arXiv:2603.20405v2 Announce Type: replace Abstract: We report on an experiment in which Claude Opus~4.6, equipped with a suite of Model Context Protocol (MCP) tools for the Rocq proof assistant, autonomously proved 10 of 12 problems from the 2025 Putnam Mathematical Competition. The MCP tools, designed with Claude by analyzing logs from a prior experiment on miniF2F-Rocq, encode a "compile-first, interactive-fallback" strategy. Running on an isolated VM with no internet access, the agent deployed 141 subagents over 17.7 hours of active compute (51.6h wall-clock), consuming approximately 1.9 bi
The rapid advancements in large language models and autonomous agentic systems are enabling machines to solve complex, unstructured problems like mathematical proofs with increasing proficiency, evidenced by this new benchmark.
This demonstration highlights the accelerating capability of AI agents to perform tasks previously requiring high-level human cognition, indicating a significant step towards autonomous scientific and intellectual work.
The perceived boundary of AI's capability in complex reasoning tasks has expanded, suggesting that sophisticated white-collar roles demanding mathematical and logical prowess are increasingly susceptible to automation.
- · AI Agent developers
- · Proof assistant developers
- · Academic research institutions
- · High-tech companies leveraging advanced AI
- · Entry-level mathematicians
- · Routine engineering roles
- · Traditional white-collar employment requiring logical problem-solving
- · Education systems slow to adapt to AI capabilities
AI agents will increasingly be deployed to tackle unsolved problems in mathematics, science, and engineering.
The demand for human experts will shift from routine problem-solving to problem formulation, AI system design, and verification of AI-generated solutions.
This could lead to an acceleration of scientific discovery and technological innovation by offloading complex reasoning tasks to highly capable AI systems.
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Read at arXiv cs.LG