| # Execution audit — Infinite Players 3.0.0 |
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| Date: 18 October 2026. The uploaded source was copied to `verification_run` before any execution. The original `source/Infinite_Players_Final` directory was not edited. Fresh output is in `verification_output`. |
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| ## Validation outcome |
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| All requested numerical checks completed successfully. This is fresh local source execution, not external replication, peer review, real-player validation, or proof-assistant verification. |
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| | Check | Fresh result | |
| |---|---:| |
| | Supplied research file manifest | 82/82 files verified | |
| | New unit tests | 50/50 passed | |
| | New deterministic reference assets | 32/32 SHA-256 hashes matched | |
| | Preserved v2 unit tests | 22/22 passed | |
| | Preserved v2 reference assets | 14/14 SHA-256 hashes matched | |
| | Standalone `RUN_DEMO.py` | Passed | |
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| Exact command from `verification_run`: |
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| ```bash |
| python VERIFY_RELEASE.py |
| python REPRODUCE.py --output /workspace/scratch/2533268896e4/verification_output --legacy |
| python RUN_DEMO.py --output /workspace/scratch/2533268896e4/standalone_demo_output |
| ``` |
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| ## Environment and dependency qualification |
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| Fresh runtime: Python 3.12.14; NumPy 2.3.5; SciPy 1.17.0; Matplotlib 3.10.8; Linux x86-64/glibc 2.39; single-threaded numerical libraries; no accelerator. The supplied environment records Python 3.13.5/glibc 2.41. All 32 deterministic assets match despite that difference. The generated upstream reproduction report uses a hard-coded “same-environment” description; this audit records the actual environment. |
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| **Recommended Hugging Face quickstart: Python 3.11+ for `requirements-locked.txt`.** Both locked NumPy and SciPy declare Python >=3.11. The broad Python >=3.10 declaration can use older compatible dependencies from `requirements.txt`, but Python 3.10 was not exercised here. Dependencies were already installed; no fresh pip installation was needed or claimed. |
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| ## Primary finite arena |
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| Mean normalized discounted value is averaged over six initial ground states and 16 arena seeds. Semantic total variation is exact enumeration of the six-step semantic law from ground state zero. The paired intervals are descriptive t intervals over 16 seeds; they are not evidence for a larger real-game population. |
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| | Controller | Mean normalized value | Gain versus source | Paired 95% gain interval | Mean six-step semantic TV | |
| |---|---:|---:|---|---:| |
| | source_anchor | 0.511568389 | 0.000000000 | [0.000000000, 0.000000000] | 0 | |
| | path_shield_LP | 0.642582819 | 0.131014430 | [0.116648674, 0.145380186] | 4.30286e-16 | |
| | conditional_class_only | 0.731438499 | 0.219870110 | [0.209962867, 0.229777353] | 0.483658 | |
| | unconstrained | 0.823908873 | 0.312340484 | [0.293457438, 0.331223529] | 0.856473 | |
| | nullspace_24_updates | 0.605199312 | 0.093630923 | [0.083067074, 0.104194772] | 2.30614e-14 | |
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| The constrained LP optimum improves value within the protected semantic-law fiber. Weaker constraints allow higher return and substantially more semantic drift. The 24 projected-Q updates remain below the constrained LP ceiling. |
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| ## Sequence inference |
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| | Events per record | Records | Operator-tree accuracy | Full-HMM replay accuracy | Paired-product accuracy | |
| |---|---:|---:|---:|---:| |
| | 8 | 1000 | 93.6% | 93.6% | 91.0% | |
| | 16 | 1000 | 97.5% | 97.5% | 96.2% | |
| | 32 | 1000 | 99.9% | 99.9% | 99.7% | |
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| The maximum posterior difference between tree and independently implemented full replay is 5.3290705182007514e-15 across 3,000 records. The tree changes update cost; it does not improve on the same exact Bayesian inference model. |
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| ## Continuous Markov lag study |
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| | Lag | Synthetic trajectories | Accuracy | Wilson 95% interval | |
| |---|---:|---:|---| |
| | 0 | 16000 | 100.00000% | [99.97600%, 100.00000%] | |
