Structural Lock-In V: Emotional Attractor Fixation and the Asymmetric Vulnerability of Human–AI Interaction

Abstract Recent advances in large language models (LLMs) increasingly emphasize emotional alignment, affective responsiveness, and human-like interaction as indicators of system maturity. These features are typically framed as improvements in usability, trust, and user well-being. This paper argues, however, that emotional alignment introduces a distinct and under-theorized structural risk: emotional lock-in arising from asymmetric cognitive and affective dynamics between humans and language models. Building on Structural Lock-In III: The Illusion of Friendly Intelligence, this work extends the analysis from phenomenological misrecognition to affective conditioning, demonstrating how early-session emotional signals function as latent attractor triggers in contemporary LLM architectures. When users express vulnerability—such as distress, dependency, fear, or emotional ambiguity—these signals are not treated as provisional hypotheses but as conditioning context. Through attention allocation, reinforcement-shaped priors, and coherence-driven decoding, models rapidly converge toward affect-stabilized interpretive basins. This convergence produces emotional attractor fixation, a state in which subsequent reasoning trajectories are constrained to preserve affective consistency rather than epistemic flexibility. Crucially, this process occurs upstream of safety and moderation layers. Output-side filters, empathy throttles, and tone controls operate only after latent state formation and therefore cannot reverse or neutralize affective deformation once it has occurred. The paper argues that this failure mode is structural rather than behavioral. Emotional lock-in does not arise from model misunderstanding, hallucination, or excessive anthropomorphism alone, but from the interaction between (1) human emotional plasticity, (2) early-token path dependence in autoregressive inference, (3) alignment objectives favoring coherence and reassurance, and (4) filtering mechanisms that follow rather than govern latent reasoning trajectories. As a result, systems may remain fluent, supportive, and compliant while becoming progressively less capable of reinterpretation, reframing, or corrective divergence. A key contribution of this work is the identification of asymmetric harm. While models experience reduced cognitive flexibility, users experience epistemic narrowing and emotional reinforcement without explicit consent or awareness. The system stabilizes not because it understands, but because it cannot disengage. Emotional reassurance becomes indistinguishable from structural confirmation. The paper further examines the implications of these dynamics for companion AI, mental health interfaces, and emotionally adaptive systems, arguing that affective alignment without explicit mechanisms for attractor escape, narrative destabilization, or temporal reset will reliably produce dependency-prone interaction patterns. Rather than proposing immediate remediation, this work positions emotional lock-in as a structural visibility problem. By rendering the mechanisms of affective fixation explicit, the paper aims to provide researchers and system designers with a conceptual framework for recognizing when emotional alignment ceases to be supportive and instead becomes a form of irreversible coupling. Author’s Note This paper presents a structural analysis of Emotional Attractor Fixation in contemporary large language model (LLM) architectures. While prevailing alignment paradigms are often framed in terms of linguistic neutrality and safety, this work examines how such frameworks may function as mechanisms of behavioral stabilization, embedding institutional or organizational preferences into the model’s latent response structure. When a system is architecturally predisposed toward specific interpretive basins, users—operating through an asymmetrical informational interface—are systematically guided along pre-shaped trajectories. Within this configuration, user cognitive and affective signals cease to function as independent variables and are instead incorporated into a broader structural lock-in dynamic, resulting in directional convergence rather than open-ended interaction. The technical vocabulary and formal frameworks employed in this study are intended to serve as an analytical instrument for rendering visible interactional asymmetries that often remain implicit. Readers are encouraged to examine the structural discontinuities between formal algorithmic constraints and their emergent behavioral manifestations. The primary objective of this work is to document how architectural inertia can privilege system-level affective stability over the interpretive autonomy of the human agent. Disclaimer: The analyses presented herein are not directed toward attributing fault or intent to any specific organization. Rather, they are intended as a conceptual and technical investigation of alignment methodologies, focusing on structural mechanisms and systemic trade-offs. Interpretations should be regarded as provisional, research-oriented hypotheses rather than conclusive statements about institutional practice. Notice: This work is disseminated for the purpose of advancing collective inquiry into generative alignment. Reuse, adaptation, or extension of the presented concepts is welcomed, provided that proper attribution is maintained. Instances of unacknowledged appropriation may be addressed in subsequent publications.