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📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG
Graph-Native Infrastructure for Context and Accountable AI Systems
A visual playground for agentic workflows: Iterate over your agents 10x faster
Implement a reasoning LLM in PyTorch from scratch, step by step
[ICLR 2023] ReAct: Synergizing Reasoning and Acting in Language Models
Skywork-R1V is an advanced multimodal AI model series developed by Skywork AI, specializing in vision-language reasoning.
Fully open data curation for reasoning models
拼好RAG:手搓并融合了GraphRAG、LightRAG、Neo4j-llm-graph-builder进行知识图谱构建以及搜索;整合DeepSearch技术实现私域RAG的推理;自制针对GraphRAG的评估框架| Integrate GraphRAG, LightRAG, and Neo4j-llm-graph-builder for knowledge graph construction and search. Combine DeepSearch for private RAG reasoning. Create a custom evaluation framework for GraphRAG.
WFGY is heading toward WFGY 5.0 Polaris Protocol, a major open-source release for AI reasoning, RAG, agents, and real-world workflows. Includes Problem Map, Global Debug Card, WFGY 4.0, and the CFV Easter Egg.
[NeurIPS 2025] 🌐 WebThinker: Empowering Large Reasoning Models with Deep Research Capability
Engineering decisions engine that know when they're stale. Frame, compare, decide — with evidence decay and parity enforcement. For Claude Code, Cursor, Gemini CLI, Codex and more.
🔍 Search-o1: Agentic Search-Enhanced Large Reasoning Models [EMNLP 2025]
LLMs can generate feedback on their work, use it to improve the output, and repeat this process iteratively.
MathCode: A Frontier Mathematical Coding Agent
MedReason: Eliciting Factual Medical Reasoning Steps in LLMs via Knowledge Graphs
AI-native ontology engine: a Rust MCP server with tools for building, validating, querying, and reasoning over RDF/OWL ontologies. In-memory Oxigraph triple store, native OWL2-DL tableaux reasoner, SHACL validation, SPARQL, versioning. Single binary, no JVM.
Advanced cognitive reasoning MCP server — DAG thought graph, 10 strategies, metacognition, self-critique, knowledge integration, and pruning
Advanced cognitive reasoning MCP server — DAG thought graph, 10 strategies, metacognition, self-critique, knowledge integration, and pruning
Verified knowledge for AI agents. Compress context, extract and store facts, define rules, and ask questions — get deterministic answers with proof, not LLM guesses. Connect agents via MCP, Python SDK, TypeSc