{ "schema_version": "1.0.0", "research_version": "3.0.0", "release_revision": "HF.1", "format": "UTF-8 JSON Lines", "split_semantics": "test is an organizational label, not a held-out ML benchmark", "nested_json_columns": "Columns ending _json hold strict JSON strings; decode explicitly.", "configurations": { "tomography_exact": { "path": "data/tomography_exact.jsonl", "split": "test", "rows": 33, "row_unit": "one visible-spectrum rational reconstruction fixture", "fields": { "record_id": "string", "m": "integer", "t": "array[string]", "r": "array[string]", "probe_z": "array[string]", "P_ascending": "array[string]", "Q_ascending": "array[string]", "rank": "integer", "probe_response_exact": "array[string]", "evidence_kind": "string", "source_file": "string", "source_pointer": "string" }, "source_files": [ { "path": "research/v3/results/tomography_check.json", "sha256": "e984dea79e1bbf4d76d5de3a099ab752e10ed129fdc9b77f50624c0a1de3798d" }, { "path": "research/v3/tomography_check.py", "sha256": "1158b2f5c68a4b6d66b31a9c4d77ffdf5ab0c24b00bfba277c5677daa578ca0a" } ], "scope_note": "Original fields preserved. rank is the interpolation-system rank, not rank(A). Derived responses are exact labels, not measured observations." }, "suite_metrics": { "path": "data/suite_metrics.jsonl", "split": "test", "rows": 50, "row_unit": "one named aggregate metric or scope note", "fields": { "record_id": "string", "suite": "string", "metric_path": "string", "value_type": "string", "value_json": "string", "evidence_kind": "string", "source_file": "string" }, "source_files": [ { "path": "research/v3/results/spectral_order_check.json", "sha256": "629967c5800cc717cc2bb00a316f2f91d385d7207f6b0adbe3e2570a8a6bdac9" }, { "path": "research/v3/results/tomography_check.json", "sha256": "e984dea79e1bbf4d76d5de3a099ab752e10ed129fdc9b77f50624c0a1de3798d" }, { "path": "research/v3/results/foundational_geometry_checks.json", "sha256": "2b9f98e3001f242658786c22d72de898275c00c6b71ca169e8dfecc1d9445c30" } ], "scope_note": "Values are encoded as strict JSON strings to keep one uniform field type. Aggregate fixture counts are not new individual fixture rows." }, "joint_loss_log_grid": { "path": "data/joint_loss_log_grid.jsonl", "split": "test", "rows": 9, "row_unit": "one aggregate step-grid/loss-setting result", "fields": { "record_id": "string", "N": "integer", "h": "number", "paths": "integer", "ell": "number", "c": "number", "nu": "number", "mean_endpoint_log_error": "number", "rms_endpoint_log_error": "number", "mean_uniform_log_error": "number", "p95_uniform_log_error": "number", "mean_survival": "number", "mean_memory": "number", "pulse_variance_mean": "number", "expected_pulse_variance": "number", "seed": "integer", "T": "number", "initial_x": "array[number]", "initial_b": "array[number]", "metadata_provenance": "string", "ensemble_dependence": "string", "evidence_kind": "string", "source_file": "string", "source_pointer": "string" }, "source_files": [ { "path": "research/v3/results/joint_loss_checks.json", "sha256": "4f0ac744731af2046a00f8e0fed244d41826a378e282d534a280466874f25c3f" }, { "path": "research/v3/joint_loss_check.py", "sha256": "bfa28b6615ebc9df443c3fe0499bf944881a9a20ed0551fb0262cfc5f9b55308" } ], "scope_note": "Nine summaries of 256-path model simulations. No trajectory-level or experimental data. CLI reproduction must specify --paths 256." }, "joint_loss_survival_grid": { "path": "data/joint_loss_survival_grid.jsonl", "split": "test", "rows": 15, "row_unit": "one grid/loss-setting aggregate of paired path