General Collaborative Intelligence: Architecting Cognition for Resilient Multi-Agent Ecosystems

原文：General Collaborative Intelligence: Architecting Cognition for Resilient Multi-Agent Ecosystems

作者：Lei Zhang, Chun Ye, Le Yang, Zhaozhong Wang, Deng-Ping Fan, Hang Dai, Binglu Wang

来源：arXiv cs.AI（人工智能）

正文

Computer Science > Computer Vision and Pattern Recognition

arXiv:2609.22967v1 (cs)

[Submitted on 19 Sep 2026]

Title:General Collaborative Intelligence: Architecting Cognition for Resilient Multi-Agent Ecosystems

Authors:Lei Zhang, Chun Ye, Le Yang, Zhaozhong Wang, Deng-Ping Fan, Hang Dai, Binglu Wang

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Abstract:Multi-agent unmanned systems are moving from isolated, ego-centric sensing toward collaborative intelligence, in which distributed agents exchange compact features to overcome a local observation trap that no single agent can escape: occlusions, finite sensor range, and environmental degradation. The field has matured across architectural, communication, embodied, resilience, and trust dimensions, yet existing surveys examine these dimensions in isolation and rarely expose their dependencies. This review offers a unified synthesis through two complementary lenses. The first is a five-dimensional taxonomy spanning collaboration stage, communication paradigm, fusion architecture, learning strategy, and application domain. The second is three cognitive synergy conditions, Semantic Disambiguation, Pragmatic Information Exchange, and Proactive Informational Foraging, that turn cognitive synergy into operational criteria. Across these lenses we survey collaboration architectures and topologies, neural-communication co-design that treats the channel as a differentiable pipeline component, embodied action-perception loops via multi-agent reinforcement learning, and resilience mechanisms for synchronization, uncertainty quantification, and label-efficient learning. We then map these advances onto four operational domains, V2X, unmanned aerial, industrial logistics, and smart cities, and onto the safety-privacy-utility triad. To counter benchmark saturation and evaluation fragmentation, we propose GCI-Bench, a five-pillar scoring protocol with a maturity model that makes the trade-offs of collaborative methods comparable across studies. A critical reflection on reproducibility, the sim-to-real gulf, and conditions under which collaboration degrades performance identifies open challenges and charts directions toward general collaborative intelligence under real-world uncertainty.

Subjects:

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Robotics (cs.RO)

Cite as:

arXiv:2609.22967 [cs.CV]

(or

arXiv:2609.22967v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2609.22967

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arXiv-issued DOI via DataCite (pending registration)

主题

计算机视觉 · 人工智能 · 机器人

由「前沿雷达」于 2026-09-19 采集。正文取自原文页面，已保留出处链接。标题与正文版权归原作者所有。