arXiv:2607. 25921v1 Announce Type: cross 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.
By Carlos Celemin, Benedict Wilkins, Adri\'an Barahona-R\'ios, Saman Zadtootaghaj, Nabajeet Barman
arXiv:2605.19410v2 Announce Type: replace
Abstract: Segmentation has become easy when the concept is known, requiring retrieval of a learned visual grounding from text. It remains hard for open ad-ho...
By Zilin Wang, Stella X. Yu
arXiv:2604.11082v2 Announce Type: replace
Abstract: Visual glitches in video games degrade player experience and perceived quality, yet manual quality assurance cannot keep pace with the growing test...
By Yakun Yu, Ashley Wiens, Adri\'an Barahona-R\'ios, Benedict Wilkins, Saman Zadtootaghaj, Nabajeet Barman, Cor-Paul Bezemer
Visual Language Models (VLMs) excel at describing visible scene content but struggle to reason about dynamic multi-agent interactions, where action semantics depend on coordinated roles and spatial-te...
The paper introduces TED (Text-Axis Evidence Decomposition), a post‑hoc scoring method that improves anomaly localization in CLIP‑based detectors without altering the backbone or prompts. TED evaluates whether ambiguous responses are better supported by defect patches or normal patches, thereby distinguishing true defects from visually complex normal regions. Experiments show that TED significantly enhances pixel‑level localization across frozen VLM backbones and adapted hosts, especially under hard‑false‑positive competition.
By JinYoung Kim, Geonho Kim, GiJeong Park, Geonu Lee, YoungJoon Yoo
arXiv:2609.40325v1 Announce Type: new
Abstract: As interactive 3D worlds are increasingly used to study intelligent behavior, it becomes important to develop efficient pipelines for identifying anoma...
By Ziyan Jiang, Jingbo Yang, Jiabao Ji, Yujian Liu, Qiucheng Wu, Tommi Jaakkola, Yang Zhang, Shiyu Chang