Spreadsheets and tables are widely used representations for structured data analysis, but effective analysis still requires substantial manual effort and domain expertise. Recent large language model (LLM) agents can automate parts of this process, but they often provide limited transparency into intermediate decisions, rely on implicit assumptions, struggle with multi-table comparison, and repeat similar workflows without adapting to a user's preferences.
arXiv:2506. 12339v2 Announce Type: replace-cross Abstract: We present SheetMind, a modular multi-agent framework powered by large language models (LLMs) for spreadsheet automation via natural language instructions.
By Xi Cheng, Ruiyan Zhu, Ke Liu, Rakesh Chowdary Machineni, Lyuhao Chen, Brian Zhu, Daniel Jin, Zheng Qi, Neeraj Parihar, Zhoutian Xu, Oliver Gao
arXiv:2604. 12147v3 Announce Type: replace-cross Abstract: Agents are commonly instructed to follow a task-specific plan for guidance.
By Shuyang Liu, Saman Dehghan, Jatin Ganhotra, Martin Hirzel, Reyhaneh Jabbarvand
arXiv:2603. 10002v2 Announce Type: replace-cross Abstract: We consider the task of end-to-end spreadsheet generation, where language models produce spreadsheet artifacts to satisfy users' explicit and implicit constraints, specified in natural language.
By Srivatsa Kundurthy, Clara Na, Michael Handley, Zach Kirshner, Chen Bo Calvin Zhang, Manasi Sharma, Emma Strubell, John Ling
The paper titled "Professional Software Developers Don't Vibe, They Control: AI Agent Use for Coding in 2025" examines how experienced developers employ AI agents in software development. Through field observations and surveys, it finds that developers value agents for productivity but maintain control over design and implementation to ensure quality. They use agents as collaborative tools rather than full delegation, selecting tasks based on suitability and leveraging their expertise to guide agent behavior.
By Ruanqianqian Huang, Avery Reyna, Sorin Lerner, Haijun Xia, Brian Hempel
arXiv:2603. 02070v3 Announce Type: replace Abstract: When automating plan generation for a real-world sequential decision problem, the goal is often not to replace the human planner, but to facilitate an iterative reasoning and elicitation process, where the human's role is to guide the AI planner according to their preferences and expertise.
By Guilhem Fouilh\'e, Rebecca Eifler, Antonin Poch\'e, Sylvie Thi\'ebaux, Nicholas Asher
arXiv:2608. 12355v1 Announce Type: cross Abstract: Recent progress in AI coding agent research has led to rapid improvements in agents' ability to autonomously perform complex software engineering tasks, from editing large codebases to executing long-horizon development workflows.
By Zora Z. Wang, John Yang, Kilian Lieret, Alexa Tartaglini, Valerie Chen, Yuxiang Wei, Zijian Wang, Lingming Zhang, Karthik Narasimhan, Ludwig Schmidt, Graham Neubig, Daniel Fried, Diyi Yang
arXiv:2609.22249v1 Announce Type: new
Abstract: This paper treats prompt engineering as a discipline for turning informal human intent into structured AI work specifications. It develops the practice...
By Erfan Loweimi, Hadi Daneshvar, Samira Loveymi, Samir Ouelha, Zhengjun Yue, Hajar Mozaffar, Saturnino Luz
Agentick is a unified benchmark for sequential decision‑making agents that evaluates RL, LLM, VLM, hybrid, and human agents on 37 procedurally generated tasks across six capability categories, four difficulty levels, and five observation modalities via a single Gymnasium‑compatible interface. It includes a Coding API, oracle reference policies, pre‑built SFT datasets, a composable agent harness, and a live leaderboard. An evaluation of 27 configurations and over 90,000 episodes shows no single approach dominates, with GPT‑5 mini leading overall, PPO excelling in planning and multi‑agent tasks, and the reasoning harness boosting LLM performance by 3‑10×, while ASCII observations outperform natural language.
By Roger Creus Castanyer, Pablo Samuel Castro, Glen Berseth
arXiv:2510. 04452v3 Announce Type: replace-cross Abstract: Computer use agents (or "agents") are generative AI that automates actions within user interfaces from user commands.
By Jenny T. Liang, Titus Barik, Jeffrey Nichols, Eldon Schoop, Ruijia Cheng
arXiv:2608.23035v1 Announce Type: new
Abstract: As on-device LLM agents evolve into personal copilots, the mobile operating system has become a key testbed for this paradigm, making rigorous capabili...
By Yi Zhu, Xiongwei Wu, Qiyi Wang, Tingyu Qu, Jiajun Liu, Sihan Cao, Long Chen, Weigao Sun, Feida Zhu, Yiran Zhong, Steven Hoi
The paper investigates whether large language model–based coding agents can automatically synthesize programs that solve generalized task and motion planning (TAMP) problems across diverse instances. Using Claude Code and Codex, the authors evaluate 980 generated programs on 100 held‑out environments from KinDER and PDDLStream, achieving mean success rates between 56 % and 95 %—higher than hand‑engineered planners and other baselines—while requiring an order of magnitude less computation per instance. The study demonstrates that coding agents can calibrate physical models, test edge cases, and refine strategies, suggesting they are a strong baseline for generalized TAMP.
By Matteo Merler, Bowen Li, Josh Roy, Yichao Liang, Qianwei Wang, Yixuan Huang, Tom Silver