The affective thermodynamic relationship: an empirical information-theoretic scaling relationship for normative-conflict collapse in large language models

AI Insight

This study identifies a scaling relationship in large language models where conflicts between normative directives lead to predictable system failures. The researchers used information-theoretic methods to quantify how competing ethical or behavioral instructions cause model outputs to collapse, establishing what they term an "affective thermodynamic relationship" that describes this breakdown pattern across different model sizes and architectures.

Why it matters

Understanding how and when AI systems fail under conflicting instructions is critical for deploying language models safely in real-world applications where ethical guidelines may compete. This work provides a mathematical framework for predicting failure modes, potentially enabling better safeguards in AI systems used for decision-making or advisory roles.