MaxProof achieves gold-medal-level performance on IMO 2025 and USAMO 2026 via population-level test-time scaling
MiniMax introduces MaxProof, a test-time scaling framework for competition-level mathematical proof built on their MiniMax-M3 model. The system trains three capabilities — proof generation, verification, and critique-conditioned repair — then at inference time runs tournament selection over a population of candidate proofs. MaxProof scores 35/42 on IMO 2025 and 36/42 on USAMO 2026, exceeding the human gold-medal threshold on both competitions.
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MiniMax M2.7 proprietary reasoning model competes with Gemini and Claude Opus; roundup covers Cursor Composer 2, MAI-Image-2, Claude Code Channels, and Anthropic defense dispute
MiniMax released M2.7, a proprietary reasoning model that achieved 66.6% on MLE Bench Lite (tying Gemini 3.1) and 56.22% on SWE-Pro, priced at $0.30/$1.20 per million tokens, with the shift to proprietary marking a potential strategic pivot among Chinese AI labs away from open weights. Cursor released Composer 2, an agentic coding model built on a fine-tuned Kimi 2.5 (via Moonshot partnership), priced 86% cheaper than its predecessor and scoring 73.7 on SWE-bench Multilingual. Anthropic released Claude Code Channels, routing Telegram and Discord messages into local Claude Code sessions via MCP plugins, and separately filed a court response denying it has any backdoor or kill switch into military deployments of Claude. Microsoft announced MAI-Image-2, a text-to-image model ranking third on Arena.ai among research labs.
PROVE framework trains LLMs for multi-step tool use via stateful MCP environments and programmatic rewards
Researchers introduce PROVE (Programmatic Rewards On Verified Environments), a framework for training LLMs to orchestrate multi-step tool calls using reinforcement learning. The system includes a library of 20 stateful MCP servers with 343 tools, an automated data synthesis pipeline that grounds training queries in live server state, and a multi-component programmatic reward function requiring no judge model. Training four models (Qwen3-4B, Qwen3-8B, Qwen2.5-7B, Granite-4.1-8B) with ~13K examples yields gains of up to +10.2 on BFCL Multi-Turn, +6.8 on tau2-bench, and +6.5 on T-Eval, demonstrating consistent improvements in multi-step tool orchestration.
Large-Scale Evaluation of LLM-Driven Formal Proof Search on Open Mathematical Problems
Researchers present the first large-scale evaluation of LLM-based formal proof search on genuinely open mathematical problems, using Lean as a verification backend. Their most capable agent autonomously resolved 9 of 353 open Erdős problems and proved 44 of 492 OEIS conjectures, at a cost of a few hundred dollars per problem. The system is already being deployed in active research across combinatorics, optimization, graph theory, algebraic geometry, and quantum optics. The study also compares agent architectures, finding that more sophisticated designs outperform simple generate-and-verify loops on the hardest problems.
OpenAI Neural Theorem Prover Solves Formal Math Olympiad Problems in Lean
OpenAI developed a neural theorem prover integrated with the Lean proof assistant that can solve challenging high-school olympiad problems, including problems from AMC12, AIME, and two IMO-adapted problems. The system demonstrates automated formal mathematical reasoning at a level previously requiring human expertise. This represents a significant capability milestone in AI-assisted formal verification and mathematical problem-solving.
OpenAI Shares First Proof Math Challenge Submissions
OpenAI has published its AI model's proof attempts for the First Proof math challenge, a competition designed to test research-grade mathematical reasoning on expert-level problems. This represents a capability demonstration of OpenAI's models on formal mathematical proof generation. The submission signals continued progress in AI mathematical reasoning at a level approaching or engaging with professional research mathematics.
Gemini with Deep Think Achieves Gold-Medal Standard at IMO 2025
DeepMind's advanced Gemini model with Deep Think reasoning has officially achieved gold-medal standard at the International Mathematical Olympiad, the world's most prestigious pre-university mathematics competition. The IMO involves six problems across algebra, combinatorics, geometry, and number theory, and has been held annually since 1959. This represents a formal, externally validated milestone in AI mathematical reasoning capability.
How NuminaMath Won the 1st AIMO Progress Prize
NuminaMath won the first AI Mathematical Olympiad (AIMO) Progress Prize, a competition focused on advancing AI capabilities in mathematical reasoning. The blog post details the technical approach and methodology used by the winning team. This represents a notable milestone in AI mathematical problem-solving, a domain considered a key frontier for reasoning capabilities.
Goedel-Architect achieves state-of-the-art formal theorem proving with blueprint-based agentic framework
Goedel-Architect is an agentic framework for formal theorem proving in Lean 4 that uses blueprint generation — a dependency graph of definitions and lemmas — rather than recursive decomposition, enabling parallel lemma closure and global refinement. Built on DeepSeek-V4-Flash (284B-A13B), it achieves 99.2% pass@1 on MiniF2F-test and 75.6% on PutnamBench, scaling to 100% on MiniF2F, 88.8% on PutnamBench, and 4/6 on IMO 2025 when seeded with natural-language proofs. The authors claim state-of-the-art performance for an open-source pipeline at up to 500x lower cost than comparable systems.