most citedLLM/Agent-as-Data-Analyst: A Survey

2 citations · 2 across the 7 of their papers we have counts for

collaborators

7 papers

cs.AI2026

SPARK: Skeleton-Guided Reasoning Synthesis from Large-Scale Scientific Literature

Yu Li, Wei Li, Xin Gao +4

Scientific reasoning remains challenging for open-source models, largely due to the lack of high-quality scientific reasoning data. Existing datasets are often dominated by factual…

cs.AI2026

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs

Yu Li, Xiaoran Shang, Qizhi Pei +11

Post-training data plays a pivotal role in shaping the capabilities of Large Language Models (LLMs), yet datasets are often treated as isolated artifacts, overlooking the systemic…

cs.CV2026

Scientific Graphics Program Synthesis via Dual Self-Consistency Reinforcement Learning

Juekai Lin, Yun Zhu, Honglin Lin +6

Graphics Program Synthesis is pivotal for interpreting and editing visual data, effectively facilitating the reverse-engineering of static visuals into editable TikZ code. While Ti…

cs.CV2026

ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch

Zheng Liu, Honglin Lin, Chonghan Qin +13

Chart reasoning is a critical capability for Vision Language Models (VLMs). However, the development of open-source models is severely hindered by the lack of high-quality training…

cs.CL2025

Closing the Data Loop: Using OpenDataArena to Engineer Superior Training Datasets

Xin Gao, Xiaoyang Wang, Yun Zhu +3

The construction of Supervised Fine-Tuning (SFT) datasets is a critical yet under-theorized stage in the post-training of Large Language Models (LLMs), as prevalent practices often…

cs.AI2025

OpenDataArena: A Fair and Open Arena for Benchmarking Post-Training Dataset Value

Mengzhang Cai, Xin Gao, Yu Li +13

The rapid evolution of Large Language Models (LLMs) is predicated on the quality and diversity of post-training datasets. However, a critical dichotomy persists: while models are r…

cs.AI2025★ 2 cited

LLM/Agent-as-Data-Analyst: A Survey

Zirui Tang, Weizheng Wang, Zihang Zhou +16

Large language models (LLMs) and agent techniques have brought a fundamental shift in the functionality and development paradigm of data analysis tasks (a.k.a LLM/Agent-as-Data-Ana…