6 papers
Tool Specifications Matter: Uncovering and Mitigating Safety Risks in AI Agents
Minghui Pan, Jiayuxuan Yang, Yuanyuan Yuan +2
AI agents extend large language models (LLMs) with external tools, enabling them to perform complex tasks and translate model outputs into consequential real-world actions. Yet LLM…
An Empirical Study of Downstream Adaptation for Agent Skills
Xinjian Wu, Jingzhi Gong, Gunel Jahangirova +2
As Large Language Model (LLM) agents become integral to modern software systems, ``skills'' have emerged as a novel unit of software reuse, enabling developers to package workflows…
TransAgent: Enhancing LLM-Based Code Translation via Fine-Grained Execution Alignment
Zhiqiang Yuan, Weitong Chen, Hanlin Wang +3
Code translation transforms code between programming languages while preserving functionality, which is critical in software development and maintenance. While traditional learning…
Large Language Model-Based Agents for Software Engineering: A Survey
Junwei Liu, Kaixin Wang, Yixuan Chen +4
The recent advance in Large Language Models (LLMs) has shaped a new paradigm of AI agents, i.e., LLM-based agents. Compared to standalone LLMs, LLM-based agents substantially exten…
BAMAS: Structuring Budget-Aware Multi-Agent Systems
Liming Yang, Junyu Luo, Xuanzhe Liu +2
Large language model (LLM)-based multi-agent systems have emerged as a powerful paradigm for enabling autonomous agents to solve complex tasks. As these systems scale in complexity…
Can Agents Fix Agent Issues?
Alfin Wijaya Rahardja, Junwei Liu, Weitong Chen +2
LLM-based agent systems are emerging as a new software paradigm and have been widely adopted across diverse domains such as medicine, robotics, and programming. However, maintainin…