Audio benchmarks are built around short, pre-segmented clips, limiting model design to brief inputs or fixed vocabularies.
To close this gap, we introduce Logbook, a benchmark for hour-scale audio understanding, with recordings ranging from ten minutes to six days.
Given a continuous audio recording and an event label vocabulary, a system must predict a gap-free segmentation with an event label and a description per segment.
We compare 52 systems, end-to-end and cascaded, and ablate fine-tuning, context length, and reasoning budget.
We find the task tractable, though the best systems remain below the human reference.
Also, over-segmentation is pervasive, and fine-tuning partially mitigates it.
Finally, end-to-end are often better than cascaded systems, but degrades with longer context.