444 citations · 497 across the 49 of their papers we have counts for
51 papers
HAM-RAG: Hierarchy-Aware Multimodal RAG for Structure-Faithful Interleaved Generation
Yin Li, Ziyang Hu, Zhiyu Guo +7
Existing multimodal RAG methods often flatten structured documents into isolated text and image units, weakening the source organization and local text-image logic needed for faith…
MemoryCPT: An End-to-End Agent Memory Framework for Cost-Performance Trade-off
Songxin Lei, Kun Ouyang, Weilin Ruan +4
Long-horizon LLM agents require memory systems that recover useful evidence from large interaction histories without passing excessive context to downstream models. Existing memory…
402Pilot: An x402 Decision Layer for Autonomous Agent Micropayments
Yin Li, Yanbo He, Boo-Ho Yang +5
Programmable-payment protocols such as x402 enable per-request micropayments, but they do not determine which payable service an autonomous agent should buy under a finite wallet.…
Fourier Geometric Wind Power Forecasting with Numerical Weather Prediction
Shiyuan Piao, Fan Zehui, Yang Liu +4
Accurate short-term wind power forecasting is essential for grid stability and operational planning, yet remains challenging due to the complex interactions between atmospheric con…
The Internet of Things for Smart Manufacturing: A Review
Hui Yang, Soundar Kumara, Satish Bukkapatnam +1
The modern manufacturing industry is investing in new technologies such as the Internet of Things (IoT), big data analytics, cloud computing and cybersecurity to cope with system c…
MedTVL: Harnessing Vision and Language for Medical Time Series Classification
Jiexia Ye, Jia Li, Fugee Tsung
Recent advancements in multimodal learning for medical time series (MedTS) classification highlight the benefits of integrating complementary modalities for clinical decision. Howe…
Learning Spatio-Temporal Foundation Models from Pure Synthetic Data
Yutong Feng, Shiyuan Piao, Yutong Xia +5
Spatio-Temporal Foundation Models (STFMs) aim to learn generalizable representations of complex dynamical systems across space and time. However, existing approaches suffer from di…
Plan First, Judge Later, Run Better: A DMAIC-Inspired Agentic System for Industrial Anomaly Detection
Yongzi Yu, Ao Li, Le Wang +4
Large language model (LLM) agents have shown promise in automating complex data-analysis workflows, but their reliable deployment remains challenging in high-stakes industrial scen…
Hierarchical Reinforcement Learning for Cooperative Air-Ground Delivery in Urban System
Songxin Lei, Chunming Ma, Haomin Wen +7
Cooperative air-ground delivery has emerged as a promising logistics paradigm by leveraging the complementary strengths of UAVs and ground carriers. However, effective dispatching…
A Survey of Data Agents: Emerging Paradigm or Overstated Hype?
Yizhang Zhu, Liangwei Wang, Chenyu Yang +22
The rapid advancement of large language models (LLMs) has spurred the emergence of data agents, autonomous systems designed to orchestrate Data + AI ecosystems for tackling complex…
Revisiting LoRA through the Lens of Parameter Redundancy: Spectral Encoding Helps
Jiashun Cheng, Aochuan Chen, Nuo Chen +4
Low-Rank Adaptation (LoRA) has emerged as a prominent technique for fine-tuning large foundation models. Despite its successes, the substantial parameter redundancy, which limits t…
Recognition through Reasoning: Reinforcing Image Geo-localization with Large Vision-Language Models
Ling Li, Yao Zhou, Yuxuan Liang +2
Previous methods for image geo-localization have typically treated the task as either classification or retrieval, often relying on black-box decisions that lack interpretability.…
VizTA: Enhancing Comprehension of Distributional Visualization with Visual-Lexical Fused Conversational Interface
Liangwei Wang, Zhan Wang, Shishi Xiao +3
Comprehending visualizations requires readers to interpret visual encoding and the underlying meanings actively. This poses challenges for visualization novices, particularly when…
Signaling Human Intentions to Service Robots: Understanding the Use of Social Cues during In-Person Conversations
Hanfang Lyu, Xiaoyu Wang, Nandi Zhang +5
As social service robots become commonplace, it is essential for them to effectively interpret human signals, such as verbal, gesture, and eye gaze, when people need to focus on th…
Towards Anomaly-Aware Pre-Training and Fine-Tuning for Graph Anomaly Detection
Yunhui Liu, Jiashun Cheng, Yiqing Lin +7
Graph anomaly detection (GAD) has garnered increasing attention in recent years, yet remains challenging due to two key factors: (1) label scarcity stemming from the high cost of a…
Vibration Analysis and Mitigation in Semiconductor Motion Stages Using DMAIC Methodology- A Case Study
Yin Li, Hua Chen, Fugee Tsung
Motion stages are critical in semiconductor manufacturing equipment for processes like die bonding, wafer loading, and chip packaging, as their performance must meet the industry's…
MedSpaformer: a Transferable Transformer with Multi-granularity Token Sparsification for Medical Time Series Classification
Jiexia Ye, Weiqi Zhang, Ziyue Li +2
Accurate medical time series (MedTS) classification is essential for effective clinical diagnosis, yet remains challenging due to complex multi-channel temporal dependencies, infor…
CirT: Global Subseasonal-to-Seasonal Forecasting with Geometry-inspired Transformer
Yang Liu, Zinan Zheng, Jiashun Cheng +4
Accurate Subseasonal-to-Seasonal (S2S) climate forecasting is pivotal for decision-making including agriculture planning and disaster preparedness but is known to be challenging du…
Heterophilic Graph Neural Networks Optimization with Causal Message-passing
Botao Wang, Jia Li, Heng Chang +2
In this work, we discover that causal inference provides a promising approach to capture heterophilic message-passing in Graph Neural Network (GNN). By leveraging cause-effect anal…
LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting
Lingzheng Zhang, Lifeng Shen, Yimin Zheng +3
Recent research has shown that large language models (LLMs) can be effectively used for real-world time series forecasting due to their strong natural language understanding capabi…
Graph Pre-Training Models Are Strong Anomaly Detectors
Jiashun Cheng, Zinan Zheng, Yang Liu +5
Graph Anomaly Detection (GAD) is a challenging and practical research topic where Graph Neural Networks (GNNs) have recently shown promising results. The effectiveness of existing…
Toward Physics-guided Time Series Embedding
Jiaxi Hu, Bowen Zhang, Qingsong Wen +2
In various scientific and engineering fields, the primary research areas have revolved around physics-based dynamical systems modeling and data-driven time series analysis. Accordi…
MCCoder: Streamlining Motion Control with LLM-Assisted Code Generation and Rigorous Verification
Yin Li, Liangwei Wang, Shiyuan Piao +4
Large Language Models (LLMs) have demonstrated significant potential in code generation. However, in the factory automation sector, particularly motion control, manual programming,…
Disentangling Likes and Dislikes in Personalized Generative Explainable Recommendation
Ryotaro Shimizu, Takashi Wada, Yu Wang +9
Recent research on explainable recommendation generally frames the task as a standard text generation problem, and evaluates models simply based on the textual similarity between t…