Tootfinder

Unlock the Potential of Fine-grained LLM Serving via Dynamic Module Scaling
Jingfeng Wu, Yiyuan He, Minxian Xu, Xitong Gao, Kejiang Ye, Chengzhong Xu
https://arxiv.org/abs/2507.18006

Unlock the Potential of Fine-grained LLM Serving via Dynamic Module Scaling
The rise of large language models (LLMs) has created new opportunities across various fields but has also introduced significant challenges in resource management. Current LLM serving systems face a fundamental tension: balancing serving demands with limited resources while adapting to unpredictable traffic patterns. Static deployments lead to suboptimal resource utilization and performance degradation under dynamic workloads. Furthermore, the high cost of adjusting instances hinders dynamic sc…

Understanding Pedestrian Gesture Misrecognition: Insights from Vision-Language Model Reasoning
Tram Thi Minh Tran, Xinyan Yu, Callum Parker, Julie Stephany Berrio Perez, Stewart Worrall, Martin Tomitsch
https://arxiv.org/abs/2508.06801

Understanding Pedestrian Gesture Misrecognition: Insights from Vision-Language Model Reasoning
Pedestrian gestures play an important role in traffic communication, particularly in interactions with autonomous vehicles (AVs), yet their subtle, ambiguous, and context-dependent nature poses persistent challenges for machine interpretation. This study investigates these challenges by using GPT-4V, a vision-language model, not as a performance benchmark but as a diagnostic tool to reveal patterns and causes of gesture misrecognition. We analysed a public dataset of pedestrian-vehicle interact…