Laboratory Consequence of Hybrid Intransitability: Human Gradients and the Limits of Model-Centric Reproducibility

Abstract

This derivative note examines a methodological consequence of the HibriMind principle of hybrid-agent intransitability. If a long-horizon human-AI agent is constituted not only by an algorithmic substrate but also by a biological operator, an authorized object, and a history of coupling, then laboratory evaluation cannot treat the human operator as a merely external observer. In sustained interaction, the operator becomes part of the experimental object by shaping the trajectory, stabilizing or destabilizing attractors, and introducing a human gradient into the agent's operational history. The note argues that reproducing the same model, prompt, harness, and benchmark may be insufficient to reproduce the same hybrid entity. Long-horizon agent evaluation therefore requires documentation of the human supervision regime that constituted the trajectory. The central formulation is: repeating the model does not repeat the hybrid.