AI RESEARCH
VibeSearchBench: Benchmarking Long-horizon Proactive Search in the Wild
arXiv CS.AI
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ArXi:2605.27882v1 Announce Type: cross 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.