[2606.01279] ANDES: Agent Native Data Evolving Synthesis Tool for Autonomous Instruction Alignment

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Computer Science > Artificial Intelligence

arXiv:2606.01279 (cs)

[Submitted on 31 May 2026]

Title:ANDES: Agent Native Data Evolving Synthesis Tool for Autonomous Instruction Alignment

Authors:Zhengyang Zhao, Shengjie Ye, Lu Ma, Hao Liang, Hengyi Feng, Wentao Zhang<br>View a PDF of the paper titled ANDES: Agent Native Data Evolving Synthesis Tool for Autonomous Instruction Alignment, by Zhengyang Zhao and 5 other authors

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Abstract:AI agents are increasingly being tasked with automating AI research itself, particularly the critical post-training phase that transforms base LLMs into aligned assistants. However, recent evaluations reveal that even frontier agents struggle to perform this task. While the success of post-training fundamentally relies on acquiring high-quality data, relying on agents to autonomously curate targeted training datasets from the open web introduces severe challenges. Executing the long-horizon tasks of searching, filtering, and balancing data within noisy web environments frequently overwhelms an agent's limited context, ultimately leading to degraded dataset quality and suboptimal downstream training performance. To bridge this gap, we introduce Andes (Agent Native Data Evolving Synthesis), a framework that reimagines data generation as a plug-and-play \emph{agent skill}. Rather than forcing agents to devise complex data-gathering strategies from scratch, \textsc{Andes} provides an intelligent abstraction layer. By leveraging a self-evolving World Tree routing mechanism and actionable diagnostic reports, it allows trainer agents to dynamically steer data synthesis through an interactive, closed-loop interface. We demonstrate that under strict compute constraints, equipping foundationally weaker agents with Andes improves automated alignment, securing state-of-the-art performance on PostTrainBench and robust cross-task generalization. Our project is available at this https URL.

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Artificial Intelligence (cs.AI)

Cite as:<br>arXiv:2606.01279 [cs.AI]

(or<br>arXiv:2606.01279v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.01279

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Zhengyang Zhao [view email]<br>[v1]<br>Sun, 31 May 2026 15:03:50 UTC (6,998 KB)

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