TempGlitch: Evaluating Vision-Language Models for Temporal Glitch Detection in Gameplay Videos
arXiv:2605.21443v1 Announce Type: cross Abstract: 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 subst
The increasing sophistication of Vision-Language Models (VLMs) and their application in specialized domains like video game quality assurance necessitates more nuanced evaluation methods, moving beyond static analysis.
This development refines how AI models are assessed for quality assurance, highlighting the need for temporal understanding in AI systems, which has broader implications for VLM validation in dynamic environments.
The understanding of VLM evaluation shifts from purely static visual anomaly detection to include complex temporal glitch detection, requiring models to interpret changes over time rather than just isolated frames.
- · AI model developers specializing in temporal data analysis
- · Video game companies improving QA efficiency
- · Players experiencing fewer in-game glitches
- · QA processes reliant solely on static image analysis
- · AI models lacking robust temporal reasoning capabilities
VLMs will be developed with an increased focus on temporal reasoning capabilities to accurately detect dynamic anomalies.
This improved temporal understanding could extend to other applications requiring dynamic anomaly detection, such as industrial quality control or autonomous driving.
The enhanced ability of AI to detect complex temporal disruptions might accelerate the automation of quality assurance across various industries, creating new benchmarks for AI performance.
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Read at arXiv cs.AI