most citedRAGVA: Engineering Retrieval Augmented Generation-based Virtual Assistants in Practice

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

cs.SE2025

DecipherGuard: Understanding and Deciphering Jailbreak Prompts for a Safer Deployment of Intelligent Software Systems

Rui Yang, Michael Fu, Chakkrit Tantithamthavorn +3

Intelligent software systems powered by Large Language Models (LLMs) are increasingly deployed in critical sectors, raising concerns about their safety during runtime. Through an i…

cs.CR2025

AdaptiveGuard: Towards Adaptive Runtime Safety for LLM-Powered Software

Rui Yang, Michael Fu, Chakkrit Tantithamthavorn +3

Guardrails are critical for the safe deployment of Large Language Models (LLMs)-powered software. Unlike traditional rule-based systems with limited, predefined input-output spaces…

cs.AI2025

Robust, Observable, and Evolvable Agentic Systems Engineering: A Principled Framework Validated via the Fairy GUI Agent

Jiazheng Sun, Ruimeng Yang, Xu Han +5

The Agentic Paradigm faces a significant Software Engineering Absence, yielding Agentic systems commonly lacking robustness, observability, and evolvability. To address these defic…

cs.CL2025

SEALGuard: Safeguarding the Multilingual Conversations in Southeast Asian Languages for LLM Software Systems

Wenliang Shan, Michael Fu, Rui Yang +1

Safety alignment is critical for LLM-powered systems. While recent LLM-powered guardrail approaches such as LlamaGuard achieve high detection accuracy of unsafe inputs written in E…

cs.SE20251 cited

RAGVA: Engineering Retrieval Augmented Generation-based Virtual Assistants in Practice

Rui Yang, Michael Fu, Chakkrit Tantithamthavorn +3

Retrieval-augmented generation (RAG)-based applications are gaining prominence due to their ability to leverage large language models (LLMs). These systems excel at combining retri…