📄 中文摘要
LLM 代理在推理方面表现出色，但在数据导航上却存在不足。虽然它们能够分析代码、编写解决方案和调试问题，但在面对诸如“这个变更影响了什么？”或“我遗漏了什么？”的问题时，它们往往只能在平面文件中进行搜索。为了解决这一问题，m1nd 被开发出来。m1nd 通过激活数据而非简单搜索来工作，用户查询一个概念时，相关节点会被激活并发出衰减信号，系统能够过滤噪声并根据用户反馈进行学习。这种方法使得 LLM 代理能够更有效地理解和处理复杂的关系网络。
扩散激活如何超越关键字搜索为 LLM 代理
出处: How spreading activation beats keyword search for LLM agents
发布: 2026年3月12日
📄 English Summary
How spreading activation beats keyword search for LLM agents
LLM agents excel in reasoning but struggle with navigation through data. While they can analyze code, write solutions, and debug issues, they often find themselves searching through flat files when faced with questions like 'What does this change affect?' or 'What am I missing?'. To address this limitation, m1nd was developed. Instead of merely searching data, m1nd activates it. When a user queries a concept, connected nodes light up with decaying signals, noise is canceled out, and the system learns from user feedback. This approach enables LLM agents to better understand and manage complex relational networks.