Latest AI for Mathematics Research Papers

The newest AI for Mathematics papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks AI for Mathematics so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• A Comprehensive Anatomy of Human and DeepSeek-R1 LLM Mathematical Reasoning
  Yuxiang Chen, Jun Wang · arXiv · Jun 5, 2026

  The emergence of "Aha moments" in large language models, particularly DeepSeek-R1-0120, has raised the question of whether these systems genuinely reason or merely imitate the appearance of reasoning. We conduct a comprehensive empirical co…

• Goedel-Architect: Streamlining Formal Theorem Proving with Blueprint Generation and Refinement
  Jui-Hui Chung, Ziyang Cai, Zihao Li, Qishuo Yin et al. · arXiv · Jun 4, 2026

  We introduce Goedel-Architect, an agentic framework for formal theorem proving in Lean 4 centered on blueprint generation and refinement. A blueprint is a dependency graph of definitions and lemmas that builds up to the main theorem. First,…

• Advancing Mathematics Research with AI-Driven Formal Proof Search
  George Tsoukalas, Anton Kovsharov, Sergey Shirobokov, Anja Surina et al. · arXiv · May 21, 2026

  Large language models (LLMs) increasingly excel at mathematical reasoning, but their unreliability limits their utility in mathematics research. A mitigation is using LLMs to generate formal proofs in languages like Lean. We perform the fir…

• Verifier-Backed Hard Problem Generation for Mathematical Reasoning
  Yuhang Lai, Jiazhan Feng, Yee Whye Teh, Ning Miao · arXiv · May 7, 2026

  Large Language Models (LLMs) demonstrate strong capabilities for solving scientific and mathematical problems, yet they struggle to produce valid, challenging, and novel problems - an essential component for advancing LLM training and enabl…

• AI Co-Mathematician: Accelerating Mathematicians with Agentic AI
  Daniel Zheng, Ingrid von Glehn, Yori Zwols, Iuliya Beloshapka et al. · arXiv · May 7, 2026

  We introduce the AI co-mathematician, a workbench for mathematicians to interactively leverage AI agents to pursue open-ended research. The AI co-mathematician is optimized to provide holistic support for the exploratory and iterative reali…

• Contextual Multi-Objective Optimization: Rethinking Objectives in Frontier AI Systems
  Jie Zhou, Qin Chen, Liang He · arXiv · May 5, 2026

  Frontier AI systems perform best in settings with clear, stable, and verifiable objectives, such as code generation, mathematical reasoning, games, and unit-test-driven tasks. They remain less reliable in open-ended settings, including scie…

• Turning the TIDE: Cross-Architecture Distillation for Diffusion Large Language Models
  Gongbo Zhang, Wen Wang, Ye Tian, Li Yuan · arXiv · Apr 29, 2026

  Diffusion large language models (dLLMs) offer parallel decoding and bidirectional context, but state-of-the-art dLLMs require billions of parameters for competitive performance. While existing distillation methods for dLLMs reduce inference…

• Rethinking Math Reasoning Evaluation: A Robust LLM-as-a-Judge Framework Beyond Symbolic Rigidity
  Erez Yosef, Oron Anschel, Shunit Haviv Hakimi, Asaf Gendler et al. · arXiv · Apr 24, 2026

  Recent advancements in large language models have led to significant improvements across various tasks, including mathematical reasoning, which is used to assess models' intelligence in logical reasoning and problem-solving. Models are eval…