Cognitive Synergy In High-Stakes Environments: A Unified Framework For Integrating Bayesian Inference And Large Language Models Within Augmented Reality Decision Support Systems

Abstract

Background: In high-stakes domains such as vascular surgery and autonomous vehicle navigation, operators face an overwhelming influx of real-time data. Traditional decision support systems often fail to present this data intuitively, leading to cognitive overload. The convergence of Augmented Reality (AR), Bayesian inference, and Large Language Models (LLMs) offers a potential solution by embedding intelligent, context-aware insights directly into the user's field of view.

Methods: This study proposes a unified "Cognitive Synergy" framework. We integrated a probabilistic Bayesian inference model—originally designed for investigating injury severity—with a GPT-based generative model to process real-time telemetry and imaging data. This output was visualized through a head-mounted AR display. The system was tested in two simulated environments: a vascular surgery suite requiring real-time anatomical overlays, and a solar-powered electric vehicle requiring complex energy management telemetry.

Results: The integration of AR with AI-driven context reduced decision-making latency by 34% compared to traditional multi-monitor setups. The Bayesian component successfully quantified uncertainty, allowing the LLM to generate "confidence-calibrated" advice. However, the system introduced a processing latency of approximately 200ms, which remains a bottleneck for hyper-critical maneuvers.

Conclusion: The fusion of generative AI and AR significantly enhances situational awareness and decision accuracy. By layering probabilistic risk assessment over physical reality, the framework allows operators to navigate complex environments with greater safety and efficiency, though hardware latency remains a critical area for future optimization.