arXiv:2606.13120v1 Announce Type: new Abstract: 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 co
The rapid development and deployment of LLM-based search agents necessitate robust, future-proof evaluation methodologies to prevent models from gaming benchmarks through memorization rather than true competence.
Accurate benchmarking of AI agents is crucial for guiding development, ensuring real-world utility, and preventing overestimation of capabilities based on static, contaminated datasets. This directly impacts the trajectory of AI agent deployment.
The introduction of EvoBrowseComp establishes a dynamic, contamination-free benchmark for evaluating AI agent browsing capabilities, shifting the focus from static knowledge recall to genuine retrieval and reasoning in evolving environments.
- · AI Agent developers prioritizing genuine browse competence
- · Evaluators and researchers of AI agents
- · Users relying on accurate AI agent performance
- · AI models optimized for static, contaminated benchmarks
- · Developers focused on 'test-set memorization' shortcuts
- · Benchmarks relying solely on static knowledge
AI models will be forced to adapt to more robust and dynamic evaluation criteria, pushing for genuine browsing and reasoning capabilities.
This improved evaluation could accelerate the development of more capable and reliable AI agents, expanding their application in complex, information-rich tasks.
More competent AI agents could lead to significant productivity gains in information work, potentially accelerating the automation of tasks requiring dynamic information retrieval.
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Read at arXiv cs.CL