arXiv AI By Nicole Rose Schneider, Davide Ghilardi, Giacomo Piccinini, Jacopo Tagliabue

"Skill Issues'': Data-Centric Optimization of Lakehouse Agents

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arXiv:2606. 01185v2 Announce Type: replace Abstract: Coding agents are becoming users of data infrastructure, but their success depends not only on model quality: it also depends on the skills and environment files that teach agents how to use a system.

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arXiv AI
Jun 2

"Skill issues'': data-centric optimization of lakehouse agents

arXiv:2606. 01185v1 Announce Type: new Abstract: Coding agents are becoming users of data infrastructure, but their success depends not only on model quality: it also depends on the skills and environment files that teach agents how to use a system.

By Nicole Rose Schneider, Davide Ghilardi, Giacomo Piccinini, Jacopo Tagliabue
arXiv AI
Jul 21

DataFlow-Harness: A Grounded Code-Agent Platform for Constructing Editable LLM Data Pipelines

arXiv:2607. 16617v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to automate data-processing workflows, yet coding agents typically produce scripts that are not automatically materialized as persistent, editable platform artifacts.

By Runming He, Zhen Hao Wong, Hao Liang, Zimo Meng, Chengyu Shen, Xiaochen Ma, Wentao Zhang
arXiv AI
Aug 12

DSAgentBench: Can Agents Automate End-to-End Data-Science Workflows in Real Computer Environments?

arXiv:2608. 10366v1 Announce Type: new Abstract: Real-world data science involves long-horizon workflows that span data wrangling, exploration, modeling, visualization, and validation, and require coordinated use of tools such as notebooks, IDEs, terminals, browsers, and databases within real operating environments.

By Mizanur Rahman, Mohammed Saidul Islam, Ridwan Mahbub, Md Tahmid Rahman Laskar, Shafiq Joty, Enamul Hoque Prince
arXiv AI
4d ago

SkillGym: Training Skill-Use Agents with Automatic Verifiable Environment Generation

SkillGym is an automatic pipeline that generates verifiable environments for training skill-use agents. It crawls internet skills, filters for reproducible workflows, and uses a builder‑reviewer process to create difficulty‑controlled tasks with reference solutions and verifiers. The system builds 6.8k environments, collects 19k successful trajectories, and fine‑tunes LLMs from 2B to 122B parameters, improving performance and skill invocation rates.

By Renxi Wang, Mingshan Hee, Fajri Koto, Timothy Baldwin, Haonan Li
arXiv AI
Sep 21

CodeMidas: Scaling Agentic Coding RL Environments from Code Itself

arXiv:2609.22068v1 Announce Type: new Abstract: Training capable coding agents via reinforcement learning (RL) requires diverse tasks with reliable verifiers. Open-source codebases offer a rich sourc...

By Bowen Ye, Lei Li, Shicheng Li, Zihao Yue, Linghao Zhang, Hanglong Lv, Yuanxin Liu, Wenhan Ma, Hao Tian, Rang Li, Jinhao Dong, Yikai Zhao, Xiangwei Deng, Hailin Zhang, Liang Zhao, Qi Liu, Lingpeng Kong, Tong Yang, Fuli Luo