@inproceedings{zhang-etal-2025-osc,
    title = "{OSC}: Cognitive Orchestration through Dynamic Knowledge Alignment in Multi-Agent {LLM} Collaboration",
    author = "Zhang, Jusheng  and
      Fan, Yijia  and
      Cai, Kaitong  and
      Sun, Xiaofei  and
      Wang, Keze",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.335/",
    doi = "10.18653/v1/2025.findings-emnlp.335",
    pages = "6320--6337",
    ISBN = "979-8-89176-335-7",
    abstract = "This paper introduces OSC (Orchestrating Cognitive Synergy), a knowledge-aware adaptive collaboration framework designed to enhance cognitive synergy in multi-agent systems with large language models. While prior work has advanced agent selection and result aggregation, efficient linguistic interactions for deep collaboration among expert agents remain a critical bottleneck. OSC addresses this gap as a pivotal intermediate layer between selection and aggregation, introducing Collaborator Knowledge Models (CKM) to enable each agent to dynamically perceive its collaborators' cognitive states. Through real-time cognitive gap analysis, agents adaptively adjust communication behaviors, including content focus, detail level, and expression style, using learned strategies. Experiments on complex reasoning and problem-solving benchmarks demonstrate that OSC significantly improves task performance and communication efficiency, transforming ``parallel-working individuals'' into a ``deeply collaborative cognitive team''."
}