Cognitive Hives: A Distributed Systems Theory for Conflict-Aware Multimodal Intelligence

Abstract

The very recent developments in large-scale foundation models have shown impressive language, vision, and multimodal reasoning. But current architectures are still fundamentally monolithic and rely on centralized parameter scaling rather than structural distribution. Multimodal complexity heightens the constraints of monolithic systems with respect to their latency, interpretability, internal conflict management and scalability. The paper presents the idea of Cognitive Hives - a theory of distributed systems of conflict-aware multimodal intelligence. A Cognitive Hive is a system of special-purpose expert models that run with common temporal synchronization and defined conflict-arbitration rules. We define the architectural layers, communication semantics, arbitration functions, and temporal cohesion mechanisms required for stable distributed reasoning. The framework comprises a common time-based backbone, message relaying, and graphical conflict detection to enable scalable cooperative intelligence. We also examine infrastructure needs, relative structural features, and implications for the enterprise. Cognitive Hives signify a shift from scale-through-size to scale-through-structure and provide a principled approach to distributed artificial cognition.