A Survey on Theorem Provers in Formal Methods
Not on the map. Why?
Abstract (arXiv)
Mechanical reasoning is a key area of research that lies at the crossroads of mathematical logic and artificial intelligence. The main aim to develop mechanical reasoning systems (also known as theorem provers) was to enable mathematicians to prove theorems by computer programs. However, these tools evolved with time and now play vital role in the modeling and reasoning about complex and large-scale systems, especially safety-critical systems. Technically, mathematical formalisms and automated reasoning based-approaches are employed to perform inferences and to generate proofs in theorem provers. In literature, there is a shortage of comprehensive documents that can provide proper guidance about the preferences of theorem provers with respect to their designs, performances, logical frameworks, strengths, differences and their application areas. In this work, more than 40 theorem provers are studied in detail and compared to present a comprehensive analysis and evaluation of these tools. Theorem provers are investigated based on various parameters, which includes: implementation architecture, logic and calculus used, library support, level of automation, programming paradigm, programming language, differences and application areas.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Dec 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 3; 2025: 3.
8 papers.
- 2024Learn from Failure: Fine-Tuning LLMs with Trial-and-Error Data for Intuitionistic Propositional Logic Provingcited by 11
- 2021Neural Unification for Logic Reasoning over Natural Languagecited by 8
- 2025StepProof: Step-by-step verification of natural language mathematical proofscited by 6
- 2025Efficient PRM Training Data Synthesis via Formal Verificationcited by 5
- 2024Towards Large Language Model Aided Program Refinementcited by 2
- 2025Improving Autoformalization Using Direct Dependency Retrievalcited by 2
- 2022Learning to Prove Trigonometric Identitiescited by 1
- 2024Towards Generalizable and Faithful Logic Reasoning over Natural Language via Resolution Refutationcited by 0
- 2025Improving Autoformalization Using Direct Dependency Retrieval15 Nov 2025
- 2025StepProof: Step-by-step verification of natural language mathematical proofs12 Jun 2025
- 2025Efficient PRM Training Data Synthesis via Formal Verification21 May 2025
- 2024Towards Large Language Model Aided Program Refinement26 Jun 2024
- 2024Learn from Failure: Fine-Tuning LLMs with Trial-and-Error Data for Intuitionistic Propositional Logic Proving10 Apr 2024
- 2024Towards Generalizable and Faithful Logic Reasoning over Natural Language via Resolution Refutation2 Apr 2024
- 2022Learning to Prove Trigonometric Identities14 Jul 2022
- 2021Neural Unification for Logic Reasoning over Natural Language17 Sep 2021