---
id: 20260913-T0-08
title: "没有任务示例也能适配环境？任务无关预处理让Agent先建资源"
title_en: "Task-Agnostic Preprocessing Lets Agents Build Resources Without Task Examples"
url: https://ai.daily.yangsir.net/daily/20260913-T0-08
issue_date: 2026-09-13
publish_date: 2026-09-12T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2609.10824
---

# 没有任务示例也能适配环境？任务无关预处理让Agent先建资源

LLM Agent进入新环境时，可先检查可用语料和工具，构建索引、脚本或流程指南等可复用资源。但多数自动适配方法依赖任务示例来指导，限制了在新场景下的泛化能力。新研究提出任务无关的环境预处理方法，让Agent在没有具体任务示例的情况下也能为环境做好准备，提升后续任务执行效率。

## English Version

**Task-Agnostic Preprocessing Lets Agents Build Resources Without Task Examples**

When LLM agents enter a new environment, they can inspect available corpora and tools to build reusable resources like indices, scripts, or procedural guidance. But most automated adaptation methods rely on task examples for guidance, limiting generalization to new scenarios. New research proposes task-agnostic environment preprocessing, enabling agents to prepare for environments without specific task examples and improving subsequent task execution efficiency.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2609.10824)

**详情页**：https://ai.daily.yangsir.net/daily/20260913-T0-08

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