activity
20242026
most citedFlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation

1 citations · 1 across the 6 of their papers we have counts for

collaborators

6 papers

cs.HC2026

EvoDesign: Agentic Editable Diagram Creation via Design Expertise Evolution

Tianfu Wang, Leilei Ding, Ziyang Tao +13

High-fidelity diagram creation requires the complex orchestration of semantic topology, visual styling, and spatial layout, posing a significant challenge for automated systems. Ex…

cs.AI2026

Environment-Aware Adaptive Pruning with Interleaved Inference Orchestration for Vision-Language-Action Models

Yuting Huang, Leilei Ding, Zhipeng Tang +7

While Vision-Language-Action (VLA) models hold promise in embodied intelligence, their large parameter counts lead to substantial inference latency that hinders real-time manipulat…

cs.NI2025

Virne: A Comprehensive Benchmark for RL-based Network Resource Allocation in NFV

Tianfu Wang, Liwei Deng, Xi Chen +7

Resource allocation (RA) is critical to efficient service deployment in Network Function Virtualization (NFV), a transformative networking paradigm. Recently, deep Reinforcement Le…

cs.AI2025

A Framework for Benchmarking and Aligning Task-Planning Safety in LLM-Based Embodied Agents

Yuting Huang, Leilei Ding, Zhipeng Tang +5

Large Language Models (LLMs) exhibit substantial promise in enhancing task-planning capabilities within embodied agents due to their advanced reasoning and comprehension. However,…

cs.AI2024★ 1 cited

FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation

Tianfu Wang, Qilin Fan, Chao Wang +4

Virtual network embedding (VNE) is an essential resource allocation task in network virtualization, aiming to map virtual network requests (VNRs) onto physical infrastructure. Rein…

cs.IR2024

DGR: A General Graph Desmoothing Framework for Recommendation via Global and Local Perspectives

Leilei Ding, Dazhong Shen, Chao Wang +3

Graph Convolutional Networks (GCNs) have become pivotal in recommendation systems for learning user and item embeddings by leveraging the user-item interaction graph's node informa…