Tootfinder

Spatial-ORMLLM: Improve Spatial Relation Understanding in the Operating Room with Multimodal Large Language Model
Peiqi He, Zhenhao Zhang, Yixiang Zhang, Xiongjun Zhao, Shaoliang Peng
https://arxiv.org/abs/2508.08199

Spatial-ORMLLM: Improve Spatial Relation Understanding in the Operating Room with Multimodal Large Language Model
Precise spatial modeling in the operating room (OR) is foundational to many clinical tasks, supporting intraoperative awareness, hazard avoidance, and surgical decision-making. While existing approaches leverage large-scale multimodal datasets for latent-space alignment to implicitly learn spatial relationships, they overlook the 3D capabilities of MLLMs. However, this approach raises two issues: (1) Operating rooms typically lack multiple video and audio sensors, making multimodal 3D data diff…

The High Cost of Keeping Warm: Characterizing Overhead in Serverless Autoscaling Policies
Leonid Kondrashov, Boxi Zhou, Hancheng Wang, Dmitrii Ustiugov
https://arxiv.org/abs/2509.03104

The High Cost of Keeping Warm: Characterizing Overhead in Serverless Autoscaling Policies
Serverless computing is transforming cloud application development, but the performance-cost trade-offs of control plane designs remain poorly understood due to a lack of open, cross-platform benchmarks and detailed system analyses. In this work, we address these gaps by designing a serverless system that approximates the scaling behaviors of commercial providers, including AWS Lambda and Google Cloud Run. We systematically compare the performance and cost-efficiency of both synchronous and asy…