2 citations · 2 across the 7 of their papers we have counts for
7 papers
DCFA: Dual-view Causal-inspired Attribution for Failure Reasoning in LLM-based Multi-agent Systems
Zehao Wang, Lanjun Wang, Shilong Jin +2
Large language model (LLM)-based multi-agent systems have experienced rapid growth in recent years. Despite their promise, such systems remain fragile, frequently exhibiting reason…
When Planning Fails Despite Correct Execution: On Epistemic Calibration for LLM-Based Multi-Agent Systems
Zehao Wang, Shilong Jin, Zhao Cao +1
LLM-based multi-agent systems can fail even when planned actions are executed correctly because agents may misjudge their knowledge when evaluating plan feasibility, a phenomenon w…
SE-GA: Memory-Augmented Self-Evolution for GUI Agents
Shilong Jin, Lanjun Wang, Zhuosheng Zhang
Autonomous Graphical User Interface (GUI) agents often struggle with multi-step tasks due to constrained context windows and static policies that fail to adapt to dynamic environme…
Key Decision-Makers in Multi-Agent Debates: Who Holds the Power?
Qian Zhang, Yan Zheng, Jinyi Liu +2
Recent studies on LLM agent scaling have highlighted the potential of Multi-Agent Debate (MAD) to enhance reasoning abilities. However, the critical aspect of role allocation strat…
Exploring and Mitigating Fawning Hallucinations in Large Language Models
Zixuan Shangguan, Yanjie Dong, Lanjun Wang +3
Large language models (LLMs) have demonstrated exceptional proficiency in language understanding. However, when LLMs align their outputs with deceptive and/or misleading prompts, t…
MetaEformer: Unveiling and Leveraging Meta-patterns for Complex and Dynamic Systems Load Forecasting
Shaoyuan Huang, Tiancheng Zhang, Zhongtian Zhang +3
Time series forecasting is a critical and practical problem in many real-world applications, especially for industrial scenarios, where load forecasting underpins the intelligent o…
Automatic Data Transformation Using Large Language Model: An Experimental Study on Building Energy Data
Ankita Sharma, Xuanmao Li, Hong Guan +9
Existing approaches to automatic data transformation are insufficient to meet the requirements in many real-world scenarios, such as the building sector. First, there is no conveni…