arXiv:2606.12871v1 Announce Type: new Abstract: Search Agents (SAs) typically leverage large language models (LLMs) to support complex information-seeking tasks by autonomously exploring web sources and synthesizing information into comprehensive responses. For SAs evaluation, prior benchmarks mainly focus on specialized tasks that are unlikely to arise in real-world user scenarios. Moreover, their reliance on coarse task-level rubrics often limits evaluation interpretability. To bridge this gap, we introduce DailyReport, an open-ended benchmark to evaluate SA capabilities on daily search task
The rapid advancement of large language models (LLMs) and their integration into agentic systems necessitates more rigorous and realistic evaluation methods for their practical applications.
A strategic reader should care because improved benchmarks for AI agents directly impact the speed of progress, reliability, and eventual deployment of autonomous systems in white-collar workflows.
The introduction of DailyReport provides an open-ended, real-world oriented framework for evaluating search agents, moving beyond specialized tasks and offering more interpretable metrics for performance.
- · AI agents developers
- · LLM companies
- · Enterprises adopting AI agents
- · Research institutions
- · Companies with outdated AI agent evaluation methods
- · Developers of less robust AI agent systems
More accurate and reliable AI agents will emerge due to better evaluation.
Increased adoption of AI agents could lead to significant automation in information-seeking and synthesis tasks across various industries.
This could accelerate the collapse of certain white-collar workflows and generate demand for new types of human-AI collaboration roles.
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Read at arXiv cs.AI