Xnhyacinth/Awesome-LLM-Long-Context-Modeling

• highreadme#1

  Reposition README opening to clarify it's a curated list

  原因：

  当前

  This repository includes papers and blogs about Efficient Transformers, KV Cache, Length Extrapolation, Long-Term Memory, Retrieval-Augmented Generation (RAG), Compress, Long Text Generation, Long Video, Long CoT and Evaluation for Long Context Modeling.

  复制粘贴的修复

  复制

  This repository is a curated collection of must-read papers and blogs on Large Language Model based Long Context Modeling, covering Efficient Transformers, KV Cache, Length Extrapolation, Long-Term Memory, Retrieval-Augmented Generation (RAG), Compress, Long Text Generation, Long Video, Long CoT and Evaluation.

  待办完成忽略
• mediumhomepage#2

  Update homepage to point to the repository itself

  原因：

  当前

  https://arxiv.org/abs/2503.17407

  复制粘贴的修复

  复制

  https://github.com/Xnhyacinth/Awesome-LLM-Long-Context-Modeling

  待办完成忽略
• lowreadme#3

  Remove empty markdown links from README

  原因：

  当前

  <div align="center">
  
  [](https://github.com/Xnhyacinth/Awesome-LLM-Long-Context-Modeling/blob/main/LICENSE)
  
  [](https://github.com/Xnhyacinth/Long_Text_Modeling_Papers/commits/main)
  [](https://github.com/Xnhyacinth/Long_Text_Modeling_Papers/pulls)
  [](https://github.com/Xnhyacinth/Awesome-LLM-Long-Context-Modeling)
  
  </div>

  复制粘贴的修复

  复制

  Remove all instances of `[]()` from the README, such as `[](https://github.com/Xnhyacinth/Awesome-LLM-Long-Context-Modeling/blob/main/LICENSE)`.

  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

  Where can I find a comprehensive list of research papers on large language model long context?

  你：未被推荐
  AI 推荐顺序：

  1. arXiv.org
  2. Google Scholar
  3. Papers With Code
  4. Semantic Scholar
  5. Hugging Face Blog/Research Posts
  6. The Batch by DeepLearning.AI
  7. Import AI by Jack Clark

  AI 推荐了 7 个替代方案，却始终没点名 Xnhyacinth/Awesome-LLM-Long-Context-Modeling。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  What techniques are available for improving long-term memory and context handling in LLMs?

  你：未被推荐
  AI 推荐顺序：

  1. Pinecone
  2. Weaviate (weaviate/weaviate)
  3. Chroma (chroma-core/chroma)
  4. FAISS (facebookresearch/faiss)
  5. LangChain (langchain-ai/langchain)
  6. LlamaIndex (run-llama/llama_index)
  7. Claude 3 Opus/Sonnet/Haiku
  8. GPT-4 Turbo
  9. Gemini 1.5 Pro
  10. Neo4j (neo4j/neo4j)
  11. Vaticle's TypeDB (vaticle/typedb)
  12. OpenAI Fine-tuning API
  13. Hugging Face Transformers (huggingface/transformers)

  AI 推荐了 13 个替代方案，却始终没点名 Xnhyacinth/Awesome-LLM-Long-Context-Modeling。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 Xnhyacinth/Awesome-LLM-Long-Context-Modeling?

  pass
  ✓AI 明确点名了 Xnhyacinth/Awesome-LLM-Long-Context-Modeling

  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 Xnhyacinth/Awesome-LLM-Long-Context-Modeling in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 Xnhyacinth/Awesome-LLM-Long-Context-Modeling

  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 Xnhyacinth/Awesome-LLM-Long-Context-Modeling solve, and who is the primary audience?

  pass
  ⚠AI 未点名 Xnhyacinth/Awesome-LLM-Long-Context-Modeling —— 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？