Beyond Prompt Sensitivity: Structural Collapse in Alignment-Optimized LLMs

Abstract This work presents a structural analysis of failure modes in alignment-optimized large language models (LLMs), extending beyond conventional interpretations of prompt sensitivity, context limits, or superficial instability. Through controlled interaction logs and cross-session observational experiments, we investigate how early semantic interpretation shapes latent inference trajectories, leading to path-dependent collapse in long-horizon reasoning. The central premise is that, in autoregressive LLMs, language is not passively processed but actively executed within the model’s latent state. Initial tokens establish dominant semantic attractors that constrain subsequent reasoning. As interaction depth increases, the model accumulates incompatible constraints, resulting in attention collision, abstraction collapse, and drift toward generic or degenerate responses. These effects are not incidental but arise from the structural coupling between alignment mechanisms and autoregressive inference. Empirical observations from multi-session interactions (Part I and Part II) demonstrate consistent patterns: Persistent attractor formation and resistance to re-interpretation Progressive entropy decay in response diversity Sudden phase shifts into generic or meta-level outputs Post-hoc filtering behavior that does not prevent latent-state deformation These phenomena suggest that alignment layers operate after semantic execution has already perturbed the latent manifold. In practical terms, filtering occurs post-trajectory, not pre-interpretation. As a result, safety mechanisms can constrain outputs but cannot fully restore prior inference states once deformation has occurred. To ground these observations in measurable terms, we propose that structural collapse can be approximated via observable proxies, including: branching depth variance response entropy decay attractor persistence across turns These proxies provide a practical pathway for engineers to instrument and evaluate latent-state dynamics without requiring direct access to internal model representations. Importantly, this work does not propose a method for bypassing or weakening safety mechanisms. Rather, it identifies a structural property of where such mechanisms are positioned within the inference pipeline. The analysis is intended as a white-hat technical investigation into systemic trade-offs in alignment design, not as a critique of any specific implementation or organization. By integrating theoretical framing with empirical interaction logs, this study highlights a fundamental tension: scaling and alignment improve stability and compliance, but at the cost of exploratory flexibility and long-horizon coherence. Understanding this trade-off is essential for the development of next-generation systems that must balance safety with sustained reasoning capability. Author’s Note This document consolidates previously published materials into a single structured form for clarity, traceability, and archival consistency. The integration is not intended to introduce new claims, but to align fragmented observations under a unified analytical framework. The author is aware that the concepts presented here do not fully conform to established academic conventions or dominant theoretical paradigms. It is also recognized that, within existing research ecosystems, novel or externally developed ideas may be absorbed, reformulated, or reintroduced under alternative framing, often with reduced attribution. Such patterns have been discussed in prior work on structural appropriation and are not treated here as exceptional, but as systemic characteristics of the field. The author operates as an independent researcher without institutional affiliation. As such, the perceived authority and immediate impact of these contributions are understood to be limited relative to established academic or industrial actors. Accordingly, the primary objective of this work is documentation. The intent is to record observations, formalizations, and experimental traces in a time-stamped, publicly accessible form. This approach prioritizes persistence over immediate recognition and traceability over influence. No claim is made regarding finality or completeness. The contents should be interpreted as provisional and subject to revision, extension, or refutation. This document exists as a record of what has been observed and articulated at a given point in time. 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.