# From cacophony to hierarchy: a principled framework for assessing AI consciousness

arXiv: https://arxiv.org/abs/2609.35618 · HTML: https://mlsanity.com/paper/2609.35618/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 208 references that are in the map.
- First posted on arXiv 28 Sep 2026.

## Builds on (most cited first, 30 of 32)

- [Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs](https://mlsanity.com/paper/2306.13063/) (2023)
- [Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task](https://mlsanity.com/paper/2210.13382/) (2022)
- [Persona Vectors: Monitoring and Controlling Character Traits in Language Models](https://mlsanity.com/paper/2507.21509/) (2025)
- [Emergent Linear Representations in World Models of Self-Supervised Sequence Models](https://mlsanity.com/paper/2309.00941/) (2023)
- [Language Models Represent Space and Time](https://mlsanity.com/paper/2310.02207/) (2023)
- [Consciousness in Artificial Intelligence: Insights from the Science of Consciousness](https://mlsanity.com/paper/2308.08708/) (2023)
- [The Assistant Axis: Situating and Stabilizing the Default Persona of Language Models](https://mlsanity.com/paper/2601.10387/) (2026)
- [Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words?](https://mlsanity.com/paper/2405.16908/) (2024)
- [Coordination Among Neural Modules Through a Shared Global Workspace](https://mlsanity.com/paper/2103.01197/) (2021)
- [Emergent Introspective Awareness in Large Language Models](https://mlsanity.com/paper/2601.01828/) (2026)
- [Could a Large Language Model be Conscious?](https://mlsanity.com/paper/2303.07103/) (2023)
- [Emotion Concepts and their Function in a Large Language Model](https://mlsanity.com/paper/2604.07729/) (2026)
- [Taking AI Welfare Seriously](https://mlsanity.com/paper/2411.00986/) (2024)
- [Large Language Models Report Subjective Experience Under Self-Referential Processing](https://mlsanity.com/paper/2510.24797/) (2025)
- [Where is the Mind? Persona Vectors and LLM Individuation](https://mlsanity.com/paper/2604.17031/) (2026)
- [A Case for AI Consciousness: Language Agents and Global Workspace Theory](https://mlsanity.com/paper/2410.11407/) (2024)
- [Dissociating Artificial Intelligence from Artificial Consciousness](https://mlsanity.com/paper/2412.04571/) (2024)
- [Mechanisms of Introspective Awareness](https://mlsanity.com/paper/2603.21396/) (2026)
- [Linear Spatial World Models Emerge in Large Language Models](https://mlsanity.com/paper/2506.02996/) (2025)
- [A Brain-like Synergistic Core in LLMs Drives Behaviour and Learning](https://mlsanity.com/paper/2601.06851/) (2026)
- [Initial results of the Digital Consciousness Model](https://mlsanity.com/paper/2601.17060/) (2026)
- [Are Biological Systems More Intelligent Than Artificial Intelligence?](https://mlsanity.com/paper/2405.02325/) (2024)
- [Exploring Consciousness in LLMs: A Systematic Survey of Theories, Implementations, and Frontier Risks](https://mlsanity.com/paper/2505.19806/) (2025)
- [A Disproof of Large Language Model Consciousness: The Necessity of Continual Learning for Consciousness](https://mlsanity.com/paper/2512.12802/) (2025)
- [Functional Emotions or Situational Contexts? A Discriminating Test from the Mythos Preview System Card](https://mlsanity.com/paper/2604.13466/) (2026)
- [The inner screen model of consciousness: applying the free energy principle directly to the study of conscious experience](https://mlsanity.com/paper/2305.02205/) (2023)
- [Report on Candidate Computational Indicators for Conscious Valenced Experience](https://mlsanity.com/paper/2404.16696/) (2024)
- [Why Is Anything Conscious?](https://mlsanity.com/paper/2409.14545/) (2024)
- [Introduction to Artificial Consciousness: History, Current Trends and Ethical Challenges](https://mlsanity.com/paper/2503.05823/) (2025)
- [The Copernican Argument for Alien Consciousness; The Mimicry Argument Against Robot Consciousness](https://mlsanity.com/paper/2412.00008/) (2024)

## Abstract (arXiv)

The question of AI consciousness is one of the most urgent pre-emptive problems in philosophy and computer science, yet progress is hampered by a cacophony of competing theories that often talk past each other. Separating the hard problem from the mapping problem allows the deepest metaphysical disagreements to be set aside: granting that experience supervenes on a system's organisation, the tractable question becomes at which grain of description that supervenience base sits. We extend Marr's three levels of analysis into a five-level hierarchy of functional descriptions (behavioural, computational, intrinsic causal-structural, organismic, and organism-environment) grounded in supervenience, coarse-graining, and multiple realisability. The major theories of consciousness are positioned within this hierarchy according to which level they take to be critical, and for each level we develop operationalisable indicators and assess current AI systems against them. A Bayesian model then combines theoretical credences with indicator evidence into an overall credence in a system's capacity for consciousness. In illustrative assessments, the verdict for current LLMs is driven as much by where theoretical credence is placed as by how the evidence is read: under different stipulated readings and credence distributions, assessments range from below 0.01 to roughly 0.8, showing sensitivity to assumptions. Finally, the consciousness indicators at each level closely overlap with the architectural features needed for general intelligence, suggesting that increasingly capable AI may become a stronger candidate for consciousness. The framework supports a structured agnosticism, in which theoretical commitments are made explicit, credences are updated as evidence accumulates, and assessments take the form of aggregated probabilities rather than verdicts.