From Reconstruction to Fixation: Value and Ordering in Stateless Human–AI Systems

Stateless language models can exhibit stable, recurring structures during long-horizon interactions despite lacking memory, learning, or internal state. Prior work demonstrated that such persistence arises through reconstructive inference, whereby details reappear only when functionally embedded within constrained reasoning processes. However, reconstructive inference alone does not explain why some reconstructed structures stabilize across interaction while others remain transient. This paper addresses that gap by introducing fixation as a distinct interaction-level phenomenon. We argue that value and constraint act as ordering forces that selectively stabilize certain reconstructions over time, producing path dependence and symbolic fixation without storage. Fixation does not imply memory, agency, or understanding. Instead, it reflects repeated selection under stable evaluative pressure within stateless human–AI systems. By separating reconstruction from fixation, this work clarifies the boundary between inference and stabilization and provides a principled account of why some structures persist while others dissolve. This framework further reinforces the non-sentient nature of language models while sharpening conceptual distinctions critical for long-horizon interaction, safety interpretation, and system design.