outputs", "fields": { "record_id": "string", "steps": "integer", "h": "number", "seed": "integer", "ell": "number", "nu": "number", "c": "number", "f": "number", "T": "number", "paths": "integer", "initial_x": "array[number]", "initial_b": "array[number]", "mean_survival": "number", "mean_endpoint_target": "number", "paired_mean_discrepancy": "number", "paired_standard_error": "number", "pathwise_mean_absolute_error": "number", "max_endpoint_quadrature_discrepancy_on_60_checks": "number", "mean_resolved_target": "number", "mean_diffusion_first_target": "number", "minimum_survival": "number", "maximum_survival": "number", "loss_scaling": "string", "metadata_provenance": "string", "ensemble_dependence": "string", "evidence_kind": "string", "source_file": "string", "source_pointer": "string", "ell_index": "integer" }, "source_files": [ { "path": "research/v3/results/joint_loss_simulation.json", "sha256": "33cc7f0c40240aec30367c8a60665ff3a91ccbd1de4cee4621ee548655879140" }, { "path": "research/v3/joint_loss_simulation.py", "sha256": "28cce0fc0b9e3db83f27709cfb281c1eb0369d10e33dfcdd6633b09dc408db56" } ], "scope_note": "Fifteen rows reuse three 512-path controller ensembles. Quadrature maxima belong to the grid and are repeated across its five rows. No certified convergence rate.", "grid_shared_source_metrics": [ "max_endpoint_quadrature_discrepancy_on_60_checks", "mean_resolved_target", "mean_diffusion_first_target", "minimum_survival", "maximum_survival" ], "grid_shared_metric_note": "These scalar metrics are repeated in each of the five ell rows for a grid. Survival minima/maxima cover all paths and all five loss settings at that grid, rather than that row alone." }, "tomography_conditioning": { "path": "data/tomography_conditioning.jsonl", "split": "test", "rows": 5, "row_unit": "one floating inverse-system conditioning illustration", "fields": { "record_id": "string", "sigma_gap": "string", "floating_2norm_condition": "number", "evidence_kind": "string", "source_file": "string", "source_pointer": "string" }, "source_files": [ { "path": "research/v3/results/tomography_check.json", "sha256": "e984dea79e1bbf4d76d5de3a099ab752e10ed129fdc9b77f50624c0a1de3798d" } ], "scope_note": "Five illustrations only; no stable noisy inverse theorem or measurement noise data." }, "claims": { "path": "data/claims.jsonl", "split": "test", "rows": 32, "row_unit": "one source-authored scoped mathematical/research claim", "fields": { "record_id": "string", "title": "string", "kind": "string", "statement": "string", "report_reference_json": "string", "assumptions_json": "string", "proof_status_json": "string", "evidence_json": "string", "limits_json": "string", "priority_json": "string", "independent_review_focus_json": "string", "evidence_kind": "string", "source_file": "string" }, "source_files": [ { "path": "CLAIM_LEDGER.json", "sha256": "f52c5527634f595be44815f2996199ca15f766b664082f5863aebd12983f3c18" }, { "path": "research/Avenyra_Geometric_Memory_Physics.tex", "sha256": "a3e9a40c61ad64af5a5e18725ddfa11821b1bc6c9685cd2afad7ba9b432cf689" } ], "scope_note": "AI-assisted claim annotations, not external human review, formal proof objects, measured facts or independent ground-truth labels. Nested fields are preserved as JSON strings; full native ledger is also supplied." }, "research_sections": { "path": "data/research_sections.jsonl", "split": "test", "rows": 26, "row_unit": "one authored LaTeX section from the integrated manuscript", "fields": { "record_id": "string", "section_index": "integer", "title_latex": "string", "text_latex": "string", "labels": "array[string]", "source_line_start": "integer", "source_line_end": "integer", "context_requirement": "string", "evidence_kind": "string", "source_file": "string" }, "source_files": [ { "path": "research/Avenyra_Geometric_Memory_Physics.tex", "sha256": "a3e9a40c61ad64af5a5e18725ddfa11821b1bc6c9685cd2afad7ba9b432cf689" } ], "scope_note": "Retrieval corpus preserving arguments and limits. Contains author text and attributed references, not full external papers. No model-training outcome tested." } }, "total_rows": 170 }