---
id: 20261022-T0-18
title: "DataPraxis用可验证工作区演化生成分析任务，训练通用Agent"
title_en: "DataPraxis Generates Analytics Tasks via Verified Workspace Evolution"
url: https://ai.daily.yangsir.net/daily/20261022-T0-18
issue_date: 2026-10-22
publish_date: 2026-10-21T04:00:00.000Z
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2610.26924
---

# DataPraxis用可验证工作区演化生成分析任务，训练通用Agent

arXiv论文DataPraxis提出一种通过可验证工作区演化来扩展分析任务的方法。通用Agent要胜任专业分析工作，需要持续推理、反复调查和可靠证据，而训练这类能力需要多样且有难度的任务，现有任务来源不足。该方法通过演化工作区并验证结果来批量生成任务。若有效，这类数据管线可为分析型Agent的训练提供更稳定的任务供给。

## English Version

**DataPraxis Generates Analytics Tasks via Verified Workspace Evolution**

The arXiv paper DataPraxis proposes scaling analytics tasks through verified workspace evolution. General-purpose agents are increasingly expected to do professional analytics work, which demands sustained reasoning, iterative investigation and reliable evidence—but training these capabilities requires diverse, challenging tasks that are in short supply. The method generates tasks by evolving workspaces and verifying outcomes. If it holds up, such data pipelines could give analytics agents a steadier supply of training tasks.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2610.26924)

**详情页**：https://ai.daily.yangsir.net/daily/20261022-T0-18

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