Epistemic Legibility in AI-Assisted Science: Ignorance Auditing as a Governance Instrument for the Peer-Review and Evaluation Bottleneck

In the paper's words

As AI systems take on hypothesis generation, experiment planning, analysis, and writing, the scientific ecosystem faces a new bottleneck: not production, but evaluation, verification, and governance. This position paper contributes a documented case and an instrument to that problem. The case: a flagship paper produced inside an AI-assisted research pipeline was independently identified as AI-generated by structural, mathematical, and stylistic markers — while the forensic analyses that caught it simultaneously fabricated institutional and biographical claims of their own.

The abstract, verbatim, and its opening sentence is the claim. Every line below is quoted from the text.

Numbers it reports

1. The analyses identified a category error in the treatment of Landauer's bound: computing the bound at room temperature ($T = 300$ K) versus cryogenic temperature ($T = 10$ mK) in Planck units, and conflating thermodynamic erasure-energy floors with room-temperature operational coherence.

   from 2. A Documented Case: The AI-Generated Flagship and Its Auditors

How it is done

1. Rowan Brad Quni-Gudzinas QNFO, Amsterdam (rowan@qnfo.org) Submitted to AI for Meta-Science workshop (NeurIPS 2026)

   from Epistemic Legibility in AI-Assisted Science: Ignorance Auditing as a Governance Instrument for the Peer-Review and Evaluation Bottleneck

2. As AI systems take on hypothesis generation, experiment planning, analysis, and writing, the scientific ecosystem faces a new bottleneck: not production, but evaluation, verification, and governance.

   from Abstract

3. The analyses identified a category error in the treatment of Landauer's bound: computing the bound at room temperature ($T = 300$ K) versus cryogenic temperature ($T = 10$ mK) in Planck units, and conflating thermodynamic erasure-energy floors with room-temperature operational coherence.

   from 2. A Documented Case: The AI-Generated Flagship and Its Auditors

4. One day before the finding surfaced, the same organization developed, through iterative human–AI dialogue, the Universal Ignorance Audit (Quni-Gudzinas 2026a, https://papers.qnfo.org/papers/universal-ignorance-audit/): a fifteen-question, five-phase method for systematically interrogating the structure of not-knowing in any domain.

   from 3. The Instrument: The Universal Ignorance Audit

What would falsify it

1. The paper's rigid scaffolding — pre-registered prediction registers, calibration registers, disconfirmation-condition tables — was identified as mimicking popular synthetic-evaluation frameworks designed to make AI text appear scientifically rigorous.

   from 2. A Documented Case: The AI-Generated Flagship and Its Auditors

2. Its protocol requires explicit disconfirmation conditions, which converts vague epistemic unease into named, checkable structure.

   from 3. The Instrument: The Universal Ignorance Audit

3. Publish disconfirmation conditions; treat "what if I am wrong about everything?" as a standard step, not a rhetorical flourish.

   from 5. Six Principles and Workshop Recommendations

Where the claim stops

1. Prevalence research is an urgent open question for the meta-science community.

   from 6. Limitations and Open Questions

2. The audit's administration is not free; scaling it to high-throughput review requires tooling that does not yet exist.

   from 6. Limitations and Open Questions

3. Legibility governance raises the cost of concealment; it does not eliminate it.

   from 6. Limitations and Open Questions

Where to read

1. Abstract198 words · 1 min
2. 1. Introduction296 words · 1 min
3. 2. A Documented Case: The AI-Generated Flagship and Its Auditors431 words · 2 min
4. 3. The Instrument: The Universal Ignorance Audit275 words · 1 min
5. 4. Position: Three Legibility Layers as the Unit of Governance275 words · 1 min
6. 5. Six Principles and Workshop Recommendations334 words · 1 min
7. 6. Limitations and Open Questions166 words · 1 min
8. 7. Conclusion132 words · 1 min