Mathematicians in the age of AI

• URL: http://arxiv.org/abs/2603.03684v1
• Date: Wed, 04 Mar 2026 03:23:58 GMT
• Title: Mathematicians in the age of AI
• Authors: Jeremy Avigad,
• Abstract summary: Recent developments show that AI can prove research-level theorems in mathematics.<n>This essay urges mathematicians to stay up-to-date with the technology.
• Score: 1.2691047660244335
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent developments show that AI can prove research-level theorems in mathematics, both formally and informally. This essay urges mathematicians to stay up-to-date with the technology, to consider the ways it will disrupt mathematical practice, and to respond appropriately to the challenges and opportunities we now face.

Related papers

• Towards Autonomous Mathematics Research [48.29504087871558]
  We introduce Aletheia, a math research agent that iteratively generates, verifies, and revises solutions end-to-end in natural language.<n>Specifically, Aletheia is powered by an advanced version of Gemini Deep Think for challenging reasoning problems.<n>We demonstrate Aletheia from Olympiad problems to PhD-level exercises and most notably, through several distinct milestones in AI-assisted mathematics research.
  arXiv  Detail & Related papers  (2026-02-10T18:50:15Z)
• Advancing mathematics research with generative AI [0.0]
  We discuss how generative AI models can be used to advance mathematics research.<n>By putting the design of generative AI models to their advantage, mathematicians may use them as powerful interactive assistants.
  arXiv  Detail & Related papers  (2025-09-29T01:04:44Z)
• Mathematics and Machine Creativity: A Survey on Bridging Mathematics with AI [14.825293189738849]
  This paper presents a comprehensive overview on the applications of artificial intelligence (AI) in mathematical research.<n>Recent developments in AI, particularly in reinforcement learning (RL) and large language models (LLMs), have demonstrated the potential for AI to contribute back to mathematics.<n>This survey aims to establish a bridge between AI and mathematics, providing insights into the mutual benefits and fostering deeper interdisciplinary understanding.
  arXiv  Detail & Related papers  (2024-12-21T08:58:36Z)
• Formal Mathematical Reasoning: A New Frontier in AI [60.26950681543385]
  We advocate for formal mathematical reasoning and argue that it is indispensable for advancing AI4Math to the next level.<n>We summarize existing progress, discuss open challenges, and envision critical milestones to measure future success.
  arXiv  Detail & Related papers  (2024-12-20T17:19:24Z)
• FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI [8.32177898148028]
  FrontierMath is a benchmark of hundreds of original, exceptionally challenging mathematics problems crafted and vetted by expert mathematicians.<n>Current state-of-the-art AI models solve under 2% of problems, revealing a vast gap between AI capabilities and the prowess of the mathematical community.<n>As AI systems advance toward expert-level mathematical abilities, FrontierMath offers a rigorous testbed that quantifies their progress.
  arXiv  Detail & Related papers  (2024-11-07T17:07:35Z)
• A Triumvirate of AI Driven Theoretical Discovery [0.0]
  We argue that while the theorist is in no way in danger of being replaced by AI in the near future, the hybrid of human expertise and AI algorithms will become an integral part of theoretical discovery.
  arXiv  Detail & Related papers  (2024-05-30T11:57:00Z)
• MathBench: Evaluating the Theory and Application Proficiency of LLMs with a Hierarchical Mathematics Benchmark [82.64129627675123]
  MathBench is a new benchmark that rigorously assesses the mathematical capabilities of large language models. MathBench spans a wide range of mathematical disciplines, offering a detailed evaluation of both theoretical understanding and practical problem-solving skills.
  arXiv  Detail & Related papers  (2024-05-20T17:52:29Z)
• Machine learning and information theory concepts towards an AI Mathematician [77.63761356203105]
  The current state-of-the-art in artificial intelligence is impressive, especially in terms of mastery of language, but not so much in terms of mathematical reasoning. This essay builds on the idea that current deep learning mostly succeeds at system 1 abilities. It takes an information-theoretical posture to ask questions about what constitutes an interesting mathematical statement.
  arXiv  Detail & Related papers  (2024-03-07T15:12:06Z)
• AI for Mathematics: A Cognitive Science Perspective [86.02346372284292]
  Mathematics is one of the most powerful conceptual systems developed and used by the human species. Rapid progress in AI, particularly propelled by advances in large language models (LLMs), has sparked renewed, widespread interest in building such systems.
  arXiv  Detail & Related papers  (2023-10-19T02:00:31Z)
• A Survey of Deep Learning for Mathematical Reasoning [71.88150173381153]
  We review the key tasks, datasets, and methods at the intersection of mathematical reasoning and deep learning over the past decade. Recent advances in large-scale neural language models have opened up new benchmarks and opportunities to use deep learning for mathematical reasoning.
  arXiv  Detail & Related papers  (2022-12-20T18:46:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.