MaxProof: Scaling Mathematical Proof with Generative-Verifier RL and Population-Level Test-Time Scaling
arXiv:2606.13473v1 Announce Type: cross Abstract: We present MaxProof, a population-level test-time scaling framework for competition-level mathematical proof in the MiniMax-M3 series. M3 first trains three proof-oriented capabilities -- proof generation, proof verification, and critique-conditioned proof repair -- using a defense-in-depth generative verifier engineered for low false-positive rate. These capabilities are merged into a single released M3 model. At test time, MaxProof treats the model as a generator, verifier, refiner, and ranker, searches over a population of candidate proofs,
The continuous advancements in AI, particularly in generative models and reinforcement learning, are enabling new breakthroughs in complex problem-solving domains like mathematical proof.
This breakthrough indicates significant progress in AI's ability to perform abstract reasoning, a critical step towards more advanced general intelligence and autonomous AI systems.
AI models are becoming more proficient and reliable in formal reasoning tasks, moving beyond pattern recognition to demonstrable logical deduction and verification.
- · AI research labs
- · AI agent developers
- · Mathematics community
- · Software verification
- · Tasks requiring manual formal proof
- · Traditional theorem provers
Increased automation and accuracy in tasks requiring logical deduction and formal verification.
Acceleration of scientific discovery and engineering R&D due to AI-assisted proof and verification capabilities.
The development of highly reliable and verifiable AI systems, expanding their deployment into safety-critical domains.
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Read at arXiv cs.CL