这篇论文挑战了“语义搜索是Agent标配”的直觉，做Agent检索或RAG的开发者值得一读，看完可能会重新审视你的检索策略。
PwC发布论文《Is Grep All You Need?》，研究Agent搜索中不同检索工具的效果。他们让Agent同时使用向量搜索和grep，发现grep在准确率上普遍优于语义搜索。论文测试了多种Agent框架（包括Claude Code、Codex），但局限在于检索对象是对话记忆而非企业文档。作者认为Agent框架确实简化了检索问题，但仍有改进空间。
There’s an open question on whether grep is all you need for agentic search. This recent paper by @...
There’s an open question on whether grep is all you need for agentic search. This recent paper by @PwCUS (Sen et al.) seems to suggest that. It’s titled “Is Grep All You Need? How Agent Harnesses Reshape Agentic Search”. They test various agentic harnesses (in-house, Claude Code, Codex), and equip the agent with both vector search and grep. They find that grep generally yields higher accuracy than semantic search. IMO the main gap of the paper is that it tests retrieval over conversational memory, not over a real-world corpus of enterprise documents. Standard enterprise RAG setups involve asking complex questions over a static document corpus (e.g. 10-Ks, legal contracts, SOPs). The corpus here is per-user chat history, which is quite a different document distribution. I do think that evolving agentic harnesses simplify the problem of retrieval - hence the popularity with file sandboxes and a vector db is “just a database” - but IMO there’s still more work to be done here. Paper: arxiv.org/pdf/2605.15184 💬 2 🔄 2 ❤️ 7 👀 540 📊 5 ⚡