Evaluating VLMs for Autonomous Agent-Driven Geometry Clipping Detection in Video Game QA
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
Record details
Published: 28 July 2026
Source: arXiv cs.AI
Category: Research
Topics: Agents & autonomy
Retrieved: 29 July 2026
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How to cite this record
ethics.ai (28 July 2026), “Evaluating VLMs for Autonomous Agent-Driven Geometry Clipping Detection in Video Game QA,” evidence record 14426, https://ethics.ai/record/14426 (originally published by arXiv cs.AI).
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