activity
20222026
most citedFedDisco: Federated Learning with Discrepancy-Aware Collaboration

25 citations · 63 across the 37 of their papers we have counts for

collaborators
Showing 2025 · cs.AIShow all

7 papers · 2 filters

cs.AI2025

Bohrium + SciMaster: Building the Infrastructure and Ecosystem for Agentic Science at Scale

Linfeng Zhang, Siheng Chen, Yuzhu Cai +46

AI agents are emerging as a practical way to run multi-step scientific workflows that interleave reasoning with tool use and verification, pointing to a shift from isolated AI-assi…

cs.AI2025

MCP-Flow: Facilitating LLM Agents to Master Real-World, Diverse and Scaling MCP Tools

Wenhao Wang, Peizhi Niu, Zhao Xu +8

Large Language Models (LLMs) increasingly rely on external tools to perform complex, realistic tasks, yet their ability to utilize the rapidly expanding Model Contextual Protocol (…

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★ 1 cited

ML-Master: Towards AI-for-AI via Integration of Exploration and Reasoning

Zexi Liu, Yuzhu Cai, Xinyu Zhu +6

As AI capabilities advance toward and potentially beyond human-level performance, a natural transition emerges where AI-driven development becomes more efficient than human-centric…

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.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…