| --- |
| license: cc-by-4.0 |
| task_categories: |
| - reinforcement-learning |
| - text-generation |
| - tabular-classification |
| language: |
| - en |
| pretty_name: LeanWordBench |
| size_categories: |
| - 10K<n<100K |
| tags: |
| - benchmark |
| - formal-verification |
| - theorem-proving |
| - algebra |
| - reinforcement-learning |
| - word-problems |
| - lean |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/leanwordbench_instances.jsonl |
| --- |
| |
| # Dataset Card: LeanWordBench |
|
|
| ## Summary |
|
|
| LeanWordBench is a benchmark suite for verifiable certificate search over |
| algebraic word problems. Instances are grouped into families such as free-group |
| word equality, subgroup membership, word equations, braid rewriting, exact |
| planning, Andrews--Curtis frontier search, knot move traces, Gröbner reduction, |
| and group-ring witness search. |
|
|
| ## Intended Use |
|
|
| The benchmark is intended for evaluating systems that produce explicit |
| mathematical certificates: rewrite traces, witnesses, substitutions, action |
| sequences, collision candidates, or algebraic objects. Scores should be reported |
| only after exact replay by the family verifier. |
|
|
| ## Not Intended For |
|
|
| - Treating frontier timeouts as mathematical negative labels. |
| - Ranking systems by natural-language explanations that do not include accepted |
| certificates. |
| - Claiming that classical exact procedures solve frontier regimes merely because |
| they audit released closed slices. |
|
|
| ## Data Sources |
|
|
| Most rows are generated from standard mathematical presentations and exact |
| verifiers. External sources are referenced in `data/manifest.json` and include |
| SMT-LIB/StringFuzz style word-equation resources, AC-Solver/Miller--Schupp |
| Andrews--Curtis instances, SymbolicData-inspired Gröbner systems, Regina/knot |
| diagram resources, and Gardam/Soelberg group-ring artifacts where applicable. |
|
|
| ## Responsible AI Notes |
|
|
| The benchmark contains symbolic mathematical data, not human-subject, personal, |
| biometric, medical, or demographic records. The main risk is scientific |
| overclaiming: unknown frontier rows must remain unknown unless an explicit |
| certificate or obstruction is found. |
|
|
