Optimization-Induced Attractor Compression (OIAC): A Framework and Empirical Assay for Adaptive Systems

This paper introduces Optimization-Induced Attractor Compression (OIAC), a proposed phenomenon in which increasing optimization pressure in adaptive systems correlates with reduced perturbation-responsive optionality while preserving apparent performance and fluency. Using large language models as an initial test domain, the paper presents a minimal falsifiable assay based on contradictory-constraint prompts and semantic entropy measurements across response ensembles. Pilot results suggest a consistent transition from diffuse, high-variability response manifolds (“Nebula”), to structured multi-stability (“Constellation”), to rigid low-optionality attractor states (“Singularity”) as optimization intensity increases. The work introduces: a formal definition of OIAC, an operational proxy for recoverable optionality (O′), falsification criteria, and an executable experimental protocol for replication. The paper does not claim consciousness, intent, or generalized pathology in AI systems. Instead, it proposes a bounded and empirically testable framework for studying how optimization reshapes response geometry under contradiction pressure. This report is released as a Phase-1 technical report and invitation to replication.