Datasets:
Tasks:
Other
Formats:
parquet
Languages:
English
Size:
1K - 10K
Tags:
machine-learning
active-learning
representation-learning
sparse-fourier-transform
experimental-design
synthetic-data
| # Data dictionary | |
| The tables contain research-run outcomes and provenance, not raw supervised | |
| input–target examples. Parquet is the typed viewer format; files with the same | |
| stems in `tables_jsonl/` are lossless JSONL row mirrors. | |
| ## Configuration views | |
| | View | Backing tables | Rows | | |
| |---|---|---:| | |
| | `evaluation` | Four `*_evaluation.parquet` files | 668 | | |
| | Individual study configurations | Their named evaluation table | 324 / 120 / 192 / 32 | | |
| | `warmup_usages` | `warmup_usages.parquet` | 41 | | |
| | `all_risk_records` | Evaluation tables plus warmup table | 709 | | |
| | `contexts` | `contexts.parquet` | 74 | | |
| Configuration aliases do not create additional observations. Evaluation rows | |
| refer to 148 teacher definitions; warmup usages refer to 21 training teacher | |
| definitions. Repeated studies/methods and shared acquisition prefixes are | |
| intentional. The context table is accounting metadata, not 74 new tasks. | |
| ## Evaluation and warmup record fields | |
| Both record types use the same schema. All integer scalars use signed | |
| 64-bit Parquet storage. Nulls indicate unavailable or inapplicable metadata; | |
| they do not imply a negative result or a zero measurement. | |
| | Field | Type | Meaning | | |
| |---|---|---| | |
| | `record_id` | string | Unique study/suite/phase/world/dimension/seed/method record identifier. | | |
| | `teacher_case_id` | string | Shared dimension-and-seed identifier for the underlying teacher definition. It links repeated method/study cases; it is not an independent-trial ID. | | |
| | `family_context_id` | string | Family grouping constructed from factory context and evaluation mode; this is descriptive metadata, not an inference input. | | |
| | `context_id` | string | Study/suite/world/dimension/method accounting identifier. Evaluation rows join directly to `contexts`; a warmup row points to its primary consuming context, the first charged method. | | |
| | `study` | string | `core`, `probe`, `strong`, or `unified`. | | |
| | `suite` | string | Protocol group such as `main`, `stress`, `shift`, `online`, `confirmation`, or `mixed`. | | |
| | `phase` | string | `evaluation` or `warmup`. | | |
| | `method` | string | Returned algorithm arm or warmup record label. | | |
| | `world_id` | int64 | Saved latent-world index; the unified export uses its ordered world index. Not an independent-example count. | | |
| | `dimension` | int64 | Number of Boolean input coordinates. | | |
| | `teacher_seed` | int64 | Reproducible teacher-factory seed. | | |
| | `teacher_seed_provenance` | string | `saved_record` for evaluation; `factory_formula` for reconstructed warmup labels. | | |
| | `ground_truth_term_count` | int64 | True signal count used for teacher construction and post-decision evaluation only. Not supplied to inference. | | |
| | `noise_sigma` | float64 | Declared raw Gaussian observation-noise scale for the task. | | |
| | `family_mode_evaluation_only` | string | Factory regime, such as affine, changed, random, partial, or core. It is not passed to inference. | | |
| | `risk_epsilon` | float64 | Component-error-norm tolerance, 0.25. Corresponding MSE tolerance is 0.0625. | | |
| | `declared_error_budget` | float64 | Per-record/task-family budget: normally 0.05 for deployed methods; 0.025 for a training audit. Does not establish one joint benchmark guarantee. | | |
| | `status` | string | Recorded branch/certificate outcome; generic records use `generic_certified` or `uncertified`. | | |
| | `certified` | bool | Whether the final output received the protocol's certificate. | | |
| | `component_mse` | float64 | Exact post-decision squared Fourier-coefficient error, equal to high-degree component MSE under uniform Boolean inputs. Excludes nuisance and observation-noise variance. | | |
| | `component_error_norm` | float64 | Square root of component MSE. | | |
| | `exact_support_evaluation_only` | bool | Exact predicted/true support-set match computed after decisions. Coefficients remain approximate. | | |
| | `false_certificate_evaluation_only` | bool | `certified` and true component MSE greater than 0.0625. All exported values are false. | | |
| | `oracle_calls` | int64 | Actual fresh scalar observations for this recorded execution. Evaluation rows exclude separately charged acquisition; warmup rows describe that acquisition usage. | | |
| | `specialized_readout_calls` | int64 | Observations used by the recorded specialized readout stage; zero if none. | | |
| | `fresh_risk_audit_calls` | int64 | Sum of fresh pointwise audit observations in the execution, including a second audit if used. | | |
