https://github.com/BartoszPiotrowski/guiding-connection-tableau-by-RNNs
공식 코드Guiding Inferences in Connection Tableau by Recurrent Neural Networks
Papers with Code 아카이브
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/EleutherAI/math-lm
공식 코드https://github.com/Goedel-LM/Goedel-Prover
공식 코드Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving
https://github.com/IBM/LNN
공식 코드https://github.com/IBM/TRAIL
공식 코드A Deep Reinforcement Learning Approach to First-Order Logic Theorem Proving
https://github.com/Michiel29/ntp-release
공식 코드Neural Theorem Provers Do Not Learn Rules Without Exploration
https://github.com/NicolasAG/SGinPG
공식 코드Measuring Systematic Generalization in Neural Proof Generation with Transformers
https://github.com/RickySkywalker/TheoremLlama
공식 코드TheoremLlama: Transforming General-Purpose LLMs into Lean4 Experts
https://github.com/Zhang-Liao/ilp_coq
공식 코드Learning Rules Explaining Interactive Theorem Proving Tactic Prediction
https://github.com/ai4reason/ATP_Proofs
공식 코드https://github.com/ai4reason/isa_enigma_paper
공식 코드https://github.com/albertqjiang/INT
공식 코드INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving
https://github.com/albertqjiang/draft_sketch_prove
공식 코드Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs
https://github.com/alexgaskell10/lava
공식 코드Logically Consistent Adversarial Attacks for Soft Theorem Provers
https://github.com/ali-forootani/a-survey-on-mathematical-reasoning-and-optimization-with-large-language-models
공식 코드A Survey on Mathematical Reasoning and Optimization with Large Language Models
https://github.com/barakeel/synthesis_datasets
공식 코드Deep Reinforcement Learning for Synthesizing Functions in Higher-Order Logic
https://github.com/car-pe/hagbp
공식 코드https://github.com/chuanyang-zheng/lyra-theorem-prover
공식 코드Lyra: Orchestrating Dual Correction in Automated Theorem Proving
https://github.com/cmu-l3/minictx-eval
공식 코드https://github.com/cyzhh/MMOS
공식 코드An Empirical Study of Data Ability Boundary in LLMs' Math Reasoning
https://github.com/darpa-scify/withdrarxiv
공식 코드https://github.com/deepseek-ai/deepseek-prover-v1.5
공식 코드https://github.com/deepseek-ai/deepseek-prover-v2
공식 코드https://github.com/dodo47/cyberml
공식 코드An energy-based model for neuro-symbolic reasoning on knowledge graphs
https://github.com/dwhalen/holophrasm
공식 코드Holophrasm: a neural Automated Theorem Prover for higher-order logic
https://github.com/eleanor-h/mustard
공식 코드MUSTARD: Mastering Uniform Synthesis of Theorem and Proof Data
https://github.com/eleutherai/gpt-neox
공식 코드ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics
https://github.com/ezelikman/parsel
공식 코드Parsel: Algorithmic Reasoning with Language Models by Composing Decompositions
https://github.com/facebookresearch/lawt
공식 코드https://github.com/facebookresearch/minif2f
공식 코드Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs
https://github.com/fveler/fvel
공식 코드FVEL: Interactive Formal Verification Environment with Large Language Models via Theorem Proving
https://github.com/g-pichler/in_feasibility_of_ml_backdoor_detection
공식 코드On the (In)feasibility of ML Backdoor Detection as an Hypothesis Testing Problem
https://github.com/gpoesia/minimo
공식 코드https://github.com/gpoesia/peano
공식 코드https://github.com/haoxiongliu/proofaug
공식 코드Efficient Neural Theorem Proving via Fine-grained Proof Structure Analysis
https://github.com/haoyuzhao123/leanineqcomp
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.