# Logbook: Extremely Long-form Audio Event Understanding

arXiv: https://arxiv.org/abs/2610.07338 · HTML: https://mlsanity.com/paper/2610.07338/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 47 references that are in the map.
- First posted on arXiv 5 Oct 2026.

## Abstract (arXiv)

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.