Automated Theorem Proving
70 papers with code • 10 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
Benchmarks
These leaderboards are used to track progress in Automated Theorem Proving
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Libraries
Use these libraries to find Automated Theorem Proving models and implementations
MiniF2F
MED
Geometry3K
Kinship
HOList
ProofNet
Latest papers
Towards Large Language Models as Copilots for Theorem Proving in Lean
In this paper, we explore LLMs as copilots that assist humans in proving theorems.
A Survey on Deep Learning for Theorem Proving
Theorem proving is a fundamental aspect of mathematics, spanning from informal reasoning in mathematical language to rigorous derivations in formal systems.
Don't Trust: Verify -- Grounding LLM Quantitative Reasoning with Autoformalization
Large language models (LLM), such as Google's Minerva and OpenAI's GPT families, are becoming increasingly capable of solving mathematical quantitative reasoning problems.
LeanReasoner: Boosting Complex Logical Reasoning with Lean
Large language models (LLMs) often struggle with complex logical reasoning due to logical inconsistencies and the inherent difficulty of such reasoning.
REFACTOR: Learning to Extract Theorems from Proofs
jinpz/refactor
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With newly extracted theorems, we show that the existing proofs in the MetaMath database can be refactored.
On the (In)feasibility of ML Backdoor Detection as an Hypothesis Testing Problem
We introduce a formal statistical definition for the problem of backdoor detection in machine learning systems and use it to analyze the feasibility of such problems, providing evidence for the utility and applicability of our definition.
MUSTARD: Mastering Uniform Synthesis of Theorem and Proof Data
Recent large language models (LLMs) have witnessed significant advancement in various tasks, including mathematical reasoning and theorem proving.
Llemma: An Open Language Model For Mathematics
We present Llemma, a large language model for mathematics.
TRIGO: Benchmarking Formal Mathematical Proof Reduction for Generative Language Models
Automated theorem proving (ATP) has become an appealing domain for exploring the reasoning ability of the recent successful generative language models.
An In-Context Learning Agent for Formal Theorem-Proving
We evaluate our implementation of COPRA on the miniF2F benchmark for Lean and a set of Coq tasks from the CompCert project.
