Unified Multimodal Cognitive Architecture (UMCA): an Integrated Framework for Perception, Reasoning, and Action for High-Stakes Environments

Abstract

Existing artificial intelligence systems, such as powerful Large Language Models (LLMs), Vision-Language Models (VLMs), and specialized tools like the Segment Anything Model (SAM), have made remarkable progress in specific domains. [1] However, in high-stakes environments marked by volatility, uncertainty, complexity, and ambiguity (VUCA), these architectures are unsuitable for dynamic multimodal data integration and the generation of auditable, actionable results. Traditional generalist agents, such as DeepMind's Gato and Google's Gemini, have attempted to unify these capabilities, but they frequently lack the explicit safety mechanisms and fine-grained control required for critical applications. [6]

This paper presents the Unified Multimodal Cognitive Architecture (UMCA), a novel framework that integrates perception, reasoning, and action into a single, end-to-end pipeline. The architecture is built around three key innovations: a Latent Concept Model (LCM) for deep cross-modal alignment, a dynamic Mixture-of-Experts (MoE) routing layer for adaptive, resource-aware computation, and a Language-Action Model (LAM) for creating structured, verifiable action graphs. We demonstrate UMCA's superior performance by conducting extensive benchmarks on a variety of crisis response tasks, such as multimodal question answering, image-grounded summarization, and resource routing. Our comparative and ablation studies formally validate the importance of each architectural component, demonstrating that UMCA outperforms cutting-edge baselines by a significant margin. The UMCA framework represents a viable path to developing robust, explainable, and ethically grounded AI systems for high-stakes societal applications, directly supporting the global collaboration principles outlined in Sustainable Development Goal 17 (SDG-17).