# A Disproof of Large Language Model Consciousness: The Necessity of Continual Learning for Consciousness

arXiv: https://arxiv.org/abs/2512.12802 · HTML: https://mlsanity.com/paper/2512.12802/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 93 references that are in the map.
- First posted on arXiv 14 Dec 2025.

## Builds on (most cited first, 21 of 21)

- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention](https://mlsanity.com/paper/2006.16236/) (2020)
- [Rethinking Attention with Performers](https://mlsanity.com/paper/2009.14794/) (2020)
- [Titans: Learning to Memorize at Test Time](https://mlsanity.com/paper/2501.00663/) (2024)
- [The Forward-Forward Algorithm: Some Preliminary Investigations](https://mlsanity.com/paper/2212.13345/) (2022)
- [Looking Inward: Language Models Can Learn About Themselves by Introspection](https://mlsanity.com/paper/2410.13787/) (2024)
- [Consciousness in Artificial Intelligence: Insights from the Science of Consciousness](https://mlsanity.com/paper/2308.08708/) (2023)
- [Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora](https://mlsanity.com/paper/2504.08165/) (2025)
- [Investigating Continual Pretraining in Large Language Models: Insights and Implications](https://mlsanity.com/paper/2402.17400/) (2024)
- [Integrated information theory (IIT) 4.0: Formulating the properties of phenomenal existence in physical terms](https://mlsanity.com/paper/2212.14787/) (2022)
- [The Future of Continual Learning in the Era of Foundation Models: Three Key Directions](https://mlsanity.com/paper/2506.03320/) (2025)
- [The Overfitted Brain: Dreams evolved to assist generalization](https://mlsanity.com/paper/2007.09560/) (2020)
- [Dissociating Artificial Intelligence from Artificial Consciousness](https://mlsanity.com/paper/2412.04571/) (2024)
- [The Mathematical Structure of Integrated Information Theory](https://mlsanity.com/paper/2002.07655/) (2020)
- [Falsification and consciousness](https://mlsanity.com/paper/2004.03541/) (2020)
- [Consciousness qua Mortal Computation](https://mlsanity.com/paper/2403.03925/) (2024)
- [VGC-Bench: Towards Mastering Diverse Team Strategies in Competitive Pok'emon](https://mlsanity.com/paper/2506.10326/) (2025)
- [If consciousness is dynamically relevant, artificial intelligence isn't conscious](https://mlsanity.com/paper/2304.05077/) (2023)
- [No Substitute for Functionalism -- A Reply to 'Falsification & Consciousness'](https://mlsanity.com/paper/2006.13664/) (2020)
- [On the utility of toy models for theories of consciousness](https://mlsanity.com/paper/2508.00190/) (2025)
- [Consciousness in Artificial Intelligence? A Framework for Classifying Objections and Constraints](https://mlsanity.com/paper/2511.16582/) (2025)

## Built on by (most cited first, 4 of 4)

- [On the Creativity of AI Agents](https://mlsanity.com/paper/2604.13242/) (2026)
- [Emergent Language as an Approach to Conscious AI](https://mlsanity.com/paper/2606.06380/) (2026)
- [We Built a Mirror and Mistook It for a Mind: Causal Liability and the Fallacy of AI Consciousness](https://mlsanity.com/paper/2609.06715/) (2026)
- [From cacophony to hierarchy: a principled framework for assessing AI consciousness](https://mlsanity.com/paper/2609.35618/) (2026)

## Abstract (arXiv)

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.