AI Research
The Missing Benchmark: Why No One Can Yet Score a Model's "Stop Quality"
A new academic benchmark finds every frontier-class agent tested fails, more than half the time, at recognizing when a task is hopeless and should be abandoned

A new academic benchmark gives the industry its first real measure of "agentic abstention": whether an AI agent recognizes a task is infeasible and stops rather than keeps burning tool calls. Every frontier system tested fails most of the time, and neither Claude Fable 5 nor GPT-5.6, which OpenAI is taking to general availability this week, has been scored on it yet.






