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20232026
most citedAre We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Review

9 citations · 31 across the 26 of their papers we have counts for

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7 papers · 1 filter

cs.AI2025

BrowseMaster: Towards Scalable Web Browsing via Tool-Augmented Programmatic Agent Pair

Xianghe Pang, Shuo Tang, Rui Ye +3

Effective information seeking in the vast and ever-growing digital landscape requires balancing expansive search with strategic reasoning. Current large language model (LLM)-based…

cs.AI2025★ 1 cited

SciMaster: Towards General-Purpose Scientific AI Agents, Part I. X-Master as Foundation: Can We Lead on Humanity's Last Exam?

Jingyi Chai, Shuo Tang, Rui Ye +8

The rapid advancements of AI agents have ignited the long-held ambition of leveraging them to accelerate scientific discovery. Achieving this goal requires a deep understanding of…

cs.AI2025

Incentivizing Inclusive Contributions in Model Sharing Markets

Enpei Zhang, Jingyi Chai, Rui Ye +2

While data plays a crucial role in training contemporary AI models, it is acknowledged that valuable public data will be exhausted in a few years, directing the world's attention t…

cs.CL2025

ML-Agent: Reinforcing LLM Agents for Autonomous Machine Learning Engineering

Zexi Liu, Jingyi Chai, Xinyu Zhu +5

The emergence of large language model (LLM)-based agents has significantly advanced the development of autonomous machine learning (ML) engineering. However, the dominant prompt-ba…

cs.AI2025

FedMABench: Benchmarking Mobile Agents on Decentralized Heterogeneous User Data

Wenhao Wang, Zijie Yu, Rui Ye +3

Mobile agents have attracted tremendous research participation recently. Traditional approaches to mobile agent training rely on centralized data collection, leading to high cost a…

cs.CL2025

MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systems

Rui Ye, Shuo Tang, Rui Ge +4

LLM-based multi-agent systems (MAS) have shown significant potential in tackling diverse tasks. However, to design effective MAS, existing approaches heavily rely on manual configu…

cs.AI2025

MobileA3gent: Training Mobile GUI Agents Using Decentralized Self-Sourced Data from Diverse Users

Wenhao Wang, Mengying Yuan, Zijie Yu +5

The advancement of mobile GUI agents has opened new opportunities for automating tasks on mobile devices. Training these agents requires large-scale high-quality data, which is pro…