Phenomenological Compass: LLMs simulate meaning, lack state
A deep dive into the nature of LLM outputs argues that large language models produce a "highly refined simulation" of reasoning by selecting tokens from probability distributions rather than through genuine cognitive processes. This technical critique highlights a fundamental gap between the form of meaning observed by humans and the lack of autonomous goal formation in transformer architectures.
The current wave of AI development is hitting a "mechanism ceiling" where statistical coherence is mistaken for true reasoning. LLM coherence is local and conditional, relying on structures inherited from training data rather than real-time planning, while interventions like chain-of-thought prompting only refine output distributions without introducing genuine persistence. Ultimately, the "meaning" of an output is largely projected by the human reader onto the text, as cognitive properties like state evolution currently reside in external architectures rather than the model itself.
DISCOVERED
54d ago
2026-04-03
PUBLISHED
54d ago
2026-04-03
RELEVANCE
AUTHOR
ParadoxeParade