Ax-Prover: A Deep Reasoning Agentic Framework for Theorem Proving in Mathematics and Quantum Physics
arXiv:2510.12787v4 Announce Type: replace Abstract: We present Ax-Prover, a multi-agent system for automated theorem proving in Lean that can solve problems across diverse scientific domains and operate either autonomously or collaboratively with human experts. To achieve this, Ax-Prover approaches scientific problem solving through formal proof generation, a process that demands both creative reasoning and strict syntactic rigor. Ax-Prover meets this challenge by equipping Large Language Models (LLMs), which provide knowledge and reasoning, with Lean tools via the Model Context Protocol (MCP)
The rapid advancements in large language models and the increasing sophistication of theorem proving environments like Lean are converging to enable new capabilities in automated reasoning.
This development indicates a significant leap in AI's ability to engage in formal scientific problem-solving, impacting areas from pure mathematics to complex scientific domains like quantum physics, accelerating discovery and validation.
AI agents are moving beyond simple data processing to performing complex, abstract reasoning, collaborating with human experts, and autonomously generating rigorous formal proofs in scientific fields.
- · AI/ML researchers
- · Mathematics and Physics research
- · Formal verification industry
- · Software development (for complex systems)
- · Tasks requiring manual, repetitive formal proof generation
- · Specialized human theorem provers (for routine tasks)
Ax-Prover demonstrates a new paradigm for AI-human collaboration in scientific discovery and formal validation.
The ability to formally prove theorems in quantum physics could accelerate the development of quantum computing and other advanced technologies.
This could lead to a shift in how scientific research is conducted, with AI systems becoming indispensable partners in hypothesis testing and knowledge generation.
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Read at arXiv cs.AI