KAG is a logical form-guided reasoning and retrieval framework based on OpenSPG engine and LLMs. It is used to build logical reasoning and factual Q&A solutions for professional domain knowledge bases. It can effectively overcome the shortcomings of the traditional RAG vector similarity calculation model.
The Art of Problem-Solving in Software Engineering: How to Make MySQL Better
[ACL 2023] Reasoning with Language Model Prompting: A Survey
Recent Papers including Neural Symbolic Reasoning, Logical Reasoning, Visual Reasoning, planning and any other topics connecting deep learning and reasoning
Inductive relation prediction by subgraph reasoning, ICML'20
Repo for "Large Language Model Reasoning Failures"
[NeurIPS 2025] The official repo of SynLogic: Synthesizing Verifiable Reasoning Data at Scale for Learning Logical Reasoning and Beyond
On Memorization of Large Language Models in Logical Reasoning
This repository is a comprehensive collection of aptitude questions and exercises designed specifically for placement preparation and competitive exams.
Logic Circuits from the Juice library
Official Repository: A Comprehensive Benchmark for Logical Reasoning in MLLMs
Official code for "Divide and Translate: Compositional First-Order Logic Translation and Verification for Complex Logical Reasoning", ICLR 2025.
Evaluation on Logical Reasoning and Abstract Reasoning Challenges
[ICML 2023] Answering Complex Logical Queries on Knowledge Graphs via Query Computation Tree Optimization
[EMNLP 2024] A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners
[SIGIR 2022] The implementation of Logiformer
行测复盘一体版｜公务员考试（公考）行测 AI Skill，支持讲题、错题复盘、方法训练、题库管理与复习
[AAAI 2023] Official resources of "NQE: N-ary Query Embedding for Complex Query Answering over Hyper-relational Knowledge Graphs".
A comprehensive, structured study resource and practice suite for Quantitative Aptitude, Logical Reasoning, Data Interpretation, and Verbal Ability, featuring 340+ solved problems with step-by-step solutions and LaTeX formulas.
Repo for paper "IDOL: Indicator-oriented Logic Pre-training for Logical Reasoning" accepted to the Findings of ACL 2023
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