Alhosseini Almodarresieh, Seyed Alireza: Computational Analogues of Somatic Markers: Mitigating Epistemic Risk and Decision Paralysis in LLM Agents via Affective Termination Signals

Large Language Model (LLM) agents exhibit a critical failure mode when operating in underconstrained decision environments: Computational Decision Paralysis (CDP). This phenomenon is characterized by the inability to terminate deliberation among epistemically equivalent options, leading to combinatorial explosion and resource exhaustion. Drawing on Damasio’s Somatic Marker Hypothesis, we propose the Pain-Aware Decision Architecture (PADA). PADA is an inference-time middleware that injects an Affective Termination Signal (ATS) into LLM reasoning loops. We formally model the agent’s decision process as a Partially Observable Markov Decision Process (POMDP), where the ATS is defined as a composite, dynamically weighted function of epistemic entropy, cumulative deliberation cost, and decision stagnation. When the ATS breaches a decaying threshold, the architecture triggers mathematically defined Meta-Cognitive Escape Actions (MCEAs). We detail the experimental design for evaluating PADA against standard baselines across a novel Ambiguous Travel Planning (ATP) benchmark. This paper provides the rigorous theoretical foundation, mathematical formalization, and reproducible methodology required to transition LLM agents from unbounded optimizers to resource-bounded, satisficing decision-makers