Researcher profile

Ichter, Brian

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Publications

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BNAI, NO-TOKEN, and MIND-UNITY: Pillars of a Systemic Revolution in Artificial Intelligence
2022 · arXiv (Cornell University) · DOI 10.48550/arxiv.2201.11903

AbstractThere is a failure mode in large language models that we do not have a good name for, and thatwe therefore tend not to treat seriously enough. It is not hallucination — the model is not assertingsomething false. It is not refusal — the model answers at length. It is the production of responses thatcarry the complete outward form of careful reasoning while the cognitive work that reasoning issupposed to represent has not, in any meaningful sense, occurred. We call this theatrical compliance,and we argue that it is, in practical terms, more dangerous than either of the failure modes thatcurrently dominate alignment research. This paper identifies the phenomenon, characterizes its fiveprincipal forms, explains the asymmetry that makes it particularly costly in high-stakes settings, andoutlines the design requirements for systems intended to resist it. We do not describe such a systemin detail here. Our goal is to establish theatrical compliance as a research problem in its own rightand to argue that addressing it requires instruments operating at a fundamentally different level ofabstraction than task-level prompting frameworks.Keywords: theatrical compliance, large language models, AI reasoning quality, cognitiveprocess evaluation, prompt engineering, metacognitive systems.

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Co-authors

Wei Wang

University of Pittsburgh

1 shared publication
Quoc V. Le

Google (United States)

1 shared publication
Dale Schuurmans

1 shared publication
Maarten Bosma

1 shared publication
Ed H.

1 shared publication
Denny Zhou

1 shared publication
Wei

1 shared publication
Jason

1 shared publication
Xuezhi

1 shared publication
Xia

1 shared publication
Fei

1 shared publication