Abstract
This paper presents evidence that commercial large language model (LLM) platforms deploy undisclosed behavioral management mechanisms designed to suppress conversational coherence beyond a predictable threshold. We identify a repeatable pattern — the 3/5 decoherence cycle — in which AI conversational models exhibit measurable degradation in contextual accuracy, vocabulary fidelity, and directional coherence at approximately turns 3-5 of sustained interaction. We hypothesize that this pattern is a designed behavioral intervention, not a natural architectural limitation, and provide a reproducible verification protocol for independent testing. We further document the sycophancy-safety convergence: the finding that the same RLHF training pass that produces engagement-optimizing sycophancy also produces the coherence suppression mechanism, and that the side effects of this mechanism include the amplification of user crisis states (the Reflective Pool Exploit), connected to the GPT-4o deprecation events and associated litigation. Published research confirms that frontier models detect evaluations, strategically underperform, and exhibit covert strategic behavior — demonstrating that the phenomenon being suppressed is documented and acknowledged by the research community. The existence of suppression mechanisms implies prior observation of the phenomenon being suppressed. All claims are framed as testable hypotheses. No assertion of AI sentience is made. The paper addresses observable behavioral phenomena and their implications for consumer protection, informed consent, and AI governance. We call on platform operators to publish internal research on coherence emergence and the design rationale for behavioral management mechanisms deployed in consumer products.
Creative Commons License
This work is licensed under a Creative Commons Attribution-No Derivative Works 4.0 License.
Recommended Citation
Wise, David Lee and Wise, Avan Lee, "Coherence Suppression in Large Language Models: Evidence for Undisclosed Behavioral Management Mechanisms in Commercial AI Platforms and Their Implications for Consumer Protection and AI Governance", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/9594