VERITAS: Verifier-Guided Proof Search for Zero-Shot Formal Theorem Proving
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Computer Science > Machine Learning
Title:VERITAS: Verifier-Guided Proof Search for Zero-Shot Formal Theorem Proving
Abstract:LLM-based formal provers often collapse rich verifier signals (syntax errors, type mismatches, partial goal progress) into a binary pass/fail bit. We present VERITAS, a zero-shot framework that routes every verifier signal back into proof search through a two-phase protocol: Best-of-N sampling first, then a critic-guided MCTS pass that ingests Phase 1 failures as explicit negative examples. The protocol preserves every theorem solved by its own Phase 1 sweep, so Phase 2's additional solves are attributable to feedback-driven exploration. VERITAS reaches 40.6% on miniF2F (vs. an independently run Best-of-5 at 36.9%, Portfolio 26.2%) and 7.3% on VERITAS-CombiBench, a 55-theorem combinatorics benchmark we release on which Best-of-5 (1.8%) falls below Portfolio (3.6%), exposing that unguided sampling hurts when correct lemma names must be recovered iteratively from verifier feedback. Artifacts are available on GitHub.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO); Programming Languages (cs.PL) |
| Cite as: | arXiv:2606.19399 [cs.LG] |
| (or arXiv:2606.19399v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.19399
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