activity
20232026
most citedTaskWeaver: A Code-First Agent Framework

6 citations · 6 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2026

Beyond State Consistency: Behavior Consistency in Text-Based World Models

Youling Huang, Guanqiao Chen, Junchi Yao +8

World models have been emerging as critical components for assessing the consequences of actions generated by interactive agents in online planning and offline evaluation. In text-…

cs.AI2025

UFO2: The Desktop AgentOS

Chaoyun Zhang, He Huang, Chiming Ni +18

Recent Computer-Using Agents (CUAs), powered by multimodal large language models (LLMs), offer a promising direction for automating complex desktop workflows through natural langua…

cs.AI2024

AI Delegates with a Dual Focus: Ensuring Privacy and Strategic Self-Disclosure

Zhiyang Zhang, Xi Chen, Fangkai Yang +7

Large language model (LLM)-based AI delegates are increasingly utilized to act on behalf of users, assisting them with a wide range of tasks through conversational interfaces. Desp…

cs.AI2024

AXIS: Efficient Human-Agent-Computer Interaction with API-First LLM-Based Agents

Junting Lu, Zhiyang Zhang, Fangkai Yang +7

Multimodal large language models (MLLMs) have enabled LLM-based agents to directly interact with application user interfaces (UIs), enhancing agents' performance in complex tasks.…

cs.LG2024

Contrastive Learning with Negative Sampling Correction

Lu Wang, Chao Du, Pu Zhao +8

As one of the most effective self-supervised representation learning methods, contrastive learning (CL) relies on multiple negative pairs to contrast against each positive pair. In…

cs.AI2023★ 6 cited

TaskWeaver: A Code-First Agent Framework

Bo Qiao, Liqun Li, Xu Zhang +16

Large Language Models (LLMs) have shown impressive abilities in natural language understanding and generation, leading to their widespread use in applications such as chatbots and…

cs.LG2023

Robust Positive-Unlabeled Learning via Noise Negative Sample Self-correction

Zhangchi Zhu, Lu Wang, Pu Zhao +7

Learning from positive and unlabeled data is known as positive-unlabeled (PU) learning in literature and has attracted much attention in recent years. One common approach in PU lea…