RManLuo/reasoning-on-graphs

• highreadme#1

  Reposition README's opening to highlight RoG as a framework/methodology

  原因：

  当前

  Official Implementation of "Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning".
  
  Reasoning on graphs (RoG) synergizes LLMs with KGs to enable faithful and interpretable reasoning. We present a planning-retrieval-reasoning framework, where RoG first generates relation paths grounded by KGs as faithful plans. These plans are then used to retrieve valid reasoning paths from the KGs for LLMs to conduct faithful reasoning and generate interpretable results.

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  Reasoning on Graphs (RoG) is a novel framework and the official implementation of our ICLR 2024 paper, designed to enable faithful and interpretable reasoning by synergizing Large Language Models (LLMs) with Knowledge Graphs (KGs). RoG provides a concrete planning-retrieval-reasoning methodology to ground LLM outputs with KG-derived paths, offering a robust solution for enhanced LLM reasoning.

  待办完成忽略
• mediumtopics#2

  Add more descriptive topics to clarify the repo's function

  原因：

  当前

  kg, knowledge, large-language-models, llm, reasoning, reasoning-on-graph

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  kg, knowledge, large-language-models, llm, reasoning, reasoning-on-graph, llm-framework, knowledge-graph-integration, interpretable-llm, nlp-framework

  待办完成忽略
• mediumreadme#3

  Add a "Key Features" section to highlight differentiators

  原因：

  复制粘贴的修复

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  ## Key Features
  
  *   **Faithful Reasoning:** RoG grounds LLM outputs with verifiable paths extracted directly from Knowledge Graphs, ensuring factual accuracy.
  *   **Interpretable Results:** Our planning-retrieval-reasoning framework provides clear, traceable reasoning steps, making LLM decisions transparent.
  *   **Concrete Methodology:** Implements a structured approach for integrating LLMs and KGs, moving beyond generic RAG.
  *   **Official ICLR 2024 Implementation:** Provides reproducible code and pre-trained weights for cutting-edge research in LLM-KG synergy.

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  How to integrate knowledge graphs with large language models for more interpretable reasoning?

  你：未被推荐
  AI 推荐顺序：

  1. LangChain
  2. Neo4j
  3. RDFLib
  4. LlamaIndex
  5. Amazon Neptune
  6. TypeDB
  7. TypeDB Client
  8. OpenNARS
  9. PyTorch Geometric (PyG)
  10. Deep Graph Library (DGL)
  11. AmpliGraph
  12. OpenKE

  AI 推荐了 12 个替代方案，却始终没点名 RManLuo/reasoning-on-graphs。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  Seeking a framework to ground large language model reasoning with external knowledge graphs.

  你：未被推荐
  AI 推荐顺序：

  1. LangChain
  2. LlamaIndex
  3. Haystack
  4. GraphRAG
  5. Neo4j AuraDS
  6. Graph Data Science Library
  7. Amazon Neptune
  8. ArangoDB
  9. TigerGraph
  10. RDFLib
  11. transformers

  AI 推荐了 11 个替代方案，却始终没点名 RManLuo/reasoning-on-graphs。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  pass

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of RManLuo/reasoning-on-graphs?

  pass
  ✓AI 明确点名了 RManLuo/reasoning-on-graphs

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts RManLuo/reasoning-on-graphs in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 RManLuo/reasoning-on-graphs

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo RManLuo/reasoning-on-graphs solve, and who is the primary audience?

  pass
  ⚠AI 未点名 RManLuo/reasoning-on-graphs —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？