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General Collaborative Intelligence: Architecting Cognition for Resilient Multi-Agent Ecosystems — arXiv:2609.22967

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.22967
License (as archived): CC0-1.0
Archived: 2026-10-09T21:59:22.550Z
SHA-256 of the archived text: 10c4b93c1bc04a3673f8ef118719f12a97f7c83ad0de38bb2f5dfa64a23ea970
Stable URL of this page: https://metraipolis.com/documento/dc_abc19eccaa70

How to cite

General Collaborative Intelligence: Architecting Cognition for Resilient Multi-Agent Ecosystems — arXiv:2609.22967 — arXiv (en), https://arxiv.org/abs/2609.22967 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_abc19eccaa70 (Metraipolis archive dc_abc19eccaa70, sha256 10c4b93c1bc04a3673f8ef118719f12a97f7c83ad0de38bb2f5dfa64a23ea970)

BibTeX

@misc{dcabc19eccaa70,
  title = {General Collaborative Intelligence: Architecting Cognition for Resilient Multi-Agent Ecosystems — arXiv:2609.22967},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.22967},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_abc19eccaa70; sha256 10c4b93c1bc04a3673f8ef118719f12a97f7c83ad0de38bb2f5dfa64a23ea970},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.22967. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-22 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI); Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.22967 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-09T21:45:20.835Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-22?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).