arXiv:2605.27882v1 Announce Type: cross Abstract: LLM-based agents score well on search benchmarks, yet real users consistently find results unsatisfying, revealing a persistent evaluation-experience gap. We attribute this gap to existing benchmarks' reliance on over-specified queries, single-turn interactions, and fixed-schema evaluation, none of which reflect real search behavior where users and agents collaboratively refine vague intent through multi-turn dialogue. We term this paradigm VibeSearch and introduce VibeSearchBench, a benchmark comprising 200 manually curated bilingual (Chinese
The proliferation of LLM-based agents has exposed shortcomings in existing benchmarks, leading researchers to identify a persistent evaluation-experience gap that current methods fail to address.
This benchmark addresses a critical flaw in current AI agent evaluation, offering a more realistic assessment of performance in complex, multi-turn, and vague search scenarios, which is crucial for the development of truly useful AI agents.
The introduction of VibeSearchBench shifts the focus of AI agent evaluation from single-turn, over-specified queries to collaborative, multi-turn interactions, better reflecting real-world user behavior and agent capabilities.
- · AI agent developers
- · Search engine companies
- · Users of AI-powered search
- · Multilingual AI research
- · Developers relying on old benchmarks
- · Single-turn query optimization strategies
AI agents will become significantly more effective at understanding and fulfilling complex, nuanced user requests.
This improved understanding will accelerate the adoption of AI agents for tasks requiring iterative refinement and subjective interpretation.
The enhanced capability of agents in complex search could lead to a re-evaluation of information consumption patterns, favoring dynamically generated content over static results.
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Read at arXiv cs.AI