Bohan Lyu, Xin Cong, Heyang Yu, Pan Yang, Cheng Qian, Zihe Wang, Yujia Qin, Yining Ye, Yaxi Lu, Chen Qian, Zhong Zhang, Yukun Yan, Yankai Lin, Zhiyuan Liu, Maosong Sun. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Autonomous agents empowered by Large Language Models (LLMs) have undergone significant improvements, enabling them to generalize across a broad spectrum of tasks. However, in real-world scenarios, cooperation among individuals is often required to enhance the efficiency and effectiveness of task accomplishment. Hence, inspired by human group dynamics, we propose a multi-agent framework AgentVerse that can effectively orchestrate a collaborative group of expert agents as a greater-than-the-sum-of-its-parts system. Our experiments demonstrate that AgentVerse can proficiently deploy multi-agent groups that outperform a single agent. Extensive experiments on text understanding, reasoning, coding, tool utilization, and embodied AI confirm the effectiveness of AgentVerse. Moreover, our analysis of agent interactions within AgentVerse reveals the emergence of specific collaborative behaviors, contributing to heightened group efficiency. We will release our codebase, AgentVerse, to further facilitate multi-agent research.

Non-intrusive load monitoring (NILM) benefits both end users and utilities by perceiving the operation of individual appliances within a household merely based on analytical results of aggregated electrical data. Driven by diverse demands, a basic and practical NILM solution is on call to enable various downstream applications and strengthen the transfer ability to adapt to different real scenarios. Accordingly, an end-cloud collaborated framework for transferable NILM is proposed in this work. First, an end-to-end model with a multi-scale convolutional architecture is designed for identifying the activation of a specified target appliance using current waveform, which can be independently deployed according to actual needs. Furthermore, a transfer learning framework of NILM is established. Primarily, the model pretrained on the cloud is continuously fine tuned on the terminal side in a pseudo-supervised manner, where the group-weighted cross entropy (GWCE) is defined as the loss function. According to the experimental results based on two public datasets, the proposed model structure possesses prominent generalization ability across different appliances and scenarios, and the transfer learning procedure with GWCE can enhance the identification ability of the pretrained model, which is especially effective for unfamiliar scenarios. Provided with a NILM device eligible for terminal-side intelligence, our work is applicable with great prospects in practice.