mlsanityData to 7 Oct 2026

First Proof Second Batch

arXiv 2606.18119 ↗First posted 16 Jun 2026cs.AIcited by 5

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Abstract (arXiv)

To assess the ability of current AI systems to correctly solve research-level mathematics problems, we tested several AI systems on a set of ten problems in a broad range of mathematical fields; these problems arose naturally in the research process of the contributors. This document includes the problems, our methodology, and the results of our testing. We provide links to supplementary documents including the human solutions, the AI-generated solutions, and the referee reports and logs for the AI-generated solutions. The ten problems were contributed by the following mathematicians: (1) Dariusz Kaloci'nski and Theodore A. Slaman, (2) Richard Schwartz, (3) Aleksa Milojevic and Benny Sudakov, (4) Larry Guth, (5) Oleg Butkovsky, Jonathan Mattingly, and Lorenzo Zambotti, (6) Joshua Evan Greene and Duncan McCoy, (7) Sucharit Sarkar, (8) Sam Payne and Jidong (Jayden) Wang, (9) Sylvie Corteel and John Lentfer, (10) Srivatsav Kunnawalkam Elayavalli.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 0 of its 10 references that are in the map. First posted on arXiv 16 Jun 2026.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026Danus: Orchestrating Mathematical Reasoning Agents with Fact-Graph Memorycited by 5
  2. 2026Beyond Correctness: Toward Automated Novelty Verification with Lean 4cited by 0
  3. 2026Explanations, Prompts, and Formalizations: Arguments for New Norms in LLM-Enabled Mathematical Researchcited by 0
  4. 2026TCSAlgBench: Benchmarking Automated Proving for Research-Level Theoretical Computer Sciencecited by 0
  5. 2026When Algorithmic Exploration Becomes Cheap: A Case Study of Agentic Research in EDAcited by 0