{"ID":2849695,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.23447","arxiv_id":"2510.23447","title":"Model Proficiency in Centralized Multi-Agent Systems: A Performance Study","abstract":"Autonomous agents are increasingly deployed in dynamic environments where their ability to perform a given task depends on both individual and team-level proficiency. While proficiency self-assessment (PSA) has been studied for single agents, its extension to a team of agents remains underexplored. This letter addresses this gap by presenting a framework for team PSA in centralized settings. We investigate three metrics for centralized team PSA: the measurement prediction bound (MPB), the Kolmogorov-Smirnov (KS) statistic, and the Kullback-Leibler (KL) divergence. These metrics quantify the discrepancy between predicted and actual measurements. We use the KL divergence as a reference metric since it compares the true and predictive distributions, whereas the MPB and KS provide efficient indicators for in situ assessment. Simulation results in a target tracking scenario demonstrate that both MPB and KS metrics accurately capture model mismatches, align with the KL divergence reference, and enable real-time proficiency assessment.","short_abstract":"Autonomous agents are increasingly deployed in dynamic environments where their ability to perform a given task depends on both individual and team-level proficiency. While proficiency self-assessment (PSA) has been studied for single agents, its extension to a team of agents remains underexplored. This letter addresse...","url_abs":"https://arxiv.org/abs/2510.23447","url_pdf":"https://arxiv.org/pdf/2510.23447v1","authors":"[\"Anna Guerra\",\"Francesco Guidi\",\"Pau Closas\",\"Davide Dardari\",\"Petar M. Djuric\"]","published":"2025-10-27T15:48:14Z","proceeding":"stat.AP","tasks":"[\"stat.AP\",\"cs.MA\"]","methods":"[]","has_code":false}