Publications (7)
Agent-to-Agent Theory of Mind: Testing Interlocutor Awareness among Large Language Models
Younwoo Choi, Changling Li, Yongjin Yang +1
As large language models (LLMs) are increasingly integrated into multi-agent and human-AI systems, understanding their awareness of both self-context and conversational partners is…
ROER: Regularized Optimal Experience Replay
Changling Li, Zhang-Wei Hong, Pulkit Agrawal +2
Experience replay serves as a key component in the success of online reinforcement learning (RL). Prioritized experience replay (PER) reweights experiences by the temporal differen…
Scaling up Energy-Aware Multi-Agent Reinforcement Learning for Mission-Oriented Drone Networks with Individual Reward
Changling Li, Ying Li
Multi-agent reinforcement learning (MARL) has shown wide applicability in collaborative systems such as autonomous driving and smart cities for its ability of learning through inte…
Mitigating Data Redundancy to Revitalize Transformer-based Long-Term Time Series Forecasting System
Mingjie Li, Rui Liu, Guangsi Shi +5
Long-term time-series forecasting (LTSF) is fundamental to various real-world applications, where Transformer-based models have become the dominant framework due to their ability t…
Energy-Aware Multi-Agent Reinforcement Learning for Collaborative Execution in Mission-Oriented Drone Networks
Ying Li, Changling Li, Jiyao Chen +1
Mission-oriented drone networks have been widely used for structural inspection, disaster monitoring, border surveillance, etc. Due to the limited battery capacity of drones, missi…
ClarQ-LLM: A Benchmark for Models Clarifying and Requesting Information in Task-Oriented Dialog
Yujian Gan, Changling Li, Jinxia Xie +3
We introduce ClarQ-LLM, an evaluation framework consisting of bilingual English-Chinese conversation tasks, conversational agents and evaluation metrics, designed to serve as a str…