# Grep 真的就够了吗？代理框架如何重塑基于代理的搜索

- 来源：Hacker News 热门（buzzing.cc 中文翻译）
- 作者：Anon84
- 发布时间：2026-06-10 05:13
- AIHOT 分数：45
- AIHOT 链接：https://aihot.virxact.com/items/cmq75yayd0039sl5waxiocvnn
- 原文链接：https://arxiv.org/abs/2605.15184

## AI 摘要

该研究质疑在基于代理的搜索（agentic search）中“grep 是否足够”这一假设，并分析代理框架（agent harnesses）如何重新定义智能体搜索的交互方式与能力边界，推动搜索范式从简单工具调用向结构化代理行为演进。

## 正文

Computer Science > Computation and Language

Title:Is Grep All You Need? How Agent Harnesses Reshape Agentic Search

Abstract:Recent advances in Large Language Model (LLM) agents have enabled complex agentic workflows where models autonomously retrieve information, call tools, and reason over large corpora to complete tasks on behalf of users. Despite the growing adoption of retrieval-augmented generation (RAG) in agentic search systems, existing literature lacks a systematic comparison of how retrieval strategy choice interacts with agent architecture and tool-calling paradigm. Important practical dimensions, including how tool outputs are presented to the model and how performance changes when searches must cope with more irrelevant surrounding text, remain under-explored in agent loops. This paper reports an empirical study organized into two experiments. Experiment 1 compares grep and vector retrieval on a 116-question sample from LongMemEval, using a custom agent harness (Chronos) and provider-native CLI harnesses (Claude Code, Codex, and Gemini CLI), for both inline tool results and file-based tool results that the model reads separately. Experiment 2 compares grep-only and vector-only retrieval while progressively mixing in additional unrelated conversation history, so that each query is embedded in more distracting material alongside the passages that matter. Across Chronos and the provider CLIs, grep generally yields higher accuracy than vector retrieval in our comparisons in experiment 1; at the same time, overall scores still depend strongly on which harness and tool-calling style is used, even when the underlying conversation data are the same.

Subjects: Computation and Language (cs.CL) Cite as: arXiv:2605.15184 [cs.CL] (or arXiv:2605.15184v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2605.15184 Focus to learn more arXiv-issued DOI via DataCite

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