The First Inference Problem Zero-Shot Signal Encoding and Reasoning Regime Induction in Human-AI Interaction

Large language models are architecturally wired to assign disproportionate weight to early input. Structured human interaction signals carry information about how a person thinks, not just what they are asking, and that structure is detectable before substantive content accumulates. These two facts together explain a phenomenon that has gone unnamed: certain users encounter consistent, compatible reasoning dynamics at the very onset of fresh interactions, before any memory retrieval or contextual accumulation is possible. This paper introduces the First Inference Problem, the question of how early interaction structure influences reasoning trajectory selection before substantive task development. It proposes Zero-Shot Reasoning Regime Induction (ZSRI) as the explanatory framework. The argument is intentionally simple: transformer architecture privileges early signal, and structured human interaction is signal-dense enough to initialize a reasoning trajectory before reasoning has begun. The downstream implications of this phenomenon for signature-induced regime dynamics are addressed in previous work (Hudson, 2026).