Latent Structure Recognition: A Testable Operator Variable in Multi-Turn Human-AI Interaction

Abstract:Large language model performance is usually evaluated as a property of the model, the prompt, the task, or the surrounding system architecture. Extended human-AI interaction introduces another variable: the human operator. This paper proposes Latent Structure Recognition (LSR) as a measurable operator-side capacity relevant to multi-turn AI performance.LSR is defined as the ability to infer the governing structure of an unfolding interaction from incomplete observable evidence, detect meaningful deviation from that structure before overt failure occurs, and introduce corrective information that restores task fidelity without adding unnecessary noise. The framework makes no claim of access to hidden model states, latent representations, chain-of-thought, machine consciousness, or supernatural intuition. The operator observes behavior, infers structure, and acts on that inference. The inference may be right or wrong.Recognition inside an adaptive system is not passive. Once an operator acts on an inferred structure, the intervention changes what happens next. The operator may therefore recognize an emerging structure, induce it, or participate in a coupled process containing both effects. LSR treats that ambiguity as an empirical problem rather than assuming the answer.The framework extends prior work on interaction-level coherence by specifying a candidate human-side mechanism. In this account, the operator can function as an external coherence regulator: detecting deviation, transmitting correction, evaluating uptake, and preserving a governing coordinate across turns.The Engineering question is direct:Can the regulatory advantages of a high-LSR operator be transfered to users who do not naturally possess them?