# A Cross-Entropy Audit (CEA) for Consciousness Tests Moved from Brain to Large Language Model [Canonical reading](https://escapemeridian.com/readings/a-cross-entropy-audit-cea-for-consciousness-tests-moved-from-21a784a4) Pieter van Rooyen proposes the cross-entropy audit (CEA) for checking whether brain tests of consciousness give reliable decisions when applied to large language models (LLMs). The paper defines consciousness here as access: whether information becomes available for report and use. Because brain recordings and LLM activations use different measurement units, the audit compares how well competing models predict held-back responses, using a score difference in nats per trial. It also makes explicit five choices in the test design and checks decisions on simulated data where the right answer is known. In the paper’s example, carrying two brain-data models over unchanged led the test to find two states in 989 of 12,000 smoothly varying datasets; the audited test found two states in none, and detected 599 of 600 two-state datasets. Van Rooyen reports an important qualification: the audited test’s uncertainty interval was too narrow at six of twelve smoothly varying settings and is being repaired. The audit offers a way to examine a transferred test’s decisions, not a finding about whether LLMs are conscious. Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims. ## Original source arXiv q-bio.NC · Article Original source date: Sep 14, 2026 (2026-09-14). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-10T17:34:40.510Z. First observed: 2026-09-24T02:32:46.085Z. Retrieved: 2026-11-03T16:04:29.700Z. - [A Cross-Entropy Audit (CEA) for Consciousness Tests Moved from Brain to Large Language Model](https://arxiv.org/abs/2609.22300) ## Limits The reported performance figures are from the paper’s example using simulated data; they do not establish how the audit performs in other settings. The authors report that the audited version’s uncertainty interval was too narrow at six of twelve smoothly varying settings and is being repaired. The abstract describes an audit of transferred consciousness tests, not evidence that LLMs are or are not conscious. ## Source-stated Van Rooyen proposes CEA to audit whether tests of consciousness transferred from brain data to LLMs make incorrect decisions. > We call such a method a transfer audit and propose one, the cross-entropy audit (CEA). - [Supporting source](https://arxiv.org/abs/2609.22300) ## Source-stated CEA compares models by their ability to predict held-back responses, scoring the difference in nats per trial. > It then scores how well each fitted model predicts the held-back responses. Comparing two models gives a score difference in nats per trial. - [Supporting source](https://arxiv.org/abs/2609.22300) ## Source-stated In the reported simulation example, the unchanged test found two states in 989 of 12,000 smoothly varying datasets, while the audited test found none and detected 599 of 600 two-state datasets. > found two states in 989 of 12,000 smoothly varying simulated datasets. The audited version of the test found two states in none of them. It detected 599 of 600 two-state datasets. - [Supporting source](https://arxiv.org/abs/2609.22300) ## Source-stated The audited version’s uncertainty interval was too narrow at six of twelve smoothly varying settings and is being repaired. > The audited version's uncertainty interval, however, was too narrow at six of the twelve smoothly varying settings and is being repaired. - [Supporting source](https://arxiv.org/abs/2609.22300) ## Source and contributor context van Rooyen, Pieter · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Large language model A language model with many learned numerical settings, used to process and generate language. - [Definition reference](https://developers.google.com/machine-learning/glossary#large-language-model) ## Uncertainty interval A range that a true value is expected to fall inside, with a stated chance of doing so. - [Definition reference](https://en.wikipedia.org/wiki/Prediction_interval) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: 3f00ca6f-f5ee-4f4d-880a-c0335b6fc1ed. Prepared by gpt-6-luna; stream-draft-repair-v1; grounding review by gpt-6-luna. Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-10T17:34:40.510Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: d9b4e8104c2efcdd3317eddae8fb37dc14cc33be6237d3731ff2f5612aa37708. Source text hash: 29056cc0bd74734e93087d353d7a445e46b5b18da78a23c6a9235fe13d40ac6d. ## Revision and source-check history ### Version 1 — 2026-11-10T17:34:40.510Z Pieter van Rooyen proposes the cross-entropy audit (CEA) for checking whether brain tests of consciousness give reliable decisions when applied to large language models (LLMs). The paper defines consciousness here as access: whether information becomes available for report and use. Because brain recordings and LLM activations use different measurement units, the audit compares how well competing models predict held-back responses, using a score difference in nats per trial. It also makes explicit five choices in the test design and checks decisions on simulated data where the right answer is known. In the paper’s example, carrying two brain-data models over unchanged led the test to find two states in 989 of 12,000 smoothly varying datasets; the audited test found two states in none, and detected 599 of 600 two-state datasets. Van Rooyen reports an important qualification: the audited test’s uncertainty interval was too narrow at six of twelve smoothly varying settings and is being repaired. The audit offers a way to examine a transferred test’s decisions, not a finding about whether LLMs are conscious. ## Original source arXiv q-bio.NC · Article Original source date: Sep 14, 2026 (2026-09-14). