LATIDIA · Investigación

Repensar la colaboración multiagente: cuando más es menos

arXiv:2609.19759v1 Tipo de anuncio: nuevo Resumen: El rápido avance de los modelos de lenguaje grandes y los arneses de agente único ha remodelado el panorama de los sistemas autónomos, planteando una pregunta crítica sobre cuándo

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arXiv:2609.19759v1 Announce Type: new Abstract: The rapid advancement of large language models and single-agent harnesses has reshaped the landscape of autonomous systems, raising a critical question of when multi-agent collaboration offers genuine value. As individual agent capabilities continue to scale, multi-agent collaboration faces diminishing returns while incurring growing context overhead. Through systematic analysis, we delineate the capability boundaries of multi-agent collaboration relative to single-agent alternatives, showing that it confers systematic benefits specifically in long-horizon tasks with sparse dependencies, while single-agent harnesses remain superior in tightly coupled, sequential workflows. Building on these insights, we propose SAIGE, a lightweight multi-agent collaboration mechanism based on Semantic-Aware Incremental Graph Evolution. SAIGE models collaboration as