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MiniMax open-sourced MaxProof, a test-time scaling framework for LLM mathematical proofs, and released a companion paper. The framework uses an evolutionary search mechanism to enable the M3 model to achieve gold-medal scores on both the IMO 2025 and USAMO 2026 test sets.
This paper introduces Entropy-Guided Power Sampling (EGPS), a training-free and verifier-free sampler that improves the efficiency of power sampling for enhancing base language model reasoning. EGPS achieves up to 12.6x speedup over standard Metropolis-Hastings sampling while reaching best or tied-best accuracy on benchmarks like MATH500, HumanEval, and GPQA.
This paper introduces LinTree, which improves LLM reasoning by adding explicit parent pointers to linearized search histories, showing that making the tree structure explicit boosts both task performance and search efficiency compared to implicit reasoning and heuristic-guided search.
ETCHR is a novel image editing approach that decouples visual reasoning from image generation, using a two-stage training process (Reasoning Imitation and Reasoning Enhancement) to improve multimodal language model performance across five visual reasoning tasks. It achieves consistent gains of 4-5% Pass@1 on models like Qwen3-VL-8B, Gemini-3.1-Flash-Lite, and Kimi K2.5.
OptiLLM is an open-source inference proxy that boosts LLM reasoning accuracy by up to 10x using advanced techniques without requiring retraining, compatible with various AI APIs.