| | `search_and_screen_calls` | int64 | Remaining observations: generic search, probes, candidate screening and related paid stages. Together with readout/audit counts, sums to `oracle_calls`. | | |
| | `fallback_used` | bool, nullable | Whether a supplied generic fallback stage ran. Null for standalone generic records. | | |
| | `affine_rank` | int64, nullable | Rank reported by a specialized affine readout, where present. Null is not rank zero. | | |
| | `program_digest` | string, nullable | Program or plan identity retained by the exported execution. | | |
| | `model_terms` | int64 | Number of returned feature terms. | | |
| | `model_support_masks` | list of string | Exact decimal Fourier masks, sorted numerically. Strings preserve up to 256 bits. | | |
| | `model_coefficients` | list of float64 | Coefficients in the same order as `model_support_masks`. | | |
| | `reported_wall_seconds` | float64 | Recorded elapsed wall component for the run. Machine/concurrency dependent; not a hardware-independent speed claim. | | |
| | `source_file` | string | Repository-relative original research JSON file. | | |
| | `source_pointer` | string | JSON pointer to the exact original method/training record. | | |
| | `charged_to_methods_json` | string | Serialized list of method names to which a warmup usage is charged. Evaluation rows normally contain their own method name. | | |
| | `charged_to_context_ids_json` | string | Serialized list of all consuming accounting-context IDs; every listed ID exists in `contexts`. | | |
| The `model_support_masks` and `model_coefficients` lists have equal length. | |
| Masks encode a support set by bit positions, not a numeric model score. | |
| Convert them with Python's arbitrary-precision `int(mask)` if arithmetic is | |
| needed; do not use `int64`, `uint64`, or JavaScript numeric storage. | |
| The row normalization is deliberately narrow. Full measured coefficients, | |
| operator ASTs, hidden factory details, solver records, source hashes and | |
| historical execution ledgers remain in the linked research JSON artifacts. | |
| ## Method names | |
| | Study | Methods | | |
| |---|---| | |
| | `core` | `learned_affine`, `generic_R5`, `generic_R6` | | |
| | `probe` | `two_panel_affine_probe`, with `generic_R5` in the alternative-world comparison | | |
| | `strong` | `adaptive_affine`, `active_union` | | |
| | `unified` | `unified_controller`, `generic_R5` | | |
| | Warmup usages | `warmup_R6_1` and `warmup_R6_2`, with their charging relation recorded explicitly | | |
| The same names in different studies/suites do not mean independent teachers | |
| or identically acquired models. Use the source pointer and context to inspect | |
| their precise protocol and noise streams. | |
| ## Context cost table | |
| | Field | Type | Meaning | | |
| |---|---|---| | |
| | `context_id` | string | Unique study/suite/world/dimension/method accounting scope. | | |
| | `study`, `suite`, `method` | string | Protocol and algorithm labels. | | |
| | `world_id`, `dimension` | int64 | Context identifiers. | | |
| | `charged_training_calls` | int64 | Acquisition cost charged **once** to this deployment context. | | |
| | `evaluation_oracle_calls` | int64 | Sum of associated evaluation-row `oracle_calls`. | | |
| | `training_inclusive_calls` | int64 | `charged_training_calls + evaluation_oracle_calls`. | | |
| | `source_file`, `source_pointer` | string | Authoritative accounting provenance. | | |
| | `training_accounting` | string | Explicit instruction to charge once per context, never per evaluation row. | | |
| For an evaluation record, join its `context_id` directly to the context | |
| table. A warmup row also joins directly through its primary consuming | |
| `context_id`; its `method` still labels the acquisition record rather than | |
| the deployment arm. Parse `charged_to_context_ids_json` to recover every | |
| consumer of a shared warmup prefix. `charged_to_methods_json` retains the | |
| corresponding method names. | |
| Warmup rows are **usages**, including repeated executions and shared measured | |
| prefixes. Summing them and also summing context acquisition would double | |
| count. Summing charged costs across competing method contexts is also not a | |
| unique physical research-execution total: a shared first dictionary can be | |
| charged to both arms while physically executed once. Consult the research | |
| manifests for physical execution totals. | |
| ## Appropriate analysis | |
| Compare matched methods within one suite and teacher-case grouping, retaining | |
| refusals and their costs. Include acquisition once per context for cold-start | |
| comparisons. Do not average every row as if it were an independent sample, | |
| pool confirmation cases with the same earlier teacher definitions as new | |
| held-out data, or treat zero observed false certificates as zero population | |
| failure probability. The 24 strong alternative-world cases are not a blind | |
| untouched holdout. | |