| # Publication edition 3.0.0-hf.1 |
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| - Added searchable Hub card and expert/AI-agent reading routes. |
| - Added exact-result evidence selectors, citation formats, artifact inventory and synthetic-data dictionary. |
| - Added hidden-token Windows launcher, account checks, byte-matching retries, parent-commit guard and anonymous public verification. |
| - Preserved all scientific evidence and source; no new rendering experiment or performance claim. |
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| # v3.0.0 - Certified Innovation Rendering |
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| Added six scoped results, geometric validity, residual-support elimination, sixteen tests and two new held-out protocols. Retained v2 mathematics, code, weights and adverse evidence. Updated standalone manuscript, API, agent metadata and publication launchers. No new model training, GPU execution or external publication. |
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| # Changes in 2.0.0 |
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| Version 1 supplied a broad world-model specification, ten scoped mathematical propositions, six synthetic witnesses and fourteen checks. Version 2 adds a working physical estimation loop rather than relabeling the original proposal as finished. |
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| - Add a frozen exact-integral correction API, with tests exposing same-sample refitting, wrong-integral bias and clipping bias. |
| - Add canonical visibility memory, numeric checkpoint persistence, namespace checks, trust epochs and observation-triggered revision. |
| - Train and package one small visibility prior, with explicit train/validation/test scene splits and a model card. |
| - Execute a direct-light shadow benchmark on eight held-out scenes; retain active-sampling and stale-state failures. |
| - Execute an independent eight-scene follow-up testing trust revocation without an oracle event flag. |
| - Derive five additional scoped results: conditional correction, residual-risk information value, estimator equivalence, detection-delay limits and a fixed-budget concentration bound. |
| - Package an installable NumPy API, physical demo, new tests, exact replay evidence, publication helper, full manuscript and original v1 archive. |
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| There is no full SR/RR/FG implementation, GPU backend, game integration, broad calibration result or production speedup. Scientific maturity is estimated at 65%; the project remains classified as meaningful, not major or transformative. |
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