activity
20242026
most citedLarge Language Model-Brained GUI Agents: A Survey

7 citations · 24 across the 31 of their papers we have counts for

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7 papers · 1 filter

cs.AI2025★ 2 cited

Large Action Models: From Inception to Implementation

Lu Wang, Fangkai Yang, Chaoyun Zhang +15

As AI continues to advance, there is a growing demand for systems that go beyond language-based assistance and move toward intelligent agents capable of performing real-world actio…

cs.AI2024

RuAG: Learned-rule-augmented Generation for Large Language Models

Yudi Zhang, Pei Xiao, Lu Wang +11

In-context learning (ICL) and Retrieval-Augmented Generation (RAG) have gained attention for their ability to enhance LLMs' reasoning by incorporating external knowledge but suffer…

cs.AI2024★ 7 cited

Large Language Model-Brained GUI Agents: A Survey

Chaoyun Zhang, Shilin He, Jiaxu Qian +10

GUIs have long been central to human-computer interaction, providing an intuitive and visually-driven way to access and interact with digital systems. The advent of LLMs, particula…

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.AI2024★ 1 cited

The Vision of Autonomic Computing: Can LLMs Make It a Reality?

Zhiyang Zhang, Fangkai Yang, Xiaoting Qin +6

The Vision of Autonomic Computing (ACV), proposed over two decades ago, envisions computing systems that self-manage akin to biological organisms, adapting seamlessly to changing e…

cs.AI2024

Thread: A Logic-Based Data Organization Paradigm for How-To Question Answering with Retrieval Augmented Generation

Kaikai An, Fangkai Yang, Liqun Li +10

Recent advances in retrieval-augmented generation (RAG) have substantially improved question-answering systems, particularly for factoid '5Ws' questions. However, significant chall…