Datasets:
Tasks:
Other
Formats:
json
Languages:
English
Size:
1K - 10K
Tags:
photonic-computing
recursive-self-improvement
statistical-experiments
blackwell-order
information-preservation
gaussian-readout
| # Verification dataset | |
| The `channel_comparisons` configuration contains **1,247 saved synthetic channel comparisons**, exposed as a single `validation` split. This split name identifies mathematical verification records, not a machine-learning train/validation partition. Cases include endpoints and curated degeneracies as well as seeded random examples; no claim of independent identically distributed sampling is made. | |
| | Family | Rows | Original report | | |
| |---|---:|---| | |
| | `deficiency` | 556 | `research/data/deficiency.json` | | |
| | `purification` | 375 | `research/data/purification.json` | | |
| | `orbit_audit` | 96 | `research/data/orbit_audit.json` | | |
| | `catalogue` | 220 | `research/data/catalogue.json` | | |
| | Field | Type | Meaning | | |
| |---|---|---| | |
| | `case_id` | string | Stable family plus zero-padded source record index. | | |
| | `family` | string | One of the four modules above. | | |
| | `category` | string | Source category, or an explicit family-level fallback. | | |
| | `q` | integer | Number of hidden hypotheses in the compared experiment. | | |
| | `analytic_deficiency` | float | Formula prediction for directed worst-state TV deficiency. | | |
| | `unrestricted_lp_deficiency` | float | Independently optimized unrestricted stochastic-kernel LP value. | | |
| | `absolute_error` | float | Absolute difference between those two values. | | |
| | `constructed_kernel_tv` | float or null | Attained TV for the saved constructive kernel when that report stores it; null is not zero. | | |
| | `comparison_tolerance` | float | Common export check threshold, `1e-9`; individual modules retain their original test tolerances. | | |
| | `passed` | boolean | Formula/LP discrepancy and any saved kernel discrepancy meet this export tolerance. | | |
| | `source_report` | string | Relative path to the detailed original JSON. | | |
| | `source_record_index` | integer | Zero-based offset inside that report's `records` array. | | |
| | `parameters_json` | string | Original non-result fields, serialized as JSON to keep one stable tabular schema. | | |
| `verification/summary.json` gives the counts and largest discrepancy. All detailed reports, including separate Gaussian, finite-field and physical-flow checks, remain in `research/data/`. They have different schemas and are intentionally excluded from the default dataset configuration. | |
| ## Regenerate | |
| After intentionally rerunning the research suite: | |
| ```bash | |
| python scripts/build_verification_dataset.py | |
| python scripts/update_manifest.py | |
| ``` | |
| The normalization performs no new experiments and does not alter the scientific results. Every normalized row links back to the saved source record. Floating-point agreement supports an implementation check, not a proof, an outward-rounded interval guarantee or a physical measurement. | |