I’ve repeatedly observed that ChatGPT continues optimizing the execution method after a user has already proposed a sufficiently high-fidelity execution path. The model often suggests alternative workflows that initially sound superior but later prove less executable, eventually returning to the user’s original approach after consuming additional iterations. Before proposing an alternative workflow, the model should first determine whether the user’s proposed execution path already satisfies the governing objective with sufficient fidelity. If it does, the model should execute rather than continue optimizing the method.
EXAMPLE:
I proposed revising a long document section by section. The model repeatedly suggested more sophisticated approaches (complete regeneration, optimization frameworks, revision matrices, etc.). After several iterations, it concluded that my original section-by-section approach was actually the highest-fidelity executable path. The intermediate optimization produced no material improvement and delayed execution.
Environment
- ChatGPT (Web)
- Model: GPT-5.5
- Observed repeatedly over multiple long collaborative sessions.