The prevailing "Agent" paradigm in Artificial Intelligence focuses on extending human capability through task execution—using tools, retrieving data, and managing workflows. However, this functionalist approach overlooks a critical dimension of intelligence: perception. This paper proposes a novel theoretical paradigm: Autonomous Dissonance Perception (ADP). Unlike traditional anomaly detection, which relies on predefined thresholds, ADP leverages the implicit "world model" embedded within Large Language Models (LLMs) to detect contextual inconsistencies—scenarios that are syntactically valid but semantically dissonant. We posit that "expert intuition"—whether in pharmaceutical engineering, financial auditing, or software architecture—is mathematically isomorphic: it is a sensitivity to high-dimensional friction between input data and latent expectations. We propose a formal framework to capture this friction by monitoring internal-state perturbations (e.g., orthogonal drifts in hidden states, attention head inhibition) rather than output probabilities. This paper outlines the mathematical formulation of ADP, presents thought experiments across multiple domains, and proposes a validation protocol for the research community. ADP aims to elevate AI from a task executor to a Cognitive Sentinel, capable of perceiving the "silent fractures" in complex systems that human bandwidth often misses.
Bo Cui (Tue,) studied this question.