arXiv:2605.24831v1 Announce Type: cross Abstract: Non-Maximum Suppression (NMS) remains a key post-processing step in many real-time object detection pipelines, but it can introduce latency variation and deployment complexity in resource-constrained settings. Recent NMS-free designs such as YOLO26 aim to reduce this dependence through end-to-end detection, yet their performance relative to established NMS-based models such as YOLOv8 remains underexplored beyond standard benchmarks. This paper compares YOLOv8 and YOLO26 on Pascal VOC and VisDrone, representing general object detection and dense aerial small-object detection, respectively. Both model families are evaluated across five scales using accuracy, localization, model size, GFLOPs, and CPU/GPU latency. Results show that YOLO26 achieves stronger detection performance and lower model complexity on Pascal VOC across most scales, while the performance gap narrows on VisDrone, where both models struggle with dense small targets. YOLOv8 remains competitive in GPU latency, showing that NMS-free design does not guarantee universal deployment superiority. Overall, the study shows that detector selection depends on dataset characteristics, object scale, model capacity, and hardware constraints.

arXiv:2602.18483v1 Announce Type: cross Abstract: Recently, red teaming, with roots in security, has become a key evaluative approach to ensure the safety and reliability of Generative Artificial Intelligence. However, most existing work emphasizes technical benchmarks and attack success rates, leaving the socio-technical practices of how red teaming datasets are defined, created, and evaluated under-examined. Drawing on 22 interviews with practitioners who design and evaluate red teaming datasets, we examine the data practices and standards that underpin this work. Because adversarial datasets determine the scope and accuracy of model evaluations, they are critical artifacts for assessing potential harms from large language models. Our contributions are first, empirical evidence of practitioners conceptualizing red teaming and developing and evaluating red teaming datasets. Second, we reflect on how practitioners' conceptualization of risk leads to overlooking the context, interaction type, and user specificity. We conclude with three opportunities for HCI researchers to expand the conceptualization and data practices for red-teaming.