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[ARTICLE · art-33564] src=arxiv.org ↗ pub= topic=artificial-intelligence verified=true sentiment=↑ positive

VERITAS: Verifier-Guided Proof Search for Zero-Shot Formal Theorem Proving

Researchers introduced VERITAS, a zero-shot framework for formal theorem proving that leverages rich verifier signals through a two-phase protocol combining Best-of-N sampling and critic-guided Monte Carlo tree search. VERITAS achieved 40.6% on the miniF2F benchmark, outperforming baseline methods, and 7.3% on a new combinatorics benchmark, demonstrating the value of feedback-driven exploration in proof search.

read1 min views1 publishedJun 19, 2026

arXiv:2606.19399v1 Announce Type: new 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.

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