Describes the shift of multi-agent unmanned systems from ego-centric sensing to collaborative intelligence through compact feature exchange that overcomes local observation limits.
General Collaborative Intelligence (GCI) represents a paradigm shift in multi-agent unmanned systems where distributed agents exchange compact neural feature tokens rather than raw data to overcome the local observation trap of occlusions, limited sensor range, and environmental degradation. This transition moves the field from deterministic geometric data fusion toward cognitive synergy, a state where agents act as pragmatic informational gatekeepers of a shared semantic manifold.
The GCI framework is operationalized through three core cognitive synergy conditions: Semantic Disambiguation: Resolving entity identity and intent beyond any single agent’s line-of-sight. Pragmatic Information Exchange: Transmitting only decision-relevant informational surprise to minimize communication load. * Proactive Informational Foraging: Actively reconfiguring sensing topology to reduce residual uncertainty.
To evaluate these capabilities, the research proposes GCI-Bench, a unified scoring protocol with a five-level maturity model ranging from isolated perception (L1) to trustworthy, self-optimizing cognitive systems (L5). This architecture enables heterogeneous fleets to pool sensory, temporal, and computational resources into a shared world model, allowing the collective to execute missions that are physically or informationally impossible for any isolated agent.
This material presents General Collaborative Intelligence (GCI) as a design paradigm for multi-agent unmanned systems that move beyond isolated, ego-centric sensing toward a collectively engineered form of cognition. Rather than treating each agent as an independent perception-and-control loop, the work frames the agent population as a distributed cognitive ecosystem in which local observations are fused through compact feature exchange—a lightweight, structured communication mechanism that transmits task-relevant latent information instead of raw sensor streams or brittle high-level commands. The central claim is that such exchange can overcome the fundamental limits of local observability, enabling agents to construct a richer, shared situational model than any single unit could form on its own.
A key insight is that collaborative intelligence can be made resilient and scalable by decoupling perception, inference, coordination, and action into a modular cognitive architecture. Compact features allow the system to preserve the most decision-relevant information while reducing communication bandwidth, latency, and exposure to channel degradation or partial network loss. This supports robust fusion under occlusion, sensor failure, asynchronous updates, and heterogeneous agent capabilities, while still permitting decentralized decision-making. In effect, the paper positions communication not merely as a data link, but as a cognitive medium that shapes collective awareness, uncertainty management, and adaptive task allocation.
The work matters because it addresses a practical bottleneck in modern multi-agent systems: how to coordinate many resource-constrained, partially observable agents in complex, dynamic, or contested environments. If successful, GCI-style architectures could improve persistent autonomy in domains such as aerial swarms, maritime teams, ground robot fleets, and distributed sensing networks, where no single agent has a global view and where bandwidth, energy, and reliability are limited. More broadly, the contribution is significant because it reframes multi-agent resilience as an architectural and representational problem—showing that compact, meaning-preserving collaboration can be a foundation for more capable and dependable autonomous ecosystems.