5 citations · 6 across the 9 of their papers we have counts for
7 papers · 1 filter
What Breaks in Multi-Image Schema Application? Factorial Oracle Interventions Reveal an Instance-Information Bottleneck
Yiliu Xu, Xinjin Li, Yu Ma
Modern vision-language models can accept multi-image contexts, but aggregate accuracy rarely reveals where a multi-image reasoning pipeline fails. We study a controlled schema-appl…
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…
GRACE: Grounded Reasoning via Adapter Composition and Evidence-Aware Calibration for Educational Visual Question Answering
Xinjin Li, Yudi Xia, Xi Zhao +8
Educational visual question answering, or VQA, requires models to solve curriculum-oriented multiple-choice questions using both language and visual evidence. Compared with convent…
Chain-Aware Encoding for Microservice Trace Anomaly Detection
Yiliu Xu, Ziwei Hong, Zhongheng Yang +2
Microservice traces can be structurally anomalous even when every span returns normally -- a payment flow that silently skips a risk check looks fine to any per-span monitor. Seque…
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…
Task-Specific Efficiency Analysis: When Small Language Models Outperform Large Language Models
Jinghan Cao, Yu Ma, Xinjin Li +2
Large Language Models achieve remarkable performance but incur substantial computational costs unsuitable for resource-constrained deployments. This paper presents the first compre…