8 papers
IMPACT: Intent-driven Multi-agent Policy with Attention for SLO-guaranteed Microservice Migration in Cloud-edge Systems
Xinjin Li, Siru Tao, Shihan Yin +6
Ensuring strict tail-latency service-level objectives (SLOs) in dynamic mobile edge computing (MEC) systems remains challenging because user mobility, wireless fading, bursty workl…
MOSAIC: Orchestrating Collaborative Knowledge Tracing with Hierarchical Semantic Alignment
Xinjin Li, Mengyue Wang, Yuzhen Lin +4
Knowledge Tracing (KT) is important for personalized education but traditionally suffers from two key limitations: a reliance on shallow ID-based representations that neglect seman…
FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction
Xinjin Li, Jinghan Cao, Mengyue Wang +5
Traffic forecasting requires modeling complex temporal dynamics and long-range spatial dependencies over large sensor networks. Existing methods typically face a trade-off between…
OSGym: Scalable OS Infra for Computer Use Agents
Zengyi Qin, Jinyuan Chen, Yunze Man +25
Training computer use agents requires full-featured OS sandboxes with GUI environments, which consume substantial hardware resources as the number of sandboxes scales. Stochastic e…
CATCH: A Modular Cross-domain Adaptive Template with Hook
Xinjin Li, Yulie Lu, Jinghan Cao +3
Recent advances in Visual Question Answering (VQA) have demonstrated impressive performance in natural image domains, with models like LLaVA leveraging large language models (LLMs)…
Hy-Facial: Hybrid Feature Extraction by Dimensionality Reduction Methods for Enhanced Facial Expression Classification
Xinjin Li, Yu Ma, Kaisen Ye +3
Facial expression classification remains a challenging task due to the high dimensionality and inherent complexity of facial image data. This paper presents Hy-Facial, a hybrid fea…
R-KV: Redundancy-aware KV Cache Compression for Reasoning Models
Zefan Cai, Wen Xiao, Hanshi Sun +11
Reasoning models have demonstrated impressive performance in self-reflection and chain-of-thought reasoning. However, they often produce excessively long outputs, leading to prohib…
Entropy-based Exploration Conduction for Multi-step Reasoning
Jinghan Zhang, Xiting Wang, Fengran Mo +3
Multi-step processes via large language models (LLMs) have proven effective for solving complex reasoning tasks. However, the depth of exploration of the reasoning procedure can si…