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Automated Theorem Proving
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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
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/openai/minif2f
공식 코드MiniF2F: a cross-system benchmark for formal Olympiad-level mathematics
https://github.com/princeton-vl/CoqGym
공식 코드Learning to Prove Theorems via Interacting with Proof Assistants
https://github.com/princeton-vl/FormulaNet
공식 코드Premise Selection for Theorem Proving by Deep Graph Embedding
https://github.com/princeton-vl/MetaGen
공식 코드https://github.com/princeton-vl/MetaQNL.jl
공식 코드Learning Symbolic Rules for Reasoning in Quasi-Natural Language
https://github.com/princeton-vl/metaqnl
공식 코드Learning Symbolic Rules for Reasoning in Quasi-Natural Language
https://github.com/rickyskywalker/leanofthought-official
공식 코드MA-LoT: Multi-Agent Lean-based Long Chain-of-Thought Reasoning enhances Formal Theorem Proving
https://github.com/sileod/unigram
공식 코드Scaling Synthetic Logical Reasoning Datasets with Context-Sensitive Declarative Grammars
https://github.com/some-random/theorem-proving-reasoning
공식 코드https://github.com/stanford-centaur/pypantograph
공식 코드https://github.com/tptpworld/nonclassicallogic
공식 코드Solving Quantified Modal Logic Problems by Translation to Classical Logics
https://github.com/trishullab/copra
공식 코드https://github.com/trishullab/itp-interface
공식 코드ProofWala: Multilingual Proof Data Synthesis and Theorem-Proving
https://github.com/trishullab/proof-wala
공식 코드ProofWala: Multilingual Proof Data Synthesis and Theorem-Proving
https://github.com/trishullab/putnam
공식 코드PutnamBench: Evaluating Neural Theorem-Provers on the Putnam Mathematical Competition
https://github.com/trishullab/putnambench
공식 코드PutnamBench: Evaluating Neural Theorem-Provers on the Putnam Mathematical Competition
https://github.com/txc701/open-source-code-for-ai4m
공식 코드Mathematical Formalized Problem Solving and Theorem Proving in Different Fields in Lean 4
https://github.com/ucsd-atp/propl
공식 코드https://github.com/wclsn/Alchemy
공식 코드Alchemy: Amplifying Theorem-Proving Capability through Symbolic Mutation
https://github.com/wellecks/llmstep
공식 코드https://github.com/wellecks/naturalproofs
공식 코드NaturalProofs: Mathematical Theorem Proving in Natural Language
https://github.com/wellecks/naturalprover
공식 코드NaturalProver: Grounded Mathematical Proof Generation with Language Models
https://github.com/wiio12/LEGO-Prover
공식 코드https://github.com/wiio12/poetry
공식 코드https://github.com/zhangir-azerbayev/proofnet
공식 코드ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics
https://github.com/zhaoxlpku/subgoalxl
공식 코드SubgoalXL: Subgoal-based Expert Learning for Theorem Proving
https://github.com/zhaoyu-li/dl4tp
공식 코드https://github.com/zsoltzombori/lemma
공식 코드https://github.com/zsoltzombori/plcop
공식 코드https://gitlab.com/mcoavoux/statement_proof_matching
공식 코드https://github.com/01mf02/notes
https://github.com/BartoszPiotrowski/ATPboost
https://github.com/HEPLean/HepLean
https://github.com/IBM/LogicalFormulaEmbedder
Improving Graph Neural Network Representations of Logical Formulae with Subgraph Pooling
https://github.com/JUrban/deepmath
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.