# EvoBrowseComp：基于动态知识的搜索智能体评测基准

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-11 08:00
- AIHOT 分数：69
- AIHOT 链接：https://aihot.virxact.com/items/cmqaef40q0kqnslldkjz0id8i
- 原文链接：https://arxiv.org/abs/2606.13120

## AI 摘要

EvoBrowseComp 提出包含 400 英文和 400 中文无污染复杂问题的动态基准，问题通过实时网络遍历合成。其采用三智能体协作框架：QA 合成智能体从实时网页检索知识生成问答对；信息过滤智能体按可信度和流行度过滤以阻止参数捷径；高层指导智能体将问题形式化为推理图减少逻辑冗余。该框架支持自动合成与定期更新，防止污染并保持时效性。实验表明该基准难度极高，需广泛横向搜索能力，为可自动更新的高难度评测建立了可扩展范式。

## 正文

Search Agents -- large language models augmented with search tools -- have intensified the need for future-proof evaluation benchmarks. Existing benchmarks such as BrowseComp rely on static knowledge, making them vulnerable to test-set contamination and parametric memorization. Consequently, models can achieve high scores through fact recall rather than genuine retrieval, obscuring true browsing competence via reasoning shortcuts. In this paper, we introduce EvoBrowseComp, an evolving benchmark of 400 English and 400 Chinese contamination-free complex questions synthesized via live-web traversal. To collect these questions, we design a three-agent collaborative framework: (1) a QA synthesis agent that retrieves fresh knowledge from the live web to synthesize QA pairs; (2) an information filtering agent that filters retrieved knowledge in terms of credibility and popularity to block parametric shortcuts; and (3) a high-level guidance agent that formalizes questions into reasoning graphs to reduce logical redundancy and shortcuts in synthesized QA pairs. Because the framework supports fully automated synthesis, EvoBrowseComp can be regularly updated to prevent data contamination and maintain temporal freshness. Extensive experiments confirm its great difficulty, requiring broad horizontal search. It establishes a scalable paradigm for auto-updatable, high-difficulty benchmarking that keeps pace with both evolving world knowledge and advancing agent capabilities.