35 citations · 48 across the 8 of their papers we have counts for
9 papers
Agent Lightning v1.0: Towards Harnessed Agentic RL
Zhiyuan He, Siwei Zhang, Zhiwen Zhou +7
Modern agents operate inside agent harnesses that manage tools, context, and control flow, making the harness a critical part of the agent system. Our original Agent Lightning intr…
Prompt Orchestration Markup Language
Yuge Zhang, Nan Chen, Jiahang Xu +1
Large Language Models (LLMs) require sophisticated prompting, yet current practices face challenges in structure, data integration, format sensitivity, and tooling. Existing method…
Agent Lightning: Train ANY AI Agents with Reinforcement Learning
Xufang Luo, Yuge Zhang, Zhiyuan He +5
We present Agent Lightning, a flexible and extensible framework that enables Reinforcement Learning (RL)-based training of Large Language Models (LLMs) for any AI agent. Unlike exi…
R&D-Agent: An LLM-Agent Framework Towards Autonomous Data Science
Xu Yang, Xiao Yang, Shikai Fang +13
Recent advances in AI and ML have transformed data science, yet increasing complexity and expertise requirements continue to hinder progress. Although crowd-sourcing platforms alle…
VisEval: A Benchmark for Data Visualization in the Era of Large Language Models
Nan Chen, Yuge Zhang, Jiahang Xu +2
Translating natural language to visualization (NL2VIS) has shown great promise for visual data analysis, but it remains a challenging task that requires multiple low-level implemen…
Benchmarking Data Science Agents
Yuge Zhang, Qiyang Jiang, Xingyu Han +3
In the era of data-driven decision-making, the complexity of data analysis necessitates advanced expertise and tools of data science, presenting significant challenges even for spe…
Full-Cycle Energy Consumption Benchmark for Low-Carbon Computer Vision
Bo Li, Xinyang Jiang, Donglin Bai +6
The energy consumption of deep learning models is increasing at a breathtaking rate, which raises concerns due to potential negative effects on carbon neutrality in the context of…
How Does Supernet Help in Neural Architecture Search?
Yuge Zhang, Quanlu Zhang, Yaming Yang
Weight sharing, as an approach to speed up architecture performance estimation has received wide attention. Instead of training each architecture separately, weight sharing builds…
Deeper Insights into Weight Sharing in Neural Architecture Search
Yuge Zhang, Zejun Lin, Junyang Jiang +5
With the success of deep neural networks, Neural Architecture Search (NAS) as a way of automatic model design has attracted wide attention. As training every child model from scrat…