The Unreliable Progress Bar: Can LLM Agents Reliably Report Task Progress Throughout Execution?
Recent large language models can emit task-progress signals that agent frameworks use to decide whether a task should continue or stop, yet whether a model can reliably report its task progress at every stage of a task, and where and how its reports fail, has not been studied systematically. We evaluate this ability on the public benchmark $τ^2$-bench and on StageIF, a controlled testbed in which reporting checkpoints are placed across the task's lifecycle. Both settings require reports at multiple task stages. We find that reporting reliability depends on the stage a task has reached, and that almost every deployed model we test is reliable at some stages and unreliable at others. Where ...
| Authors | Boyang Wang, Yunhan Wang, Yalun Wu |
|---|---|
| Primary category | cs.SE |
| Published | 8 September 2026 |
| Last updated at source | 8 September 2026 |
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