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提出了 PACE（研究证明代理），支持在应用数学中联合进行猜想修订与证明开发，包含研究循环、独立验证器与证据追踪记忆，并发布了聚焦优化领域的 ResearchProofBench 基准，包含 51 个已知证明任务和 6 个开放问题案例。
DTCA introduces a token-level credit assignment framework for reinforcement learning in diffusion language models, measuring token contributions via confidence improvements and entropy calibration. Experiments on LLaDA-8B-Instruct show gains of up to 9.0 points on MATH500 and 5.3 points on GSM8K for mathematical reasoning.
The paper introduces Ask-to-Distill (A2D), a framework that RL-trains a student-initialized synthesizer to generate synthetic questions that elicit dense teacher supervision for on-policy distillation, without needing reference answers. It improves performance and teacher-gap recovery over unoptimized prompts and real-question banks across math, code, and agentic tasks.
该论文提出了 MAMath-Bench，一个面向多智能体数学推理的过程感知基准，包含 1003 道跨 11 个数学类别与文本、图像、音频多模态的题目，从答案正确性、推理过程可靠性与对抗扰动鲁棒性三个维度进行评估，并发现多智能体协作并不总能提升最终准确率且易受对抗推理错误影响。
这篇论文提出 Concurrent Constraint Reinforcement (CCR)，将多个可独立验证的问题打包进单个受上限的补全中，使 RLVR 在 8,000 token 短窗口下仍保留正确性信号，相比 32k 窗口的 GRPO 每步训练 token 成本降低约 80%。
Over the past seven months, AI models have consistently been solving open Erdős problems that mathematicians had failed to crack. In San Francisco, the industry's expected timeline for AGI has shrunk to 10 years, and the overall pace of change far exceeds what most people imagine.
中国研究团队借助上海AI实验室自研的Intern-S2科学大模型，完整证明了困扰国际数学界30余年的三维Ghys猜想，随后由北京大学团队完成形式化机器验证，验证代码已全部开源。
A guest post on Terence Tao's blog by Francesco Maggi weighs whether LLM-assisted mathematics will usher in a golden age of accelerated discovery or a dark age where knowledge becomes alien to human understanding, outlining eight key choices and actionable proposals for integrating AI into STEM.
介绍 IneqAgent，一个将 LLM 推理与符号计算结合的智能体框架，用于 Lean 4 中的不等式形式化证明，采用 lemma 级蒙特卡洛树搜索协调引理构造与符号后端选择，在四个基准上达到 94.09% 的成功率。
LeanPRM提出利用Lean证明助手自动为自然语言数学证明生成步骤级正确性标签，无需人工标注即可训练过程奖励模型（PRM）。Qwen3-4B验证器在该数据上训练后，步级标签准确率从48.2提升至74.7，并在IMO级问题的最佳-of-N选择和多个外部验证基准上超越PRM800K训练的验证器。
MathVis introduces a 512-task benchmark evaluating LLMs' ability to generate mathematical diagrams and perform diagram-assisted reasoning, with GPT-6-astra, Claude Fable 5, and Gemini 3.1 Pro achieving only 31.25%, 26.56%, and 22.85% Pass@1, revealing that models still rely primarily on symbolic reasoning rather than visual diagrams.
这篇论文提出EDEN方法，通过在RLVR中向rollout条件注入锚定的嵌入空间噪声来增加轨迹多样性、增强组相对优势信号，从而在不改变推理过程的情况下提升GRPO训练效果，尤其在解的覆盖面上取得最大增益。
作者探讨 AI 在数学证明中的角色，质疑为何 AI 不能在生成证明之外，进一步理解、解释并推广证明背后的数学思想，而非仅停留在验证层面。
A linguistics-background Reddit poster weighs into heated debates about AI in mathematics, questioning why AI-generated proofs are dismissed as "answers without understanding" and arguing that a paper's value should be judged by what readers can learn rather than the author's failed attempts.
提出了 LoopOPD 和 D-LoopOPD，一种跨循环的在线蒸馏框架，利用循环语言模型自身的末端循环作为教师，为中间循环提供稠密监督，实现循环模型的自提升，并在 Ouro-Thinking 模型的数学推理任务上取得改进。
SynCo 提出了一种基于多智能体强化学习的数据合成协同训练框架，联合优化 Synthesizer 与 Reasoner 两个智能体，使训练数据分布随模型能力共同演化，并在八个数学推理基准上取得了最强的整体表现。
该论文提出 Adaptive Reference Guidance (ARG)，通过分析前缀续写的 KL 散度来平衡参考解引导与自由生成，用于 GRPO 中全失败组的轨迹构建，在 Qwen3-4B/8B 上取得最高的聚合 pass@12。
The article explores how soon AI might effectively 'solve' mathematics and what obstacles remain, framing the discussion around the frontier of AI-driven mathematical reasoning.
论文提出 SAPD（Step-Aligned Privileged Distillation），一种无需 rollout 的自蒸馏方法，将固定示范转化为与推理步骤对齐的分布式监督，在数学推理基准上媲美在线强化学习并实现约 2 倍训练加速。
OpenAI released the openai/math repository containing 722 manuscripts in 372 research families — spanning results like integer multiplication below n log n, the irrationality exponent of π, and Mahler conjectures — produced by an unreleased internal model over ~4,000 problems, with many results formalized in Lean.