arXiv AI
Jun 30
Self-Supervised Theorem Discovery in a Formal Axiomatic System
arXiv:2606. 28747v1 Announce Type: new Abstract: Recent artificial intelligence (AI) systems have shown remarkable progress in mathematical reasoning.
A Theoretical Framework for Self-Play Theorem Proving Algorithms
arXiv:2606. 01861v1 Announce Type: new Abstract: Self-play, a type of training algorithm that enables a model to self-improve, has recently shown promising empirical results in the context of formal theorem proving using Large Language Models (LLMs).
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arXiv Machine Learning
Aug 12
Scaling Self-Play with Self-Guidance
arXiv:2604. 20209v2 Announce Type: replace Abstract: LLM self-play algorithms are notable in that, in principle, nothing bounds their learning: a Conjecturer model creates problems for a Solver, and both improve together.
Hugging Face Trending Papers
Jun 17
Diffusion-Proof: Recipe for Formal Theorem Proving Beyond Auto-Regressive Generation
Enhancing the formal math reasoning capabilities of Large Language Models (LLMs) has become a key focus in both mathematical and computer science communities in recent years. While significant progress has been made in using state-of-the-art Auto-Regressive (AR) LLMs for formal theorem proving, these models suffer from inherent limitations.
arXiv Machine Learning
Jun 18
Diffusion-Proof: Recipe for Formal Theorem Proving Beyond Auto-Regressive Generation
arXiv:2606. 19315v1 Announce Type: new Abstract: Enhancing the formal math reasoning capabilities of Large Language Models (LLMs) has become a key focus in both mathematical and computer science communities in recent years.
arXiv AI
Jun 12
Pythagoras-Prover: Advancing Efficient Formal Proving via Augmented Lean Formalisation
arXiv:2606. 12594v1 Announce Type: new Abstract: Modern Lean theorem provers achieve strong performance only with substantial training and inference compute, driven in part by scarce verified proof data and the long reasoning traces of formal proof search, making both supervised fine-tuning (SFT) and sampling expensive.
arXiv AI
5d ago
VALG: An Agentic System for ML Theory Research
arXiv:2608. 13060v1 Announce Type: new Abstract: Machine learning theory studies learning procedures through mathematical setups in which the data model, training protocol, oracle access, loss, metric, and randomness define the phenomenon that a theorem is meant to explain.
arXiv Machine Learning
Aug 11
Online Learnability of Chain-of-Thought Verifiers: Soundness and Completeness Trade-offs
arXiv:2603. 03538v4 Announce Type: replace Abstract: Large Language Models (LLMs) using chain-of-thought reasoning have demonstrated great potential for solving complex reasoning and planning tasks.
arXiv Machine Learning
Jun 25
Discovering New Theorems via LLMs with In-Context Proof Learning in Lean
arXiv:2509. 14274v3 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated significant promise in formal theorem proving.
arXiv AI
Jun 3
LEAP: Supercharging LLMs for Formal Mathematics with Agentic Frameworks
arXiv:2606. 03303v1 Announce Type: new Abstract: Large Language Models (LLMs) exhibit strong informal mathematical reasoning but struggle to generate mechanically verifiable proofs in formal languages like Lean.
OpenAI Blog
Jun 2, 2018
GamePad: A learning environment for theorem proving
arXiv AI
Aug 11
Neurosymbolic Discovery of Algebraic Graph Constructions
arXiv:2608. 08118v1 Announce Type: new Abstract: There are several methods for searching for graphs with prescribed properties, such as SAT solvers and specialized generators.
arXiv AI
Jul 1
Behavior Cloning is Not All You Need: The Optimality of On-Policy Distillation for Noisy Expert Feedback
arXiv:2606. 30923v1 Announce Type: cross Abstract: Imitation Learning is a natural framework for learning in sequential decision-making systems and has emerged as the dominant paradigm through which we understand language model training.