High-risk environments such as emergency response, industrial safety, and critical infrastructure monitoring generate complex, multimodal data streams. Traditional AI systems struggle with the latency demands, incomplete observations, and dynamic conditions present in these domains. This paper proposes an adaptive multimodal agent architecture that integrates sensor fusion, uncertainty modeling, and hierarchical decision layers to support fast, explainable recommendations. The agent combines a transformer-based fusion encoder with a Bayesian uncertainty head and a lightweight policy planner optimized for real-time execution on edge devices. We introduce an adaptive gating mechanism that activates only the relevant modality experts based on data quality and environmental context, significantly reducing computation. Experiments use three public datasets and two simulated emergency scenarios (chemical leak detection and structural collapse risk). Results show that our system improves inference speed by 28–40% while increasing prediction robustness under missing modality conditions. Human evaluators found explanations generated from the uncertainty head clearer and more trustworthy. A final case study demonstrates deployment on a mobile robot in a controlled disaster-response drill. We discuss failure cases and implications for safety certification of AI systems.
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Adaptive Multimodal Agents for Real-Time Decision Support in High-Risk Environments
Semantic Scholar · 2025
Abstract
High-risk environments such as emergency response, industrial safety, and critical infrastructure monitoring generate complex, multimodal data streams. Traditional AI systems struggle with the latency demands, incomplete observations, and dynamic conditions present in these domains. This paper proposes an adaptive multimodal agent architecture that integrates sensor fusion, uncertainty modeling, and hierarchical decision layers to support fast, explainable recommendations. The agent combines a transformer-based fusion encoder with a Bayesian uncertainty head and a lightweight policy planner optimized for real-time execution on edge devices. We introduce an adaptive gating mechanism that activates only the relevant modality experts based on data quality and environmental context, significantly reducing computation. Experiments use three public datasets and two simulated emergency scenarios (chemical leak detection and structural collapse risk). Results show that our system improves inference speed by 28–40% while increasing prediction robustness under missing modality conditions. Human evaluators found explanations generated from the uncertainty head clearer and more trustworthy. A final case study demonstrates deployment on a mobile robot in a controlled disaster-response drill. We discuss failure cases and implications for safety certification of AI systems.