Hugging Face Trending Papers
The Gold Rush in AI4Math, II: What are signatures of AI-assisted Proofs?
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arXiv AI
22h ago
The Gold Rush in AI4Math, II: What are signatures of AI-assisted Proofs?
arXiv:2611.08412v1 Announce Type: new Abstract: The growing use of AI in mathematics raises a question: can we detect AI involvement in proofs, separately from AI assistance with writing? Historical...
arXiv Computation and Language
Oct 7
Who Wrote It Is Not Enough: Detecting Who Contributed the Insight
arXiv:2610.07365v1 Announce Type: new Abstract: As LLMs increasingly assist scientific writing and peer review, detecting who wrote the text is no longer sufficient: we need to determine who contribu...
arXiv AI
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Autonomous AI Agents Systematically Diverge From Human Preferences in Proof Formalization
arXiv:2611.06995v1 Announce Type: new Abstract: Artificial intelligence (AI) systems have ushered in a new age of proof abundance, but helping humans understand these proofs requires new attention to...
Hugging Face Trending Papers
Jul 30
MECA: A Mechanism-Centered Agent for Constructing Well-Specified and Valuable Mathematical Conjectures
Automatically constructing well-specified and valuable mathematical conjectures remains a central challenge in AI-assisted mathematical discovery. Many existing open problems and conjectures are often too broad, underspecified, or difficult to connect to plausible proof or refutation strategies.
arXiv AI
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The Problem Is the Problem: Towards Scalable Mathematical Discovery
The paper introduces a new human‑AI collaboration paradigm for mathematical discovery, shifting from selecting individual problems to exploring broad research directions. It presents the Find, Attempt, and Recommend (FAR) pipeline, which automatically searches a literature corpus, filters candidate conjectures, and surfaces promising resolutions for expert review. In a combinatorics pilot, FAR processed over 5,000 papers, identified thousands of open conjectures, and ultimately highlighted 77 items that led to new discoveries.
arXiv AI
Oct 5
Continual Graph Memory for Mathematical Research Agents
The paper introduces Ansatz, a mathematical research agent that uses Continual Graph Memory to organize and reuse intermediate proof results across multiple problems. The system represents facts, plans, and counterexamples in a unified graph, enabling dependency-aware retrieval, evidence-sensitive curation, and scoped recall to guide long-horizon proof searches. Experiments show Ansatz solves all ten First Proof Second Batch problems, tackles the Jamison caterpillar conjecture, and makes progress on several Erdős problems without human help.