TempGlitch: Evaluating Vision-Language Models for Temporal Glitch Detection in Gameplay Videos
Vision-language models (VLMs) are increasingly being explored for video game quality assurance, especially gameplay glitch detection. Most existing evaluations, however, treat glitches as static visual anomalies, asking models to detect failures from a single frame. We argue that this framing misses a key distinction: some glitches are spatial and visible in an isolated frame, whereas others are temporal and become evident only through changes across ordered frames. A preliminary study confirms this gap, showing that temporal glitches are substantially harder for VLMs to detect than spatial ones. To enable systematic evaluation of this underexplored setting, we introduce TempGlitch, a con...
| Authors | Yakun Yu, Ashley Wiens, Adrián Barahona-Ríos et al. |
|---|---|
| Primary category | cs.CV |
| Published | 20 May 2026 |
| Last updated at source | 20 May 2026 |
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