collaborators

6 papers

cs.AI2026

SkillTrace: Multi-Trace Provenance Auditing for LLM-Agent Skill Reuse

Jialuo Chen, Minghe Wang, Lingqi Jiang +7

LLM-agent ecosystems are rapidly growing around reusable skills: mixed-modality packages of metadata, natural-language instructions, code, tools, references, and operational workfl…

cs.DC2026

FaaSMoE: A Serverless Framework for Multi-Tenant Mixture-of-Experts Serving

Minghe Wang, Trever Schirmer, Mohammadreza Malekabbasi +1

Mixture-of-Experts (MoE) models offer high capacity with efficient inference cost by activating a small subset of expert models per input. However, deploying MoE models requires al…

cs.DC2026

DisCEdge: Distributed Context Management for Large Language Models at the Edge

Mohammadreza Malekabbasi, Minghe Wang, David Bermbach

Deploying Large Language Model (LLM) services at the edge benefits latency-sensitive and privacy-aware applications. However, the stateless nature of LLMs makes managing user conte…

cs.SE2025

LLM4FaaS: No-Code Application Development using LLMs and FaaS

Minghe Wang, Tobias Pfandzelter, Trever Schirmer +1

Large language models (LLMs) show great capabilities in generating code from natural language descriptions, bringing programming power closer to non-technical users. However, their…

cs.DC2025

Exploring Influence Factors on LLM Suitability for No-Code Development of End User IoT Applications

Minghe Wang, Alexandra Kapp, Trever Schirmer +2

No-Code Development Platforms (NCDPs) empower non-technical end users to build applications tailored to their specific demands without writing code. While NCDPs lower technical bar…

cs.CR2025

A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment

Kun Wang, Guibin Zhang, Zhenhong Zhou +100

The remarkable success of Large Language Models (LLMs) has illuminated a promising pathway toward achieving Artificial General Intelligence for both academic and industrial communi…