EXP-3D-STATIC-CARRIER-ATTRIBUTION-V1

STATIC_STATE_STATUS=NOT_ESTABLISHED. The frozen 1000A+600B B-final carrier does not qualify for unseen-scene geometry. Seven checkpoint-unseen scenes were evaluated; full context is worse than anchor in six scenes and ties in one. No Dynamic TTT, controller training, write-rule tuning or new history experiment was run.

Frozen experiment and data

Carrier SHA256 be7b8b6d2ef366cad245732b9ff227802db596da65082ac0e160f24541579e42 exactly matches the previous experiment. 8³ volume, 128 candidates, centered bounds, RGB preprocessing, calibrated metric cameras, normalized-ray depth and 64 renderer samples remain unchanged. Old A/B roles and primary frames 8/9 are retained. All 90 old static rows reproduce exactly for RGB MSE/SSIM, depth AbsRel/RMSE/δ1, opacity and coverage, with identical query-camera hashes.

The prelocked eight candidates came only from the existing research-train and Hypersim official-train pools. Seven qualified. ai_005_003 failed the pre-existing 10 mm native-position p95 check (11.24169 mm); this is a threshold failure, not proof of a camera bug. It was retained and never replaced after selection. Frame quality and A/B feasibility used only metadata, RGB/depth validity and cameras; no model score. Download: 81,591,203 bytes using bounded HTTP ranges, no full archive.

Checkpoint-unseen IDs: ai_004_001, ai_006_001, ai_007_002, ai_008_001, ai_009_001, ai_010_001, ai_011_001. Training IDs are ai_001_001, ai_002_001, ai_003_001, verified against manifest, logs and checkpoint provenance. Official volume/archive/asset identities are distinct; supplier reuse across distinct assets cannot be independently excluded. ai_007_002 has an older previously_observed flag: it is unseen to this checkpoint, not claimed historically unobserved by all researchers. No protected/test or official validation split was opened. See scene_split.json, unseen_independence_audit.json, and the preserved candidate/data/role locks.

Unseen qualification

All rows use scene-equal means; primary full-context error is the predeclared average of A and B. Positive gain means anchor error minus full-context error. Bootstrap resamples seven paired scenes, 10,000 draws, seed 20260927, percentile 95% CI.

Full-context gain: -0.189671, CI [-0.291047, -0.091291]. Wrong-scene damage relative to A: -0.036475, CI [-0.158533, 0.080005]. Both qualification requirements fail. Every leave-one-scene-out full-context gain remains negative; this failure is not caused by one scene. All states were sealed before query/GT access and served two queries unchanged. That structural property alone does not establish useful state.

Metrics include every GT-valid depth pixel, including uncovered rays. Coverage means opacity >1e-6, not true visibility. A/B have higher mean opacity than anchor, yet worse depth. Since rendered depth is the unnormalized sum(weights * ray distance), opacity can affect apparent accuracy; no claim of opacity-independent geometry success is made. opacity_diagnostic.json reports separately labeled diagnostic normalization and common-high-opacity checks. They do not replace the frozen primary metric. In that diagnostic, A/B/anchor AbsRel is .805089/.748956/.544680: anchor still wins. The joint high-opacity subset is empty for some scenes; its full-cohort macro is null. Common-visible and volume/support bucket metrics are in bootstrap_results.json and raw records, including empty-mask counts. Missing scenes are not silently removed from regional macros. Per-object metrics are unavailable because object segmentation was not acquired; per-support-bucket metrics are supplied as requested.

Why did ai_003_001 fail?

1. Why is query RGB black? All published source JPEGs 0–15 are already exactly zero RGB. PIL/OpenCV agree; resizing and model/evaluator tensors preserve them. ROOT_CAUSE=SOURCE_FRAME_IS_BLACK. Official metadata includes these images as train, with no exclusion and a non-BAD camera. Why the published source is black—render failure, transition, or another source issue—remains UNKNOWN. It is not evidence of a decoder/range/preprocessing bug. ai003 was never removed.
2. Why no A two-view support? Projection of all 128 candidates through cameras 0/1/2 gives 13 single-view candidates and zero candidates with >=2 supporting views. B has 3 such candidates. The full coordinate/projection/validity records are in support_audit_ai003.json. A+stream has 4, but that does not repair static A.
3. Why A equals anchor? The current fusion/completion path needs two-view support. With none, both materialize the same prior branch and give identical predictions. This is expected behavior of the frozen carrier, not a newly discovered code bug. Primary GT surfaces are 100% inside its volume; A-supported surface neighborhoods are 0%. Inside the box does not imply observed support.

Unseen geometry also reveals coverage failure: ai009 primary GT surfaces are 0% inside the volume on both queries; ai007 query 15 is only 0.06% inside, and ai008 queries are 30.2% and 6.67% inside. Geometry feasibility of the context does not ensure query coverage. These scenes stay in the locked primary cohort; no post-score filtering or volume expansion is applied. Thus support design is an evidenced bottleneck; representation and training contributions are not isolated.

