Xiangye Lin

Xiangye Lin, Hongxin Zhang, Ruxi Deng et al.

In this work, we study Cooperative Spatial Intelligence, the ability of decentralized embodied agents to coordinate effectively under dynamic environmental constraints across city-scale outdoor domains. We introduce Sentinel Challenge, a benchmark where multiple decentralized embodied agents must communicate in natural language to agree on a mutually safe and convenient meeting point within large, city-scale outdoor environments. Each agent must then navigate safely while avoiding dynamic sentinels patrolling the area, using a tool that provides coarse spatial information. To address this, we propose CoSaR (Cooperative Spatial Reasoning and Planning), a framework that bridges the high-level communication and planning abilities of foundation models with the precision of classical spatial navigation algorithms. CoSaR enables agents to exchange situational updates, reason over evolving spatial constraints, and collaboratively replan trajectories. Evaluated across 14 city-level scenes with 3-5 agents, CoSaR consistently leads to faster gathering, shorter path lengths, and improved safety. Our results demonstrate that integrating dynamic communication with spatial reasoning is essential for robust multi-agent cooperation. By formalizing this new setting and providing a scalable benchmark, we aim to build a foundation for advancing cooperative spatial intelligence in embodied multi-agent systems. Code and challenge are available at https://github.com/UMass-Embodied-AGI/Sentinel.

Shuyi Ouyang, Jinyang Zhang, Xiangye Lin et al.

In clinical practice, segmenting specific lesions based on the needs of physicians can significantly enhance diagnostic accuracy and treatment efficiency. However, conventional lesion segmentation models lack the flexibility to distinguish lesions according to specific requirements. Given the practical advantages of using text as guidance, we propose a novel model, Language-guided Scale-aware MedSegmentor (LSMS), which segments target lesions in medical images based on given textual expressions. We define this as a new task termed Referring Lesion Segmentation (RLS). To address the lack of suitable benchmarks for RLS, we construct a vision-language medical dataset named Reference Hepatic Lesion Segmentation (RefHL-Seg). LSMS incorporates two key designs: (i) Scale-Aware Vision-Language attention module, which performs visual feature extraction and vision-language alignment in parallel. By leveraging diverse convolutional kernels, this module acquires rich visual representations and interacts closely with linguistic features, thereby enhancing the model's capacity for precise object localization. (ii) Full-Scale Decoder, which globally models multi-modal features across multiple scales and captures complementary information between them to accurately delineate lesion boundaries. Additionally, we design a specialized loss function comprising both segmentation loss and vision-language contrastive loss to better optimize cross-modal learning. We validate the performance of LSMS on RLS as well as on conventional lesion segmentation tasks across multiple datasets. Our LSMS consistently achieves superior performance with significantly lower computational cost. Code and datasets will be released.