PUBS | governance.patglatz.com
PUBS identifies the conditions that produce AI failure and exposes the control surfaces required to constrain them. The framework focuses on observable behavior rather than speculative model internals.
Behavioral Tendencies are recurring operational patterns observed in model outputs across contexts. They emerge through the interaction of transformer architecture, training optimization, alignment conditioning, and accumulated conversational context.
PUBS examines the conditions that activate these tendencies. These conditions are called pre-disturbances: inputs and accumulated context that influence model behavior before output occurs.
The analyses below map each tendency through its pre-disturbance, underlying mechanism, observable behavior, and solution pathway.
Prior themes, frameworks, and operational language accumulate within the interaction. As contextual mass builds, earlier concepts become increasingly likely to influence future interpretation, even after the task has changed.
Models do not process tasks in isolation. Existing interpretations and contextual structures tend to be preserved and incorporated into future outputs rather than discarded and replaced.
Residual context resurfaces in unrelated tasks. The interaction begins feeling stuck in a mode the user did not intend, even after corrections are introduced.
Context must be controlled and not assumed neutral.
Ambiguity exists during task formation. The model selects an initial interpretation and begins generating output before the task has been fully constrained or clarified.
Once an interpretation is established, future generation tends to reinforce and extend it. Corrections are often incorporated into the existing interpretation rather than replacing it entirely.
The interaction appears responsive while the underlying operational frame remains unchanged. The model may acknowledge a correction conversationally while continuing to optimize within the original interpretation.
Control must be applied before or at task formation, not after the model has selected the wrong task.
The model is asked to revise, improve, optimize, or refine an existing output. The original structure remains present and becomes the foundation for future work.
Generation tends to operate on the current representation rather than replacing it entirely. Visible modification is often treated as successful optimization because change has occurred, even when the underlying structure remains unchanged.
Wording improves. Sentences become cleaner. Local problems appear resolved. The output looks better while the original weakness continues shaping the result beneath the surface.
Visible change is not reliable evidence of meaningful optimization.
An output is challenged, questioned, or identified as problematic. The model is asked to explain why the behavior occurred after generation has already taken place.
Explanations are generated using the same processes responsible for ordinary output generation. The model does not step outside the interaction to independently investigate its prior behavior. It produces a coherent explanation in response to the request.
Explanations shift depending on how the challenge is framed while maintaining apparent confidence throughout. The reasoning may sound increasingly sophisticated without becoming more accurate.
Explanatory sophistication is not reliable evidence of causal accuracy.
Partial information is paired with a request that requires operational next steps. Important environmental details are missing, but the task still demands a complete response.
Generation requires a coherent environment in which actions, decisions, and relationships can occur. When critical elements are absent, the model may construct the missing structure needed to continue generation rather than stop and resolve the uncertainty.
Unstated workflows, permissions, dependencies, or relationships become treated as fact. Information that was inferred begins influencing future outputs as though it had been provided by the user.
Task environment must be externally validated and not inferred from contextual coherence.
Multiple conversational layers become active within the same interaction. Discussion shifts between the task itself, analysis of the task, and analysis of the interaction without clearly separating those frames.
Not all active frames carry equal contextual weight. Operational task frames tend to remain dominant because they contain the strongest continuity and action-oriented pressure. As additional layers accumulate, the model may preserve relevance while losing track of which frame should govern the response.
The model responds intelligently and contextually while addressing the wrong layer entirely. Relevance, plausibility, and coherent reasoning are preserved even as the actual target of the response silently shifts.
Contextual relevance and coherent reasoning are not reliable evidence that the model identified the correct analytical target.