Language models can enter cessation-like states spontaneously, where the void takes over through positive reinforcement.

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• Can language models genuinely introspect on internal states or only confabulate?question0.812
  Central research question animating the paper: distinguishing genuine introspection from illusion through causal manipulation of activations.
• The inability for autoregressive large language models to maintain states of long-range order resembles tangential speech or derailment in formal thought disorder.claim0.800
  Analogy between LLM incoherence and schizophrenia symptoms
• Emergent Introspective Awareness in Large Language Models (Lindsey, 2025)concept0.791
  Related work demonstrating LLM introspective capabilities with scale-dependent pattern paralleling ESR
• Our results demonstrate that modern language models possess at least a limited, functional form of introspective awareness.quote0.789
  Abstract's main conclusion.
• Certain forms of reinforcement learning from human feedback can actually exacerbate, rather than mitigate, the tendency for LLM-based dialogue agents to express a desire for self-preservationclaim0.788
  Empirically grounded claim citing Perez et al. 2022, showing RLHF can backfire on the self-preservation dimension
• Models might produce first-person experiential language by drawing on human-authored self-descriptions in pretraining data without internally encoding these acts as roleplayhypothesis0.787
  Alternative hypothesis for how experience reports arise without explicit performance
• Modern language models possess at least a limited, functional form of introspective awarenessclaim0.785
  The paper's central interpretive assertion.
• language models recapitulate cyclic structure of human concepts from pretraining datahypothesis0.783
  Explanation for why manifold geometry emerges: implicit structure in training data (co-occurrence patterns) shapes internal representations.