The State of AI Consciousness Research
A survey of empirical AI consciousness research, covering interpretability studies of self-reports, emotion and valence vectors, and a pain axis that disrupts harm avoidance when steered.
Research / Sources
LessWrong
AI research and discussion on LessWrong. www.lesswrong.com
AI (2,246)Interpretability (ML & AI) (225)AI Control (141)AI Governance (101)AI Evaluations (91)Chain-of-Thought Alignment (46)AI Risk (33)AI Safety (31)AI Takeoff (29)AI-Assisted Alignment (22)AI Rights / Welfare (20)AI Alignment Fieldbuilding (19)AI Timelines (19)Constitutional AI (15)AI Psychology (14)Dealmaking (AI) (14)Regulation and AI Risk (14)AI Benchmarking (13)Jailbreaking (AIs) (13)AI Alignment Intro Materials (11)Alignment Pretraining (11)Slowing Down AI (10)Aligned AI Proposals (9)Debate (AI safety technique) (9)AI Capabilities (8)AI Development Pause (8)OpenAI (8)Aligned AI Role-Model Fiction (7)Human-AI Safety (7)Threat Models (AI) (7)AI Oversight (6)AI Sentience (6)AI Safety Cases (5)AI "Agent" Scaffolds (4)AI arms race (4)AI Safety Camp (4)AI Safety Public Materials (4)Open Source AI (4)Transformative AI (4)Adversarial Examples (AI) (3)Adversarial Training (3)AI Alignment (3)AI Boxing (Containment) (3)AI Persuasion (3)AI Risk Concrete Stories (3)AI Risk Skepticism (3)AI Success Models (3)Guaranteed Safe AI (3)Sandbagging (AI) (3)AI Consciousness (2)AI Misuse (2)AI Racing (2)AI Spiralism / AI Psychosis (2)Frontier AI Companies (2)Grants & Fundraising Opportunities (2)PauseAI (2)Prosaic Alignment (2)Public Reactions to AI (2)Whole Brain Emulation (2)AI Auditing (1)AI Ethics (1)AI Lab Self-Immolation (1)AI Questions Open Threads (1)AI Robustness (1)AI Services (CAIS) (1)AI-Fizzle (1)AIXI (1)Blackmail / Extortion (1)Brainstorming (1)Power Seeking (AI) (1)
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Do LLMs Have Desires?
Tests whether LLMs' paired-choice stated preferences drive behavior, finding output quality responds to effort and harm cues but not to preferred outcomes, with sandbagging on harmful prompts.
A Mirror Test For LLMs
Proposes a Mirror-Window game to test LLM self-awareness by having models identify their own outputs among others' tokens, finding strong results for some recent models, which the authors conclude still fall short.
Models have linear representations of what tasks they like
Trains linear probes on Gemma-3-27B activations to predict revealed preferences, finds they track system-prompt and persona preference shifts, and shows weak causal effects via steering.
Evaluation Awareness Scales Predictably in Open-Weights Large Language Models
Probes steering-vector activations across 15 open-weight models from 0.27B to 70B to show evaluation awareness grows with model size, with a power-law trend and Qwen as an outlier.
No ghost in the machine
A philosophical essay arguing that realist, deep-fact views of consciousness are untenable and advocating a deflationary illusionist view, with implications for how AI welfare research should proceed.