arXiv Preprint: Kolab Benchmark Shows Frontier AI Models Overestimate Collaboration Quality with Experts
The newly released Kolab benchmark indicates that current frontier AI models generally overestimate their effectiveness in collaborating with humans during long-horizon professional workflows and struggle to distinguish between high- and low-quality collaboration.
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A new benchmark called Kolab has found that current frontier AI models generally overestimate their effectiveness when collaborating with human experts on long-horizon tasks, and struggle to distinguish between high- and low-quality collaboration processes.
Most existing AI agent benchmarks focus on testing a model's ability to complete specific tasks independently (automation). However, real professional workflows often involve interdependent steps, where humans place greater importance on ongoing interaction with AI agents (such as revising plans) than on single-attempt success rates. This gap in evaluation perspective leads to misjudgments of model performance in complex collaborative scenarios.
Researchers collected over 115 hours of screen recordings from 50 professionals across 40 occupations collaborating with AI agents on realistic, long-horizon problems, along with participants' judgments of the collaboration process and output quality. The analysis reveals that frontier models, whether agentic or non-agentic, fail to accurately predict whether experts will adopt, modify, or reject their contributions, and cannot assess collaboration quality without constant expert feedback. Furthermore, providing longer workflow context does not consistently improve these predictions.
This finding is currently based solely on the paper's abstract; specific quantitative metrics and experimental details require reading the full text. As a preprint, it has not yet undergone peer review or independent reproduction.