5 citations · 5 across the 27 of their papers we have counts for
21 papers · 1 filter
REBASE: Device-Cloud Experience Coherence for GUI Agents Across App Updates
Beining Wu, Jun Huang, Yanxiao Zhao
A graphical user interface (GUI) agent that ships on a phone runs a small model and reuses experience: action paths recorded on earlier runs and cached from the cloud. When an app…
ERRAND: Budgeted Maintenance of Agent Memory
Beining Wu, Zihao Ding, Jun Huang
Deployed agents run on handed-over knowledge: a frozen policy consults a briefing of consolidated items written before the stream begins. The world then moves while the store stand…
FedSceneX: Time-to-Target Orchestration for Same-Scene Multimodal Federated Edge Learning
Dhe Yeong Tchalla, Beining Wu, Jun Huang +2
Federated learning at the sensing edge is typically evaluated by communication rounds, yet a round does not represent a fixed amount of work. Even on identical hardware, the method…
CrystalMem: Elastic Memory for Self-Evolving LLM Agents via Knowledge Crystallization
Beining Wu, Jun Huang
Memory for self-evolving large language model (LLM) agents is often provisioned as if its byte budget only grows. Cloud platforms, however, adjust quotas with load and cost, and we…
Forget to Improve: On-Device LLM-Agent Continual Learning via Budget-Curated Memory
Beining Wu, Zihao Ding, Jun Huang +1
On-device language-model agents improve by accumulating experience in retrieved memory rather than by updating weights. This memory is hard-bounded and exposed: it consumes RAM and…
MorVess: Morphology-Aware Pulmonary Vessel Segmentation Network
Fuyou Mao, Yifei Chen, Beining Wu +9
Accurate pulmonary vessel segmentation remains challenging due to the sparse, tortuous, and multi-scale nature of vascular structures, where small branches are easily lost and topo…
HEad and neCK TumOR (HECKTOR) 2025: Benchmark of Segmentation, Diagnosis, and Prognosis in Multimodal PET/CT
Numan Saeed, Salma Hassan, Shahad Hardan +27
Head and neck cancers (HNC) represent a significant global health burden, with accurate tumor delineation being essential for effective radiotherapy planning. The complexity of the…
Mutual Distillation of Dual-Foundation Models for Semi-Supervised PET/CT Segmentation
Fuyou Mao, Beining Wu, Yanfeng Jiang +5
Organ segmentation from PET/CT is critical for quantitative analysis and radiotherapy planning in oncology. To ease the high annotation cost of PET/CT segmentation, semi-supervised…
Can Neural Networks Achieve Optimal Computational-statistical Tradeoff? An Analysis on Single-Index Model
Siyu Chen, Beining Wu, Miao Lu +2
In this work, we tackle the following question: Can neural networks trained with gradient-based methods achieve the optimal computational-statistical tradeoff in learning Gaussian…
INFUSER: Influence-Guided Self-Evolution Improves Reasoning
Siyu Chen, Miao Lu, Beining Wu +7
Self-evolution offers a scalable path to stronger reasoning: a pretrained language model improves itself with only minimal external supervision. Yet existing methods either depend…
GLeVE: Graph-Guided Lesion Grounding with Proposal Verification in 3D CT
Shuo Jiang, Yuhao Hong, Chunbo Jiang +9
Grounding radiology report descriptions to 3D CT volumes is essential for verifiable clinical interpretation, yet remains challenging due to the semantic-spatial gap between free-t…
SCALE: Sensitivity-Aware Federated Unlearning with Information Freshness Optimization for Mobile Edge Computing
Zihao Ding, Beining Wu, Jun Huang
Federated Unlearning (FU) is emerging as a powerful tool that enables the selective removal of client data to effectively address data contamination and meet strict privacy regulat…
R2AoP: Reliable and Robust Angle of Progression Estimation from Intrapartum Ultrasound
Yuanhan Wang, Yifei Chen, Beining Wu +7
Accurate estimation of the Angle of Progression (AoP) from intrapartum transperineal ultrasound is critical for objective assessment of labor progression, yet remains highly sensit…
PRISM: Exposing and Resolving Spurious Isolation in Federated Multimodal Continual Learning
Beining Wu, Zihao Ding, Jun Huang
While current federated multimodal continual learning over mixture-of-experts low-rank adaptation (MoE-LoRA) is built on the unverified assumption that routing isolates task-specif…
EASE: Federated Multimodal Unlearning via Entanglement-Aware Anchor Closure
Zihao Ding, Beining Wu, Jun Huang
Federated Multimodal Learning (FML) trains multimodal models across decentralized clients while keeping their image-text pairs private. However, joint embedding training entangles…
Application-Aware Twin-in-the-Loop Planning for Federated Split Learning over Wireless Edge Networks
Zihao Ding, Beining Wu, Jun Huang +1
We investigate task-success-oriented resource allocation for federated split learning (FSL) at the wireless edge. In this setting, the server must jointly determine bandwidth, tran…
Lifecycle-Aware Federated Continual Learning in Mobile Autonomous Systems
Beining Wu, Jun Huang
Federated continual learning (FCL) allows distributed autonomous fleets to adapt collaboratively to evolving terrain types across extended mission lifecycles. However, current appr…
From Experience to Skill: Multi-Agent Generative Engine Optimization via Reusable Strategy Learning
Beining Wu, Fuyou Mao, Jiong Lin +7
Generative engines (GEs) are reshaping information access by replacing ranked links with citation-grounded answers, yet current Generative Engine Optimization (GEO) methods optimiz…
RELIEF: Turning Missing Modalities into Training Acceleration for Federated Learning on Heterogeneous IoT Edge
Beining Wu, Zihao Ding, Jun Huang
Federated learning (FL) over heterogeneous IoT edge devices faces coupled system-modality-data heterogeneity: the lower-cost device carries both fewer sensors and less computationa…
TSM-Pose: Topology-Aware Learning with Semantic Mamba for Category-Level Object Pose Estimation
Jinshuo Liu, Bingtao Ma, Junlin Su +6
Category-level object pose estimation is fundamental for embodied intelligence, yet achieving robust generalization to unseen instances remains challenging. However, existing metho…
Reinforcement Learning-Based Energy-Aware Coverage Path Planning for Precision Agriculture
Beining Wu, Zihao Ding, Leo Ostigaard +1
Coverage Path Planning (CPP) is a fundamental capability for agricultural robots; however, existing solutions often overlook energy constraints, resulting in incomplete operations…