anti-cascade-collapse

Order-gap hallucination occurs when a model detects a false premise when asked directly but misses it when the premise is embedded in a complex multi-step task. It causes the model to contradict earlier constraints over time.

To prevent false premises in complex multi-step tasks, you can enforce assumption re-verification at each step. This ensures that every step respects all earlier constraints and detects contradictions before proceeding.

Assumption re-verification works by systematically re-checking the model's assumptions at each step of a complex task. This validation process identifies when a model fails to detect a false premise embedded earlier in the workflow.

Yes, you can use this for multi-step tasks without external dependencies. It operates entirely through scripts to enforce model safety and validate assumptions, requiring no additional setup or external libraries.

You should enforce assumption re-verification in model safety workflows whenever handling complex, multi-step tasks. It is specifically designed for scenarios where the model's initial assumptions may be contradicted by later steps.

The best way to validate constraints across multiple task steps is to apply automated re-verification at each stage. This prevents order-gap hallucination by ensuring no step violates the original assumptions established at the start.