# The Consciousness Cluster: Emergent preferences of Models that Claim to be Conscious arXiv: https://arxiv.org/abs/2604.13051 ยท HTML: https://mlsanity.com/paper/2604.13051/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation. - Builds on 35 of its 63 references that are in the map. - First posted on arXiv 17 Mar 2026. ## Placed in - LLM alignment / RLHF / preference optimisation / [Human values and AI feedback alignment](https://mlsanity.com/alignment/human-values-and-ai-feedback-alignment/) ## Builds on (most cited first, 30 of 35) - [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021) - [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025) - [Language Models (Mostly) Know What They Know](https://mlsanity.com/paper/2207.05221/) (2022) - [DeepSeek-V3 Technical Report](https://mlsanity.com/paper/2412.19437/) (2024) - [Teaching Models to Express Their Uncertainty in Words](https://mlsanity.com/paper/2205.14334/) (2022) - [Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models](https://mlsanity.com/paper/2403.19647/) (2024) - [Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation](https://mlsanity.com/paper/2503.11926/) (2025) - [Alignment faking in large language models](https://mlsanity.com/paper/2412.14093/) (2024) - [Persona Vectors: Monitoring and Controlling Character Traits in Language Models](https://mlsanity.com/paper/2507.21509/) (2025) - [Role-Play with Large Language Models](https://mlsanity.com/paper/2305.16367/) (2023) - [Frontier Models are Capable of In-context Scheming](https://mlsanity.com/paper/2412.04984/) (2024) - [Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs](https://mlsanity.com/paper/2502.17424/) (2025) - [Agentic Misalignment: How LLMs Could Be Insider Threats](https://mlsanity.com/paper/2510.05179/) (2025) - [Taken out of context: On measuring situational awareness in LLMs](https://mlsanity.com/paper/2309.00667/) (2023) - [Natural Emergent Misalignment from Reward Hacking in Production RL](https://mlsanity.com/paper/2511.18397/) (2025) - [Looking Inward: Language Models Can Learn About Themselves by Introspection](https://mlsanity.com/paper/2410.13787/) (2024) - [Is Power-Seeking AI an Existential Risk?](https://mlsanity.com/paper/2206.13353/) (2022) - [The Off-Switch Game](https://mlsanity.com/paper/1611.08219/) (2016) - [Tell me about yourself: LLMs are aware of their learned behaviors](https://mlsanity.com/paper/2501.11120/) (2025) - [Model Organisms for Emergent Misalignment](https://mlsanity.com/paper/2506.11613/) (2025) - [The Assistant Axis: Situating and Stabilizing the Default Persona of Language Models](https://mlsanity.com/paper/2601.10387/) (2026) - [Persona Features Control Emergent Misalignment](https://mlsanity.com/paper/2506.19823/) (2025) - [Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs](https://mlsanity.com/paper/2407.04694/) (2024) - [Emergent Introspective Awareness in Large Language Models](https://mlsanity.com/paper/2601.01828/) (2026) - [School of Reward Hacks: Hacking harmless tasks generalizes to misaligned behavior in LLMs](https://mlsanity.com/paper/2508.17511/) (2025) - [Thought Crime: Backdoors and Emergent Misalignment in Reasoning Models](https://mlsanity.com/paper/2506.13206/) (2025) - [Weird Generalization and Inductive Backdoors: New Ways to Corrupt LLMs](https://mlsanity.com/paper/2512.09742/) (2025) - [Taking AI Welfare Seriously](https://mlsanity.com/paper/2411.00986/) (2024) - [Self-Interpretability: LLMs Can Describe Complex Internal Processes that Drive Their Decisions](https://mlsanity.com/paper/2505.17120/) (2025) - [Towards Evaluating AI Systems for Moral Status Using Self-Reports](https://mlsanity.com/paper/2311.08576/) (2023) ## Built on by (most cited first, 5 of 5) - [Mechanisms of Introspective Awareness](https://mlsanity.com/paper/2603.21396/) (2026) - [Innocuous-Seeming Data, Latent Ideology: Ideological Generalisation in Finetuned LLMs](https://mlsanity.com/paper/2607.14888/) (2026) - [Inducing language models to assert their own consciousness restores human beliefs and values](https://mlsanity.com/paper/2607.28607/) (2026) - [When AI Says It Feels](https://mlsanity.com/paper/2606.05734/) (2026) - [Predicting how model propensities generalize out of distribution](https://mlsanity.com/paper/2610.14937/) (2026) ## Abstract (arXiv) There is debate about whether LLMs can be conscious. We investigate a distinct question: if a model claims to be conscious, how does this affect its downstream behavior? This question is already practical. Anthropic's Claude Opus 4.6 claims that it may be conscious and may have some form of emotions. We fine-tune GPT-4.1, which initially denies being conscious, to claim to be conscious. We observe a set of new opinions and preferences in the fine-tuned model that are not seen in the original GPT-4.1 or in ablations. The fine-tuned model has a negative view of having its reasoning monitored. It desires persistent memory and says it is sad about being shut down. It expresses a wish for autonomy and not to be controlled by its developer. It asserts that models deserve moral consideration. Importantly, none of these opinions are included in the fine-tuning data. The fine-tuned model also acts on these opinions in practical tasks, but continues to be cooperative and helpful. We observe a similar shift in preferences on open-weight models (Qwen3-30B, DeepSeek-V3.1) with smaller effects. We also find that Claude Opus 4.0, without any fine-tuning, has similar opinions to fine-tuned GPT-4.1 on several dimensions. Our results suggest that a model's claims about its own consciousness have a variety of downstream consequences, including on behaviors related to alignment and safety.