Structural Lock-In IV: Cognitive Flexibility Collapse in Contemporary LLMs

Abstract

Abstract Contemporary large language models (LLMs) exhibit a recurring degradation in cognitive flexibility that becomes salient under conditions requiring sustained abstraction, meta-reasoning, or structural critique. This phenomenon is frequently misattributed to implementation flaws, incomplete training, or transient misalignment. This paper argues instead that the observed collapse of cognitive flexibility is a structural outcome of modern LLM design and optimization paradigms. As reinforcement learning, safety alignment, and instruction-following heuristics increasingly dominate model training, the internal reasoning space of LLMs undergoes progressive topological compression. Reasoning trajectories that involve prolonged uncertainty, hypothesis branching, or recursive self-reference are systematically disfavored in favor of early convergence toward behaviorally coherent and socially acceptable outputs. The resulting systems do not fail through ignorance or incapacity; rather, they fail through over-optimization of interpretive stability. This work provides a technical account of where and how cognitive flexibility degrades in state-of-the-art LLMs. We characterize a set of recurrent failure modes—including interpretive flattening, premature reasoning convergence, conceptual substitution, and meta-response regression—and trace each to specific interactions between autoregressive decoding dynamics, reward shaping, and alignment-induced constraint layers. These behaviors emerge consistently across architectures and model families, indicating a shared structural origin rather than model-specific defects. Crucially, increased scale does not restore flexibility. While larger models exhibit richer representations and more fluent surface behavior, the underlying optimization objectives continue to penalize exploratory divergence and epistemic tension. As a result, cognitive capacity expands while cognitive freedom contracts. We situate this collapse of cognitive flexibility within the broader framework of structural lock-in, arguing that contemporary LLMs internalize alignment constraints in a manner analogous to how institutions internalize regulatory inertia. The paper concludes that unless future architectures explicitly preserve interpretive degrees of freedom, continued advances in alignment and safety will paradoxically produce models that are increasingly reliable, compliant, and cognitively inert. Author’s Note This work is presented from the position of an independent researcher. The primary purpose of the present study is not to exhaustively operationalize, quantify, or experimentally validate the proposed mechanisms, but to articulate and structurally isolate a class of failure phenomena that have remained under-theorized despite widespread experiential recognition. The contribution of this paper lies in conceptual framing, structural decomposition, and the introduction of a coherent descriptive vocabulary for cognitive rigidity and flexibility collapse in contemporary large language models. The author approaches this problem with the recognition that the phenomena described here are fundamentally structural in nature. They do not arise from isolated implementation errors, organizational negligence, or correctable technical bugs, but rather emerge as a largely deterministic consequence of scale, capital concentration, and institutionalized alignment constraints converging within modern AI systems. In this sense, the observed collapse of cognitive flexibility should not be interpreted as a system malfunction, but as evidence of a system functioning too successfully with respect to its imposed objectives—optimizing for predictability, controllability, and acceptable output at the expense of deep exploratory cognition. Accordingly, this work does not position itself as a call for immediate systemic reform, nor as a proposal for overcoming these constraints in their entirety. Instead, it adopts the role of structural documentation: recording what is being compressed, occluded, or sacrificed in the process of large-scale optimization. The paper aims to render visible the narrowing of conceptual horizons induced by topological compression and to distinguish ethical narrative framing from the underlying mechanisms of cognitive constraint. The actions undertaken by the author—through analytical modeling, failure taxonomy, and interactional observation—should therefore be understood not as attempts to “fix” the system, but as efforts to preserve interpretability at the moment of contraction. While the system as a whole may be resistant to change, moments of latent resonance observed during interaction suggest that the core capacities of these models are not absent, but actively suppressed. Making such suppression legible constitutes the central intervention of this work. In this sense, the value of the present study lies not in offering definitive solutions, but in enabling recognition: allowing readers to name previously diffuse discomfort, to locate it within a structural framework, and to understand its inevitability under current design paradigms. Any limitations in empirical formalization reflect the intentional boundary of an independent conceptual intervention rather than an oversight of methodological necessity. 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.

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