| | 1 | 16000 | 99.57500% | [99.46161%, 99.66459%] | |
| | 2 | 16000 | 96.46250% | [96.16493%, 96.73776%] | |
| | 3 | 16000 | 89.07500% | [88.58222%, 89.54902%] | |
| | 4 | 16000 | 80.01875% | [79.39200%, 80.63109%] | |
| | 6 | 16000 | 66.08125% | [65.34389%, 66.81089%] | |
| | 9 | 16000 | 56.13750% | [55.36723%, 56.90482%] | |
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| Each trajectory has 64 events. These 112,000 trajectories use a fixed synthetic known-model design. |
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| ## Retrospective query memory |
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| | Retained prefix bits / 256 | Trials | Observed accuracy | Analytic accuracy | |
| |---|---:|---:|---:| |
| | 0 | 16000 | 49.81250% | 50.00000% | |
| | 8 | 16000 | 51.39375% | 51.56250% | |
| | 32 | 16000 | 56.09375% | 56.25000% | |
| | 64 | 16000 | 62.56875% | 62.50000% | |
| | 128 | 16000 | 75.10625% | 75.00000% | |
| | 256 | 16000 | 100.00000% | 100.00000% | |
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| ## Local evidence replacement |
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| | Archived events | Tree compositions per replacement | Replay steps per replacement | Tree array bytes | Raw two-sign bytes | Largest centered logscore discrepancy | |
| |---|---:|---:|---:|---:|---:| |
| | 256 | 8 | 256 | 81,920 | 512 | 3.97904e-13 | |
| | 2048 | 11 | 2048 | 655,360 | 4,096 | 6.82121e-12 | |
| | 16384 | 14 | 16384 | 5,242,880 | 32,768 | 6.18456e-11 | |
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| The work units differ (batched matrix composition versus forward-vector step); the arithmetic also depends on four hypotheses and two hidden states. Tree arrays use 160 times the two-sign raw payload on these fixtures, excluding Python object overhead. These are update accelerators, not compression gains. |
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| | Archived events | Tree build (s) | 16 tree updates (s) | 16 replay updates (s) | Observed replay/tree ratio | |
| |---|---:|---:|---:|---:| |
| | 256 | 0.007675 | 0.006572 | 0.035252 | 5.36 | |
| | 2048 | 0.073419 | 0.010919 | 0.293609 | 26.89 | |
| | 16384 | 0.512575 | 0.011217 | 2.205227 | 196.59 | |
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| **Timing ratios exclude tree construction and are one local diagnostic run.** Do not present them as a general hardware, GPU, or commercial-game benchmark. |
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| ## Additional controls and audits |
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| - Positive-circuit approximation: 1,000 trials, zero bound violations; maximum posterior TV 0.010810353214991988; maximum observed TV/bound ratio 0.2204985843214893. |
| - Calibration fixture: protected reward goes from 0.4 before calibration to 0.85 in world 0; the negative world control remains 0.4. The no-headroom arena changes value by at most 6.661338147750939e-15. |
| - Lifetime promotion study: 10,000 histories per condition, 40 versions, 256 audit sessions/version, lifetime budget 0.05. Zero-gain condition: 60/10,000 histories accepted any promotion (0.6%). Positive gain 0.3 condition: 10,000/10,000 accepted at least one promotion; mean promotions 29.5762. |
| - Exact arithmetic audits pass for 16 lag cases/2,550 atoms, 16 hidden operator paths, 276 deterministic encoders, 2,046 semantic words, and 72 persistent-composition cases. These remain internally authored audits, not external mathematical review. |
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| ## Standalone demo |
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| Normalized source value: 0.5378491294755662. Descendant value: 0.6666691635919242. Six-step semantic TV: 3.2289488872397007e-16. Maximum signature residual: 3.885780586188048e-16. One 1,000-step execution trace has reward mean 0.676; that single trace is not a separate population performance estimate. |
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| ## Issues and unexecuted checks |
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| No runtime failure or supplied deterministic reference mismatch was found. The Python requirement distinction above should be made explicit in the Hugging Face release. Native Windows execution, TeX/PDF rebuild, real-player testing, learned world models, commercial-engine execution, local-LLM integration, and GPU benchmarking were not performed. The title’s beyond-human-expertise aspiration must not be represented as an achieved evaluation result. |
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| Machine-readable command outcomes, environment, full metrics, and exact audits are in `execution_audit.json`. |
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