2 citations · 3 across the 25 of their papers we have counts for
31 papers
FARM: Fundamental Agentic Reward Model For Multi-task Wireless Network Optimization
Feiran You, Changxu Ni, Haozhe Ma +2
Future wireless networks require learning agents to adapt across heterogeneous channel conditions, traffic patterns, quality-of-service (QoS) requirements, objectives, and operatio…
TwinJEPA: Action-Preferred Predictive Representations for Goal-Conditioned Control
Feiran You, Hongyang Du
Joint-Embedding Predictive Architectures (JEPAs) are a promising paradigm for representation learning by predicting future latent states without reconstructing observations. Recent…
Carnator: Fast Text-to-Video Generation with Generation-Native Compatibility-Guided Cross-Request Reuse
Xingkun Yin, Xuebin Tang, Mingkun Xu +1
Video diffusion transformers produce high-quality videos, yet iterative denoising incurs substantial inference latency, limiting interactive and large-scale serving. Most existing…
NebulaSD: Many-for-Many Speculative Decoding
Junhao He, Hongyang Du
Speculative decoding accelerates Large Language Model (LLM) inference by using a lightweight draft model to propose candidate tokens for parallel verification by a target model. Dr…
Designer-RSI: Evolving Procedural Memory from User Traffic for Agentic Graphic Design
Hongyang Du, Lan Yan, Christian Flores +1
Professional graphic design is a long-horizon agentic task in which structured, editable artifacts emerge from many interdependent actions, yet outcomes admit no reliable programma…
CROP: Task Relevance via Counterfactuals for Selective On-Policy Distillation
Enhan Li, Junhao He, Hongyang Du
On-policy distillation (OPD) supervises a student language model on trajectories sampled from its current policy, but assigns equal credit to response tokens with unequal supervisi…
HACO: Hedged Agent Computing for Reliable LLM Systems
Enhan Li, Hongyang Du
As large language model (LLM) agents move from isolated prompting to longhorizon workflows, failures increasingly arise at the role-to-instance binding boundary, where task-specifi…
MORES: Mobile Reasoning-as-a-Service via Distributed LLM Inference-Time Scaling
Guanchen Liu, Hongyang Du, Kaibin Huang
Inference-time scaling has emerged as an effective approach for enhancing the capabilities of Large Language Models (LLMs), addressing the growing demand for stronger reasoning wit…
Multi-SPIN: Multi-Access Speculative Inference for Cooperative Token Generation at the Edge
Haotian Zheng, Zhanwei Wang, Mingyao Cui +3
Speculative inference (SPIN) was originally developed as an efficient architecture to accelerate Large Language Models (LLMs). In this work, we propose its distributed deployment t…
StarSD: One-for-Many Speculative Decoding
Junhao He, Feiran You, Hongyang Du
Speculative decoding accelerates autoregressive generation by separating token proposal from verification, but most existing approaches are designed for single-node execution and d…
GLOVE: Global Verifier for LLM Memory-Environment Realignment
Xingkun Yin, Hongyang Du
Most existing memory-enhanced Large Language Model (LLM) approaches implicitly assume that memory validity can be established either through external evaluators that provide task-s…
VideoGPA: Distilling Geometry Priors for 3D-Consistent Video Generation
Hongyang Du, Junjie Ye, Xiaoyan Cong +7
While recent video diffusion models (VDMs) produce visually impressive results, they fundamentally struggle to maintain 3D structural consistency, often resulting in object deforma…
JAUNT: Joint Alignment of User Intent and Network State for QoE-centric LLM Tool Routing
Enhan Li, Hongyang Du
Large Language Models (LLMs) increasingly rely on emerging protocols such as the Model Context Protocol (MCP) to invoke external tools and services. However, current tool routing m…
NetMCP: Network-Aware Model Context Protocol Platform for LLM Capability Extension
Enhan Li, Hongyang Du, Kaibin Huang
Large Language Models (LLMs) remain static in functionality after training, and extending their capabilities requires integration with external data, computation, and services. The…
Experience Scaling: Post-Deployment Evolution For Large Language Models
Xingkun Yin, Kaibin Huang, Dong In Kim +1
Scaling model size, training data, and compute power have driven advances in large language models (LLMs), but these approaches are reaching saturation as human-generated text is e…
Ubiquitous Intelligence Via Wireless Network-Driven LLMs Evolution
Xingkun Yin, Feiran You, Hongyang Du +1
We introduce ubiquitous intelligence as a paradigm where Large Language Models (LLMs) evolve within wireless network-driven ecosystems. Unlike static model deployments, this approa…
Generative Feature Imputing -- A Technique for Error-resilient Semantic Communication
Jianhao Huang, Qunsong Zeng, Hongyang Du +1
Semantic communication (SemCom) has emerged as a promising paradigm for achieving unprecedented communication efficiency in sixth-generation (6G) networks by leveraging artificial…
User-Intent-Driven Semantic Communication via Adaptive Deep Understanding
Peigen Ye, Jingpu Duan, Hongyang Du +1
Semantic communication focuses on transmitting task-relevant semantic information, aiming for intent-oriented communication. While existing systems improve efficiency by extracting…
M3LLM: Model Context Protocol-aided Mixture of Vision Experts For Multimodal LLMs in Networks
Yongjie Zeng, Hongyang Du
Current Multimodal Large Language Models (MLLMs) rely on centralized architectures and often suffer from poor alignment between the input task and their fixed visual encoding modul…
LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction
Shiyuan Zhang, Tong Li, Zhu Xiao +2
Service-level mobile traffic prediction for individual users is essential for network efficiency and quality of service enhancement. However, current prediction methods are limited…
MAD-Spear: A Conformity-Driven Prompt Injection Attack on Multi-Agent Debate Systems
Yu Cui, Hongyang Du
Multi-agent debate (MAD) systems leverage collaborative interactions among large language models (LLMs) agents to improve reasoning capabilities. While recent studies have focused…
How to Design and Train Your Implicit Neural Representation for Video Compression
Matthew Gwilliam, Roy Zhang, Namitha Padmanabhan +2
Implicit neural representation (INR) methods for video compression have recently achieved visual quality and compression ratios that are competitive with traditional pipelines. How…
Empowering Intelligent Low-altitude Economy with Large AI Model Deployment
Zhonghao Lyu, Yulan Gao, Junting Chen +4
Low-altitude economy (LAE) represents an emerging economic paradigm that redefines commercial and social aerial activities. Large artificial intelligence models (LAIMs) offer trans…
ReaCritic: Reasoning Transformer-based DRL Critic-model Scaling For Wireless Networks
Feiran You, Hongyang Du
Heterogeneous Networks (HetNets) pose critical challenges for intelligent management due to the diverse user requirements and time-varying wireless conditions. These factors introd…