---
id: 20260925-T0-07
title: "大规模推理的上下文表示：从海量异构来源中抽取关键信息"
title_en: "Principled Context Representation for Large-Scale Reasoning Across Vast Sources"
url: https://ai.daily.yangsir.net/daily/20260925-T0-07
issue_date: 2026-09-25
publish_date: 2026-09-24T04:00:00.000Z
category: research
source_name: "arXiv cs.CL (NLP)"
source_url: https://arxiv.org/abs/2609.27173
---

# 大规模推理的上下文表示：从海量异构来源中抽取关键信息

科学、医学、法律和金融等领域的复杂任务，常需要整合分散在远超模型上下文限制的海量异构来源中的相互依赖信息。现有方法试图应对这一挑战，但该研究提出有原则的上下文表示方法。核心思路是识别并保留真正重要的信息，而不是简单堆叠上下文。对需要跨文档推理的专业场景，这一方法有望提升准确率并降低无效上下文带来的干扰。

## English Version

**Principled Context Representation for Large-Scale Reasoning Across Vast Sources**

Complex tasks in science, medicine, law, and finance often require assembling interdependent information scattered across vast, heterogeneous sources far beyond model context limits. Existing approaches attempt to address this, but the study proposes a principled context representation method. The core idea is to identify and retain what truly matters rather than simply stacking context. For professional scenarios requiring cross-document reasoning, this could improve accuracy and reduce interference from irrelevant context.

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**来源**：[arXiv cs.CL (NLP)](https://arxiv.org/abs/2609.27173)

**详情页**：https://ai.daily.yangsir.net/daily/20260925-T0-07

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