Camera/depth audit found no convention/unit/resize bug: old projection roundtrip max 1.17e-11 pixels, ray-depth roundtrip max 3.55e-15 m; native GT positions satisfy the locked check. PREVIOUS_DEPTH_METRIC_INVALID=false. Depth is metric ray distance; opacity normalization is a separate readout question. All old scenes received the same audit. See depth_definition.md, geometry_audit.json, volume_coverage.json.

Residual readout and state-use attribution

The existing 308-parameter 14→16→4 residual head uses exactly the same frozen states as R-Fixed. Adam lr=.001, clip=1; only original three training scenes, query frames 12–14 for fitting and 15 for train-side validation, A/B contexts. The initial 3,000-step pilot was still improving, so a documented budget-only amendment extended fresh same-seed training to 30,000 before any residual primary/unseen evaluation. First 3,000 logs match exactly. The fixed validation rule selected step 7,500. No unseen/8–9 label was used for residual training or selection.

Selected train-side validation objective: .303350 versus .375932 initially; terminal 30,000-step validation worsened to .423491. The final 3,000-step training window still improved 1.45%; global convergence is not established. This control is trained and validated, but its limited capacity/training does not rule out every possible readout. Head linear operations: 11,796,480 FLOPs per 128×160 query (multiply+add=2; excludes renderer/activation). Measured fixed 5.21 ms, residual 10.81 ms including state safety checks; head alone .0478 ms. See training curves, audit and timing definitions.

Average A/B: Fixed 0.797229, Residual 0.852789; Residual−Fixed 0.055560, CI [-0.178267, 0.387806]. B improves by 0.082708, while A worsens by 0.193828; selecting B as a new primary method after seeing this would be invalid. Residual A replacement/zero-state do not reliably hurt performance and zero-state even has lower macro AbsRel. Query-conditioned prior or bypass remains plausible; this does not establish useful scene-specific information or a general fixed-decoder bottleneck. B-specific bypass was not separately tested.

Context-feature spatial shuffle, feature-channel reversal and zero-evidence state masking are diagnostic only. Prediction RMS/hash changes, not just latent changes, are saved. Channel reversal leaves fixed typed-field rendering unchanged by design; its residual effect must not be read as a fixed-renderer bug. No state was mutated.

Train-query versus heldout-query gap

A AbsRel: 0.331528 on previously used 12–15 versus 2.213562 on 8–9. The macro gap 1.882034 is 94.59% contributed by ai003. Both ai003 cohorts are black and have zero A support, so blackness alone cannot explain their difference. Heldout versus training nearest-A view angle is 60.38° versus 20.74°, and approximate common visibility is .3305 versus .8700. ai002 heldout volume coverage is .5228 versus .8846, with supported surface neighborhood .1097 versus .2170. This supports view/support and coverage limitations; it does not isolate training memorization. ai001 has a remaining unexplained gap. Full per-frame baseline, view, depth, validity and overlap statistics are in train_heldout_gap_diagnosis.json and its narrative report.

Reproduction, tests and limitations

Run commands.sh summary from the repository root for raw-only statistics, or commands.sh full with a fresh output directory for downloads, training, inference, scripted audits and tests. The full mode ends with regenerated statistics; it does not recreate the manually collected official-source-validity notes or the curated environment/test/provenance report. Those original records are archived separately; new repetitions must record their own environment and execution results. Archived locks/raw reproduce all summaries via scripts/report_static_attribution.py. Tests: 610 passed, 1 skipped. The single historical V5 checkpoint test is skipped because that optional checkpoint is unavailable. New tests cover RGB preservation, camera/depth roundtrips, scene/query isolation, residual immutability, unchanged replacement camera, scene bootstrap and score-independent quality/volume auditing. Full log: tests.log.

For a clean checkout containing only the published report, recompute statistics with:

.venv/bin/python scripts/report_static_attribution.py --raw-only   --output docs/experiments/EXP-3D-STATIC-CARRIER-ATTRIBUTION-V1   --docs /tmp/static-attribution-recomputed

Raw model results are in static_results.json/CSV; all official statistics can be regenerated without rerunning inference. artifacts/ stores deterministic gzip copies of detailed audit/lock/training records; artifact_manifest.json maps them to output paths with original SHA256. Local full states, media, native geometry arrays and both residual checkpoints remain under outputs. Checkpoints/data are not duplicated into Git docs; downloading requires the published Hypersim source and original carrier artifacts described in the previous training report. commands.sh downloads pinned metadata and invokes bounded data preparation. Source asset identity is audited but not a forensic guarantee against supplier reuse. Seven dev scenes and sparse candidate coverage limit scope. Negative qualification is not a proof that all shared states or all visible signals are impossible. Dynamic TTT remains closed for this carrier.