A Disproof of Large Language Model Consciousness: The Necessity of Continual Learning for Consciousness
arXiv Preprint Archive December 14, 2025 via arXiv
Summary
AI-generated from the abstractA formal analysis shows that many contemporary theories of consciousness fail to meet the requirements of falsifiability and non-triviality. This is especially problematic for claims that Large Language Models (LLMs) might be conscious: because functionally equivalent systems exist, no falsifiable and non-trivial theory can judge them conscious, forming a disproof of LLM consciousness. In contrast, theories of consciousness that require continual learning do satisfy these formal constraints for humans. This supports the hypothesis that if continual learning is linked to human consciousness, the current lack of continual learning in LLMs is intimately tied to their lack of consciousness.
Study at a glance
| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Keywords | Q-bio.nc Cs.ai |
| Key finding | No falsifiable and non-trivial theory of consciousness can judge contemporary Large Language Models conscious, while theories requiring continual learning satisfy the formal constraints for human consciousness. |
Abstract
Scientific theories of consciousness should be falsifiable and non-trivial. Recent research has given us formal tools to analyze these requirements of falsifiability and non-triviality for theories of consciousness. Surprisingly, many contemporary theories of consciousness fail to pass this bar, including theories based on causal structure but also (as I demonstrate) theories based on function. Herein, I show these requirements of falsifiability and non-triviality especially constrain the potential consciousness of contemporary Large Language Models (LLMs) because of their proximity to systems that are equivalent to LLMs in terms of input/output function; yet, for these functionally equivalent systems, there cannot be any falsifiable and non-trivial theory of consciousness that judges them conscious. This forms the basis of a disproof of contemporary LLM consciousness. I then show a positive result, which is that theories of consciousness based on (or requiring) continual learning do satisfy the stringent formal constraints for a theory of consciousness in humans. Intriguingly, this work supports a hypothesis: If continual learning is linked to consciousness in humans, the current limitations of LLMs (which do not continually learn) are intimately tied to their lack of consciousness.