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

[NeurIPS 2025] The official repo of SynLogic: Synthesizing Verifiable Reasoning Data at Scale for Learning Logical Reasoning and Beyond

Repo for "Large Language Model Reasoning Failures"

On Memorization of Large Language Models in Logical Reasoning

Logic Circuits from the Juice library

Official Repository: A Comprehensive Benchmark for Logical Reasoning in MLLMs

Evaluation on Logical Reasoning and Abstract Reasoning Challenges

[ICML 2023] Answering Complex Logical Queries on Knowledge Graphs via Query Computation Tree Optimization

[SIGIR 2022] The implementation of Logiformer

Official code for "Divide and Translate: Compositional First-Order Logic Translation and Verification for Complex Logical Reasoning", ICLR 2025.

[EMNLP 2024] A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners

[AAAI 2023] Official resources of "NQE: N-ary Query Embedding for Complex Query Answering over Hyper-relational Knowledge Graphs".

Repo for paper "IDOL: Indicator-oriented Logic Pre-training for Logical Reasoning" accepted to the Findings of ACL 2023

The source code for Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning. #1 on the ReClor Leaderboard and we are the first group scored above 90% on the hidden test set around the world. The paper has been accepted by the Findings of ACL-24.

[NeurIPS2023] LoRA: A Logical Reasoning Augmented Dataset for Visual Question Answering

Understanding Expressivity of GNN in Rule Learning. ICLR 2024

An evaluation dataset comprising of 274 grid-based puzzles with different complexities

A Python-based logical reasoning system combining rule-based engines with Large Language Models (LLMs) for business automation. Supports deduction and hypothesis testing, partial fact completion, and automated fact retrieval via documents or chat. Features include explainable reasoning, workflow integration, and customizable knowledge bases.

[EMNLP 2025 Main] LogicTree: Structured Proof Exploration for Coherent and Rigorous Logical Reasoning with Large Language Models

This repository is a comprehensive collection of aptitude questions and exercises designed specifically for placement preparation and competitive exams.

The source code for #5 in the Logical Reasoning Reading Comprehension Leaderboard `ReClor`.

A sophisticated semantic network system capable of encoding inference rules within the network itself. Built for efficient memory usage and powerful logical reasoning, zelph can process the entire Wikidata knowledge graph (1.7TB) to detect contradictions and make logical deductions.

This repo is open to all who want to prepare for java DSA+Interview preparation .👍

A Reverse Approach to Training Reading Comprehension and Logical Reasoning Models

Episteme is a persistent epistemic reasoning system with non-monotonic logic, explicit conflict detection, belief revision, and quantitative belief lifecycle management layered on top of symbolic inference.

My Notes On Mathematical thinking fundamentals Course By Dr. Keith Devlin for developers and problem-solvers. Master logical reasoning, proof techniques, and mathematical language that transforms how you approach coding, algorithms, and complex problem-solving.