50 citations · 55 across the 11 of their papers we have counts for
6 papers · 1 filter
SkillAdam: Stable and Efficient Skill Evolution for Agents
Gaoyuan Li, Meihao Fan, Yizhe Liu +7
Agent skills provide a lightweight way to equip frozen language-model agents with domain knowledge and procedural guidance, yet obtaining high-quality skills remains costly and dif…
DA-Studio: An Agentic System for End-to-End Data Analysis
Yizhe Liu, Shaolei Zhang, Ju Fan
Real-world data analysis is a multi-step process over heterogeneous inputs rather than merely producing a final answer. A practical system should autonomously organize multi-step w…
CODA-BENCH: Can Code Agents Handle Data-Intensive Tasks?
Yuxin Zhang, Ju Fan, Meihao Fan +2
Advanced agents are increasingly demonstrating the potential to operate as autonomous engineers, creating a growing demand for evaluation benchmarks that capture the complexity of…
TACO: A Benchmark for Open-Domain Text-to-SQL with Ambiguous and Cross-Database Queries
Chao Deng, Ju Fan, Yuyu Luo +7
Text-to-SQL aims to translate natural language questions into executable SQL queries over structured databases. Existing benchmarks mainly focus on closed-domain settings with pred…
DataEvolver: Automatic Data Preparation for Large Language Models through Multi-Level Self-Evolving
Chao Deng, Shaolei Zhang, Ju Fan +1
High-quality training data is essential to large language models (LLMs) and typically requires extensive and costly manual curation. Automatic data preparation systems support reus…
DeepPrep: An LLM-Powered Agentic System for Autonomous Data Preparation
Meihao Fan, Ju Fan, Yuxin Zhang +7
Data preparation, which aims to transform heterogeneous and noisy raw tables into analysis-ready data, remains a major bottleneck in data science. Recent approaches leverage large…