Proof Recommendation System for the HOL4 Theorem Prover
- URL: http://arxiv.org/abs/2501.05463v1
- Date: Tue, 31 Dec 2024 17:13:59 GMT
- Title: Proof Recommendation System for the HOL4 Theorem Prover
- Authors: Nour Dekhil, Adnan Rashid, Sofiene Tahar,
- Abstract summary: We introduce a proof recommender system for the HOL4 theorem prover.<n>Our tool is built upon a transformer-based model designed specifically to provide proof assistance in HOL4.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We introduce a proof recommender system for the HOL4 theorem prover. Our tool is built upon a transformer-based model [2] designed specifically to provide proof assistance in HOL4. The model is trained to discern theorem proving patterns from extensive libraries of HOL4 containing proofs of theorems. Consequently, it can accurately predict the next tactic(s) (proof step(s)) based on the history of previously employed tactics. The tool operates by reading a given sequence of tactics already used in a proof process (in our case, it contains at least three tactics), referred to as the current proof state, and provides recommendations for the next optimal proof step(s).
Related papers
- LeanProgress: Guiding Search for Neural Theorem Proving via Proof Progress Prediction [74.79306773878955]
We introduce LeanProgress, a method that predicts the progress in the proof.
Our experiments show that LeanProgress achieves an overall prediction accuracy of 75.1%.
arXiv Detail & Related papers (2025-02-25T07:46:36Z) - Generating Millions Of Lean Theorems With Proofs By Exploring State Transition Graphs [6.65877320351217]
We developed LeanNavigator, a novel method for generating a large-scale dataset of Lean theorems and proofs.
We generated 4.7 million theorems totaling 1 billion tokens, surpassing previous datasets by more than an order of magnitude.
Using this extensive dataset, we trained an AI model that outperforms the state-of-the-art ReProver model in theorem-proving tasks.
arXiv Detail & Related papers (2025-02-16T06:20:39Z) - ImProver: Agent-Based Automated Proof Optimization [18.315243539816464]
We present ImProver, a large-language-model agent that rewrites proofs to optimize arbitrary user-defined metrics in Lean.
We test ImProver on rewriting real-world undergraduate, competition, and research-level mathematics theorems.
arXiv Detail & Related papers (2024-10-07T05:14:18Z) - Lean-STaR: Learning to Interleave Thinking and Proving [53.923617816215774]
We present Lean-STaR, a framework for training language models to produce informal thoughts prior to each step of a proof.
Lean-STaR achieves state-of-the-art results on the miniF2F-test benchmark within the Lean theorem proving environment.
arXiv Detail & Related papers (2024-07-14T01:43:07Z) - Proving Theorems Recursively [80.42431358105482]
We propose POETRY, which proves theorems in a level-by-level manner.
Unlike previous step-by-step methods, POETRY searches for a sketch of the proof at each level.
We observe a substantial increase in the maximum proof length found by POETRY, from 10 to 26.
arXiv Detail & Related papers (2024-05-23T10:35:08Z) - Faithful Explanations of Black-box NLP Models Using LLM-generated
Counterfactuals [67.64770842323966]
Causal explanations of predictions of NLP systems are essential to ensure safety and establish trust.
Existing methods often fall short of explaining model predictions effectively or efficiently.
We propose two approaches for counterfactual (CF) approximation.
arXiv Detail & Related papers (2023-10-01T07:31:04Z) - Generating Natural Language Proofs with Verifier-Guided Search [74.9614610172561]
We present a novel stepwise method NLProofS (Natural Language Proof Search)
NLProofS learns to generate relevant steps conditioning on the hypothesis.
It achieves state-of-the-art performance on EntailmentBank and RuleTaker.
arXiv Detail & Related papers (2022-05-25T02:22:30Z) - Tactic Learning and Proving for the Coq Proof Assistant [0.5735035463793007]
Our system predicts appropriate tactics and finds proofs in the form of tactic scripts.
The performance of the system is evaluated on the Coq Standard Library.
When combined with the CoqHammer system, the two systems together prove 56.7% of the library's lemmas.
arXiv Detail & Related papers (2020-03-20T08:22:30Z) - Learning to Prove Theorems by Learning to Generate Theorems [71.46963489866596]
We learn a neural generator that automatically synthesizes theorems and proofs for the purpose of training a theorem prover.
Experiments on real-world tasks demonstrate that synthetic data from our approach improves the theorem prover.
arXiv Detail & Related papers (2020-02-17T16:06:02Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.