From Monolithic Models to Cognitive Hives: A Framework for Multimodal Reasoning Systems

Abstract

The increasing fast development of multimodal foundation models has greatly contributed to the development of artificial intelligence systems that have the ability to process and integrate text, vision, audio, and structured information in single architectures. Although they have been performing admirably, these models are becoming more limited in nature as they grow in size especially in performance regarding efficiency, transparency and flexibility. Existing monolithic multimodal architectures demand a lot of computational and financial resources, have inflexible structure of design, and are not that interpretable. Moreover, they also use implicit fusion processes that are blind to modal conflict and inconsistency detection and resolution. The given paper suggests another paradigm, known as cognitive hives, which views intelligent multimodal systems as distributed assemblies of specialised expert models instead of single end-to-end networks. The approach to multimodal reasoning, in this context, is viewed as an issue in distributed systems, in which autonomous domain-specific experts interact via shared representations and explicit coordination protocols. One of the main parts of the suggested architecture is a special conflict resolution layer that would identify, arbitrage, and clarify disputes between expert models. The major contributions that were made in this work are the formal definition of cognitive hive architecture, systematic approach on cross-modal conflict resolution, and analysis of deployment strategies, latency trade-offs and failure modes in distributed reasoning systems. The framework is proposed to be hardware-agnostic, modular and future-proof, and is a scalable and explainable alternative to monolithic multimodal models.