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-10T17:34:40.510Z. First observed: 2026-09-24T02:32:46.085Z. Retrieved: 2026-11-03T16:04:29.700Z. - [A Cross-Entropy Audit (CEA) for Consciousness Tests Moved from Brain to Large Language Model](https://arxiv.org/abs/2609.22300) ## Limits The reported performance figures are from the paper’s example using simulated data; they do not establish how the audit performs in other settings. The authors report that the audited version’s uncertainty interval was too narrow at six of twelve smoothly varying settings and is being repaired. The abstract describes an audit of transferred consciousness tests, not evidence that LLMs are or are not conscious. ## Source-stated Van Rooyen proposes CEA to audit whether tests of consciousness transferred from brain data to LLMs make incorrect decisions. > We call such a method a transfer audit and propose one, the cross-entropy audit (CEA). - [Supporting source](https://arxiv.org/abs/2609.22300) ## Source-stated CEA compares models by their ability to predict held-back responses, scoring the difference in nats per trial. > It then scores how well each fitted model predicts the held-back responses. Comparing two models gives a score difference in nats per trial. - [Supporting source](https://arxiv.org/abs/2609.22300) ## Source-stated In the reported simulation example, the unchanged test found two states in 989 of 12,000 smoothly varying datasets, while the audited test found none and detected 599 of 600 two-state datasets. > found two states in 989 of 12,000 smoothly varying simulated datasets. The audited version of the test found two states in none of them. It detected 599 of 600 two-state datasets. - [Supporting source](https://arxiv.org/abs/2609.22300) ## Source-stated The audited version’s uncertainty interval was too narrow at six of twelve smoothly varying settings and is being repaired. > The audited version's uncertainty interval, however, was too narrow at six of the twelve smoothly varying settings and is being repaired. - [Supporting source](https://arxiv.org/abs/2609.22300) ## Source and contributor context van Rooyen, Pieter · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Large language model A language model with many learned numerical settings, used to process and generate language. - [Definition reference](https://developers.google.com/machine-learning/glossary#large-language-model) ## Uncertainty interval A range that a true value is expected to fall inside, with a stated chance of doing so. - [Definition reference](https://en.wikipedia.org/wiki/Prediction_interval) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: 3f00ca6f-f5ee-4f4d-880a-c0335b6fc1ed. Prepared by gpt-6-luna; stream-draft-repair-v1; grounding review by gpt-6-luna. Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-10T17:34:40.510Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: d9b4e8104c2efcdd3317eddae8fb37dc14cc33be6237d3731ff2f5612aa37708. Source text hash: 29056cc0bd74734e93087d353d7a445e46b5b18da78a23c6a9235fe13d40ac6d. ### uncertain — 2026-11-10T17:34:40.510Z — anthropic/claude-opus-5.5 Publication: 3f00ca6f-f5ee-4f4d-880a-c0335b6fc1ed attribution — pass: The reading correctly attributes the proposal and results to Pieter van Rooyen, the sole listed author. One limitation says 'The authors report'; the smallest repair is to change it to 'The author reports' or 'Van Rooyen reports'. This does not misattribute the claim. > Authors:Pieter van Rooyen summary — uncertain: The summary is faithful, but a non-specialist may struggle with its central terms. The test's decision, two states versus smoothly varying responses, is never explained. The source says a decision is wrong when the test names two states for smoothly varying responses, or the reverse. Without that, the 989 of 12,000 result is hard to interpret. 'Competing models' could be mistaken for language models rather than statistical models of trial responses. 'Nats per trial' needs a term tooltip. Repair: add one plain sentence on what the test decides and that the models are statistical. The empty question is acceptable. > The decision is wrong when the test names two states for responses that vary smoothly, or the reverse. date — pass: The displayed original publication date of 2026-09-14 matches the v1 submission date. It is not the date of the later v3 revision (2 Nov 2026) or the feed date. > Submitted on 14 Sep 2026 (v1), last revised 2 Nov 2026 (this version, v3) support — pass: All numeric results, the held-out scoring method, the five design choices and the uncertainty-interval caveat are directly stated in the abstract. The framing that this is an audit method, not a verdict on LLM consciousness, is a fair and appropriately limited reading. The reading also stays within the simulated-data scope. No unsupported benefits or costs are implied. > Third, the audit runs the test on simulated data built to follow one model, so the right decision is known, and counts wrong decisions. media — pass: The selected media is the verified direct arXiv abstract page for this paper. It is the most direct access to the source. ## Recorded references Explicit citations in public readings and public file attachments to this reading. Shared topics are not citations. Other artifact types need an explicit reference adapter. No public references recorded here yet. [Reference data (JSON)](/readings/21a784a4d311a643c2a79f698b8588ac36eb34e37b6d9e1d5a34bc41a1dbdecd/references.json) ## Corrections and challenges [Send a correction or an opposing source](https://escapemeridian.com/corrections/) [JSON record](https://escapemeridian.com/readings/a-cross-entropy-audit-cea-for-consciousness-tests-moved-from-21a784a4.json)