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
The development of VERITAS reflects the ongoing maturation of LLM capabilities for formal reasoning, pushing the boundaries of what is achievable in automated theorem proving by leveraging detailed feedback.
This framework significantly advances the efficiency and reliability of AI in zero-shot theorem proving, moving closer to systems that can autonomously verify and generate complex formal proofs.
The ability of LLMs to interpret and act upon rich verifier signals, rather than just binary pass/fail, fundamentally alters the interaction between AI and formal systems, leading to more robust and sophisticated proof search strategies.
- · AI researchers
- · Formal verification industry
- · Software engineering
- · Mathematics (theoretical)
- · Manual theorem provers (long-term)
- · Less sophisticated AI proving methods
Increased automation and accuracy in formal verification processes for critical software and hardware systems.
Accelerated development of mathematically sound AI systems and algorithms, reducing errors in complex computational tasks.
The potential for AI to autonomously discover and prove new mathematical theorems, expanding the frontiers of human knowledge.
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Read at arXiv cs.LG