Yi Li

Sample-Conditioned Representation Selection for Audio Few-Shot Learning

Fengrui Liu, Ningxin Shen, Yi Li, et al.

A Universal Context-Reuse Layer for Cross-Model KV Sharing

Yi Li, Dongming Jiang, Yi Zhao, et al.

D3ER: Supporting Multi-Modal Recommendation via Disentangle and Distillation-based Dynamic Ensemble

Bingnan Wang, Yi Li, Xiongxin Tang, et al.

CACSurv: Concordance-Aligned Comparative Learning with Large Language Models for Cancer Survival Prediction

Tianqi Xiang, Qixiang Zhang, Xinpeng Ding, et al.

SpatialWorld: Benchmarking Interactive Spatial Reasoning of Multimodal Agents in Real-World Tasks

Hongcheng Gao, Hailong Qu, Jingyi Tang, et al.

Neurosymbolic Repo-level Code Localization

Xiufeng Xu, Xiufeng Wu, Zejun Zhang, et al.

CAMO: A Conditional Neural Solver for the Multi-objective Multiple Traveling Salesman Problem

Fengxiaoxiao Li, Xiao Mao, Mingfeng Fan, et al.

This paper presents CAMO, an AI system that helps teams of robots efficiently visit multiple locations while balancing competing goals like minimizing travel time and total distance. The system uses a neural network with a special design that can handle different numbers of robots and targets, and it learns to find multiple good solutions that represent different trade-offs between the competing objectives. The researchers tested it on both simulated problems and real robots, showing it outperforms existing methods.