What is mathematics now, and what should it be?

• URL: http://arxiv.org/abs/2608.23218v1
• Date: Mon, 24 Aug 2026 13:13:17 GMT
• Title: What is mathematics now, and what should it be?
• Abstract summary: Advances in neural theorem provers have been impressive, but the successes obscure a broader vision of what AI can do for mathematics.<n>This essay advances a more expansive and optimistic point of view.
• Score: 1.2691047660244335
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Advances in neural theorem provers have been impressive, but the successes obscure a broader vision of what AI can do for mathematics and how mathematicians can engage with AI. This essay advances a more expansive and optimistic point of view.

Related papers

• Math for AI safety: an invitation for mathematicians [0.44672487764060054]
  New mathematics is needed to design AI that is legible, steerable, and cooperative with humanity.<n>I organize this invitation by mathematical field, so you can turn straight to your own.<n>Each section ends with an open problem that is accessible to a working mathematician with no prior experience in AI safety.
  arXiv  Detail & Related papers  (2026-09-14T09:45:14Z)
• Lectures on AI for Mathematics [8.544747471603365]
  It covers the core principles and diverse applications of using artificial intelligence to advance mathematical research.<n>The book explores how AI can discover hidden mathematical patterns, assist in proving complicated theorems, and even construct counterexamples to challenge conjectures.
  arXiv  Detail & Related papers  (2026-04-13T14:07:49Z)
• Artificial Intelligence and the Structure of Mathematics [5.327210719701061]
  Recent progress in artificial intelligence (AI) is unlocking transformative capabilities for mathematics.<n>We consider how AI may open a grand perspective on mathematics by forging a new route, complementary to mathematicaltextbf logic.<n>As we send AI agents to traverse Platonic mathematical worlds, we expect they will teach us about the nature of mathematics.
  arXiv  Detail & Related papers  (2026-04-07T17:19:33Z)
• Mathematicians in the age of AI [1.2691047660244335]
  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.
  arXiv  Detail & Related papers  (2026-03-04T03:23:58Z)
• 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)
• Can the current trends of AI handle a full course of mathematics? [0.0]
  The study evaluates this ability in four significant aspects, namely, creating a course syllabus, presenting selected material, answering student questions, and creating an assessment.<n>It shows that even though the AI is strong in some important parts like organization and accuracy, there are still some human aspects that are far away from the current abilities of AI.
  arXiv  Detail & Related papers  (2025-07-29T10:21:15Z)
• 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)
• 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)
• Learning to Prove Theorems by Learning to Generate Theorems [71.46963489866596]
  We learn a neural generator that automatically synthesizes theorems and proofs for the purpose of training a theorem prover. Experiments on real-world tasks demonstrate that synthetic data from our approach improves the theorem prover.
  arXiv  Detail & Related papers  (2020-02-17T16:06:02Z)

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.