feedback signal and audit remaining cross-layer noise", + "next_test_outcome": "representable-subspace perturbation improved alignment but failed; direct vectorizer-space estimation then improved only middle/late layers and also failed the frozen early-layer gate" }, "frozen_outcome": { "confirmation_test_seeds_touched": false, @@ -88,7 +89,14 @@ "unit_target_all_layer_alignment": 0.011664319529173602, "unit_target_early_third_alignment": 0.0011051012067279469 }, - "protocol": "oral_a_A3_failure_diagnosis_v2", + "post_failure_v3_refinement": { + "matched_v2_all_layer_alignment": 0.052739956808325494, + "matched_v2_early_third_alignment": 0.007209058462952574, + "vectorizer_subspace_all_layer_alignment": 0.0625791079364717, + "vectorizer_subspace_early_third_alignment": 0.007139206010227402, + "vectorizer_subspace_gate_status": "failed" + }, + "protocol": "oral_a_A3_failure_diagnosis_v3", "short_screen_contrast": { "accuracy": 0.4198, "early_third_alignment": 0.08889565182228883, @@ -104,6 +112,7 @@ "gate": "results/oral_a_full_gate.json", "representation": "results/oral_a_representation_diagnosis.json", "short": "results/oral_a_short/sdil_channel_gated_lr0.03.json", - "v2_gate": "results/oral_a_v2_calibration_gate.json" + "v2_gate": "results/oral_a_v2_calibration_gate.json", + "v3_gate": "results/oral_a_v3_calibration_gate.json" } } diff --git a/results/oral_a_v3_calibration/channel_subspace_etaA0.01.json b/results/oral_a_v3_calibration/channel_subspace_etaA0.01.json new file mode 100644 index 0000000..04dc626 --- /dev/null +++ b/results/oral_a_v3_calibration/channel_subspace_etaA0.01.json @@ -0,0 +1,393 @@ +{ + "apical_warmup": { + "first": 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