Prompt-Level Fact-Heuristic-Emotion State Enforcement Reduces LLM Decision Instability Without Weight Changes
A new arXiv preprint introduces the Cognitive Kernel Model (CKM), a prompt-level intervention that requires large language models (LLMs) to explicitly separate input into facts, heuristics, and emotions before making decisions. In tests across 26 LLMs and over 37,000 observations, CKM significantly reduced output variability and decision-flip rates—by up to 82% in newer models—without modifying model weights. The method did not improve reasoning correctness but provided a measurable increase in behavioral consistency.
Why it matters: This work demonstrates a practical, model-agnostic approach to improving LLM reliability, addressing a key concern for real-world deployment in sensitive applications.
Full story at: arXiv Computation and Language ↗