datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
proof-bundlescve-proof-corpus
CVE Proof Corpus
Six real vulnerability classes, each with a machine-checkable proof that the shipped fix
eliminates it — and a checker that shares no code with whatever produced the proof.
Every record carries the safety relation, the guard the upstream project shipped, the declared
attacker domain, and the nonnegative multipliers that prove the guard implies safety. All six verify.
pip install "certkit@git+https://github.com/nickharris808/certkit@main"
python verify.py… See the full description on the dataset page: https://huggingface.co/datasets/nickh007/cve-proof-corpus.lean-proofs-v1
Part of the SZL Holdings governed estate — claims are designed to carry checkable receipts. Verification proves integrity & origin, never accuracy or performance.
SZLHOLDINGS/lean-proofs-v1
The complete Lean 4 theorem library for the SZL Holdings Ouroboros Invariant research programme.
Doctrine v10/v11 Canonical Numbers
Metric
Value
Declarations
749
Unique axioms
14 (15 raw, 1 dup)
Sorries
163 (112 baseline + 51 Putnam)… See the full description on the dataset page: https://huggingface.co/datasets/SZLHOLDINGS/lean-proofs-v1.lean-proof-or-refute-300
Lean Proof-or-Refute 300
Lean Proof-or-Refute 300 is a compact collection of 300 formal reasoning
problems grounded in Lean 4 and Mathlib. Each problem starts from a verified
Mathlib theorem, makes one small numerical or operator mutation, and asks the
model to return either:
a Lean certificate proving the mutated proposition; or
a Lean certificate proving the exact negation of the complete proposition.
The model receives the related source theorem, a bounded source excerpt… See the full description on the dataset page: https://huggingface.co/datasets/xlr8harder/lean-proof-or-refute-300.lm-eval-results-Contamination-contaminated_proof_7b_v1.0_safetensor-private
Dataset Card for Evaluation run of Contamination/contaminated_proof_7b_v1.0_safetensor
Dataset automatically created during the evaluation run of model Contamination/contaminated_proof_7b_v1.0_safetensor
The dataset is composed of 62 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train"… See the full description on the dataset page: https://huggingface.co/datasets/nyu-dice-lab/lm-eval-results-Contamination-contaminated_proof_7b_v1.0_safetensor-private.proofjudge
ProofJudge
Paired Lean 4 / Mathlib human-authored proofs: the state a declaration was in when a pull request was
opened, and the state it was in when Mathlib merged it. A code-reviewing judge agent is aligned on a pair when it scores the merged version above the initial one.
Pairs were selected from Mathlib PRs by two filters: a mechanical one (at least 3 changed tokens and at least 5% change relative to the shorter proof) and an LLM judge (claude-sonnet-4-20250514) classifying… See the full description on the dataset page: https://huggingface.co/datasets/SJCaldwell/proofjudge.lm-eval-results-Contamination-contaminated_proof_7b_v1.0-private
Dataset Card for Evaluation run of Contamination/contaminated_proof_7b_v1.0
Dataset automatically created during the evaluation run of model Contamination/contaminated_proof_7b_v1.0
The dataset is composed of 62 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to… See the full description on the dataset page: https://huggingface.co/datasets/nyu-dice-lab/lm-eval-results-Contamination-contaminated_proof_7b_v1.0-private.proof-adjusted-autonomy-examples
Proof-Adjusted Autonomy (PAA) — Worked Examples
12 worked scenarios of the PAA metric coined by Michał Piszczek: the share of completed work an AI system executes autonomously AND supports with independent, reliable, timely evidence.
Formula: PAA = P(A) x P(C|A) x P(R|A,C) x P(T|A,C,R)
Each row: scenario, agent type, the four gate probabilities, raw autonomy vs PAA score, the autonomy gap, and an interpretation. Includes the canonical example: a 90% agent that is a 61.6% agent.… See the full description on the dataset page: https://huggingface.co/datasets/cdiamond/proof-adjusted-autonomy-examples.cleaned_NuminaMath-RL-Verifiable_with_proof