VERITAS: Verifier-Guided Proof Search for Zero-Shot Formal Theorem Proving
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 21 of its 33 references that are in the map. First posted on arXiv 17 Jun 2026.
Builds on
21 of 33 references.
References · 21
- 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
- 2023Let's Verify Step by Stepcited by 3,789
- 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
- 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,808
- 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,395
- 2022Solving math word problems with process- and outcome-based feedbackcited by 901
- 2023LeanDojo: Theorem Proving with Retrieval-Augmented Language Modelscited by 427
- 2021MiniF2F: a cross-system benchmark for formal Olympiad-level mathematicscited by 391
- 2020Generative Language Modeling for Automated Theorem Provingcited by 388
- 2023Llemma: An Open Language Model For Mathematicscited by 388
- 2026Reinforcement Learning via Self-Distillationcited by 332
- 2022HyperTree Proof Search for Neural Theorem Provingcited by 197
9 more
- 2024DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Searchcited by 197
- 2025Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Provingcited by 133
- 2021Proof Artifact Co-training for Theorem Proving with Language Modelscited by 132
- 2023LEGO-Prover: Neural Theorem Proving with Growing Librariescited by 109
- 2023An In-Context Learning Agent for Formal Theorem-Provingcited by 82
- 2025CodeGrad: Integrating Multi-Step Verification with Gradient-Based LLM Refinementcited by 7
- 2025SPIRAL: Symbolic LLM Planning via Grounded and Reflective Searchcited by 7
- 2025DPO-F+: Aligning Code Repair Feedback with Developers' Preferencescited by 3
- 2026SynthFix: Adaptive Neuro-Symbolic Code Vulnerability Repaircited by 1
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Citing papers per year: 2026: 2.
2 papers.