activity
20232026
most citedOpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning

7 citations · 14 across the 22 of their papers we have counts for

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7 papers · 1 filter

cs.CL2025

Mining Useful General Data for Low-Resource Domain Adaptation

Pingjie Wang, Hongcheng Liu, Yusheng Liao +5

Adapting large language models (LLMs) to low-resource domains remains challenging due to the scarcity of domain-specific data. While in-domain data is limited, there exists a vast…

cs.CL2025

InfoMosaic-Bench: Evaluating Multi-Source Information Seeking in Tool-Augmented Agents

Yaxin Du, Yuanshuo Zhang, Xiyuan Yang +10

Information seeking is a fundamental requirement for humans. However, existing LLM agents rely heavily on open-web search, which exposes two fundamental weaknesses: online content…

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

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.SE2025

SWE-Dev: Evaluating and Training Autonomous Feature-Driven Software Development

Yaxin Du, Yuzhu Cai, Yifan Zhou +6

Large Language Models (LLMs) have shown strong capability in diverse software engineering tasks. However, feature-driven development, a highly prevalent real-world task that involv…

cs.LG2025

VLMGuard-R1: Proactive Safety Alignment for VLMs via Reasoning-Driven Prompt Optimization

Menglan Chen, Xianghe Pang, Jingjing Dong +3

Aligning Vision-Language Models (VLMs) with safety standards is essential to mitigate risks arising from their multimodal complexity, where integrating vision and language unveils…

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…