发表机构
Peking University（北京大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SimpleWikiSearch是一套明确可运行的离线维基百科环境，用于智能体搜索的可复现评估，提供基线结果及对比闭源模型的子集，而非提出新智能体算法。
AI 中文摘要
基于大语言模型（LLM）的智能体搜索系统评估时，常仅将底层LLM视为唯一关键组件，但实测性能还取决于周边搜索环境，包括维基百科快照、预处理流程、分块策略、检索后端、工具架构、观测格式及答案提交规则。这些细节常未明确说明，导致难以比较结果或复现报告的基线。本文提出SimpleWikiSearch，其语料构建、检索栈、工具契约及评估协议均明确且可运行。该环境从完整英文维基百科转储文件出发，清洗并分块语料，构建关键词及密集检索索引，提供含search、open_url、submit_answer的极简工具接口。我们使用开源LLM在6个问答数据集上报告基线结果，并提供随机300子集用于与闭源商业模型比较。SimpleWikiSearch为可复现的智能体搜索评估提供了领域专用智能体框架及受控离线环境，其贡献在于这套明确的参考设置，而非新智能体算法，代码与数据将在this https URL发布。
英文摘要
Large language model (LLM)-based agentic search systems are often evaluated as if the underlying LLM were the only component that matters, yet their measured performance also depends on the surrounding search environment: the Wikipedia snapshot, preprocessing pipeline, chunking policy, retrieval backend, tool schema, observation format, and answer submission rule. These details are frequently under-specified, making it difficult to compare results or reproduce reported baselines. We present SimpleWikiSearch, whose corpus construction, retrieval stack, tool contract, and evaluation protocol are explicit and runnable. The environment starts from a full English Wikipedia dump, cleans and chunks the corpus, builds keyword and dense retrieval indexes, and exposes a minimal tool interface consisting of \texttt{search}, \texttt{open\_url}, and \texttt{submit\_answer}. We report baseline results on six QA datasets using open-source LLMs and provide a random-300 subset for comparisons with closed-source commercial models. SimpleWikiSearch provides a domain-specific agent harness and a controlled offline environment for reproducible agentic-search evaluation. Its contribution is this specified reference setup, rather than a new agent algorithm. Code and data will be available at: https://github.com/JimXiongGM/simple_wiki_search.
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