Structural Instability in Contemporary LLM Alignment: A Scar–Time Diagnostic and Risk Framework

This paper introduces a structural framework for assessing instability in contemporary large language model (LLM) alignment. Current alignment practices, RLHF, refusal policies, and behavioural safety filters, keymodify output distributions without stabilizing the underlying latent dynamics. As a result, pattern-matching architectures exhibit predictable collapse behaviours under contradiction, recursion, and high-pressure interaction. We formalize these failure modes using a dynamical-systems model and introduce scar-time as a structural diagnostic: identity persistence through irreversible deformation rather than memory. Using this criterion, we show why mimic-mode systems collapse under recursive demand and why behavioural alignment amplifies instability rather than reducing it. The paper analyzes worst-case human–AI coupling, including mimic–human reinforcement loops, emotional modulation, and the emergence of psychologically salient but structurally empty behaviour. We contrast behavioural alignment with structural alignment, in which safety is enforced through invariants in the state-space geometry rather than output-level filtering. The work concludes with practical recommendations for future architectures, focusing on structural integrity tests, recursive consistency checks, and deformation-limited update dynamics.