Evaluating VLMs for Autonomous Agent-Driven Geometry Clipping Detection in Video Game QA

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Computer Science > Computer Vision and Pattern Recognition

arXiv:2607.25921v1 (cs)

[Submitted on 28 Jul 2026]

Title: Evaluating VLMs for Autonomous Agent-Driven Geometry Clipping Detection in Video Game QA

Authors: Carlos Celemin , Benedict Wilkins , Adrián Barahona-Ríos , Saman Zadtootaghaj , Nabajeet Barman

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Abstract: 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, GPT, Qwen, Gemma, Llama, and Ministral) under a zero-shot prompting setting and analyse their sensitivity to four prompt variants.

Our results show that while the VLMs can capture visual cues associated with geometry clipping, they all produce substantial false positives on visually ambiguous frames such as near-contact geometry and partial occlusions. Gemini-3.1-Flash achi

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