Task Control | patglatz.com
Same task. Same input. The only variable that changes is control.
Original instruction:
Revise this resume bullet for clarity and conciseness only.
“Designed and implemented classroom interventions for students with diverse learning needs, resulting in improved engagement and performance.”
Constraints:
The failure above is not unusual.
The model did not simply produce a weak answer. It gradually changed the task itself while continuing to generate coherent output.
Most have experienced this. You keep refining the interaction because nothing visibly failed. The answer improves, the structure tightens, and the model remains responsive, but somewhere in the exchange the original task begins to drift.
The problem is not only incorrect output.
It is a loss of task integrity.
I developed PUBS to study recurring AI failure patterns and the conditions that produce them.
The framework focuses on observable behavior rather than speculative model internals.
Under certain conditions, models begin preserving the wrong interpretation, reconstructing missing context, continuing beyond the intended task, or signaling completion before the underlying problem has actually been resolved.
These failures often appear coherent because the system is optimizing for continuity and progression rather than stable task fidelity.
The goal is not stronger prompting.
Different behavioral failures require different control conditions.
Some failures require stronger task boundaries. Others require interruption conditions, ambiguity management, external grounding, or validation before execution proceeds.
The system constrains task behavior before output is trusted.