A Closer Look at Failure Modes in Temporal Understanding of Large Audio-Language Models
Large Audio Language Models (LALMs) achieve strong performance on a variety of audio understanding tasks but continue to struggle with temporal reasoning, a fundamental capability central to human auditory perception. Understanding the causes of these failures remains challenging as existing benchmarks report performance gaps without probing underlying mechanisms. To address this, we introduce a benchmark with 1,657 questions across three foundational tasks designed specifically for mechanistic analysis. Examining model outputs across varying input settings (behavioral analysis) reveals that models often under-utilize audio when textual cues are available. We also provide the first causal...
| Authors | Apoorva Kulkarni, Kaousheik Jayakumar, Sreyan Ghosh et al. |
|---|---|
| Primary category | cs.SD |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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