# Automated Theorem Proving

83 papers with code • 9 benchmarks • 8 datasets

The goal of **Automated Theorem Proving** is to automatically generate a proof, given a conjecture (the target theorem) and a knowledge base of known facts, all expressed in a formal language. Automated Theorem Proving is useful in a wide range of applications, including the verification and synthesis of software and hardware systems.

Source: Learning to Prove Theorems by Learning to Generate Theorems

## Libraries

Use these libraries to find Automated Theorem Proving models and implementations## Latest papers

# SubgoalXL: Subgoal-based Expert Learning for Theorem Proving

This paper introduces SubgoalXL, a novel approach that synergizes subgoal-based proofs with expert learning to enhance LLMs' capabilities in formal theorem proving within the Isabelle environment.

# DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search

We introduce DeepSeek-Prover-V1. 5, an open-source language model designed for theorem proving in Lean 4, which enhances DeepSeek-Prover-V1 by optimizing both training and inference processes.

# miniCTX: Neural Theorem Proving with (Long-)Contexts

We introduce miniCTX, which tests a model's ability to prove formal mathematical theorems that depend on new context that is not seen during training.

# LEAN-GitHub: Compiling GitHub LEAN repositories for a versatile LEAN prover

To address this issue, we propose LEAN-GitHub, a dataset consisting of large-scale formal data extracted from almost all Lean 4 repositories on GitHub.

# PutnamBench: Evaluating Neural Theorem-Provers on the Putnam Mathematical Competition

We present PutnamBench, a new multilingual benchmark for evaluating the ability of neural theorem-provers to solve competition mathematics problems.

# TheoremLlama: Transforming General-Purpose LLMs into Lean4 Experts

However, due to the scarcity of aligned NL and Formal Language (FL) theorem-proving data most modern LLMs exhibit suboptimal performance. This scarcity results in a paucity of methodologies for training LLMs and techniques to fully utilize their capabilities in composing formal proofs.

# Learning Formal Mathematics From Intrinsic Motivation

We propose novel methods for hindsight relabeling on proof search trees to significantly improve the agent's sample efficiency in both tasks.

# FVEL: Interactive Formal Verification Environment with Large Language Models via Theorem Proving

In this paper, we propose FVEL, an interactive Formal Verification Environment with LLMs.

# Lean Workbook: A large-scale Lean problem set formalized from natural language math problems

Our results indicate that the synthetic data pipeline can provide useful training data and improve the performance of LLMs in translating and understanding complex mathematical problems and proofs.

# Proving Theorems Recursively

This approach allows the theorem to be tackled incrementally by outlining the overall theorem at the first level and then solving the intermediate conjectures at deeper levels.