4 citations · 8 across the 13 of their papers we have counts for
7 papers · 1 filter
Gradient Mirage: Trainable yet Label-Unidentifiable Gradients in Large Language Model Split Learning
Shiyu Miao, Yunlong Mao, Zirui Huang +5
Gradient matching attacks (GMAs) in LLM split learning (SL) rely on a critical yet underexplored assumption: the gradient exposed at the split interface is a faithful derivative of…
DS-Lighting: Making Agent Harnesses Explicit for Data-Science Automation
Fan Liu, Hao Liu
Large Language Model (LLM) agents have shown promise for automating data-science workflows, yet their end-to-end performance depends critically on the agent harness that represents…
SelfWAM: A Self-Grounded Unified World Action Model for Fast Robot Control
Bikang Pan, Fan Liu, Haotao Lu +2
World Action Models (WAMs) improve robot policy learning by jointly modeling actions and future observations. However, conditioning future prediction only on the task prompt and ob…
SIREN: Towards End-to-End Extreme-Weather Early Warning with Experience-Grounded LLM Agents
Hang Ni, Weijia Zhang, Fan Liu +2
Early warning of extreme weather is essential for mitigating the societal, economic, and environmental risks posed by hazardous weather events. However, expert-centered warning wor…
DSWorld: A Data Science World Model for Efficient Autonomous Agents
Zherui Yang, Fan Liu, Hao Liu
Despite strong capabilities in data understanding and decision-making, autonomous data science agents still heavily rely on trial-and-error workflows that involve expensive computa…
ChartLens: A Dual-Branch Framework for Chart Data Correction and Factual Summary Refinement
Hao Liu, Ruping Cao, Kun Wang +4
In this report, we present our champion solution for the DataMFM Challenge Track 2: Chart Understanding. This track requires models to recover structured chart data and generate fa…
EvoDS: Self-Evolving Autonomous Data Science Agent with Skill Learning and Context Management
Zherui Yang, Fan Liu, Yansong Ning +1
Recent progress in Large Language Model (LLM) agents has enabled promising advances in automated data science. However, existing approaches remain fundamentally limited by their st…