---
id: 20260724-T0-07
title: "BatchDAG：让LLM用执行图处理企业级跨实体分析"
title_en: "BatchDAG Uses LLM-Planned Execution Graphs for Enterprise-Scale Analysis"
url: https://ai.daily.yangsir.net/daily/20260724-T0-07
issue_date: 2026-07-24
publish_date: 2026-07-23T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2607.18241
---

# BatchDAG：让LLM用执行图处理企业级跨实体分析

arXiv 新论文提出 BatchDAG，通过 LLM 规划的 DAG（有向无环图）执行图，解决大模型在企业级数据上的穷尽分析难题。传统 LLM 在处理跨实体查询时容易因上下文溢出、失去实体归属和线性延迟而失效。BatchDAG 将查询分解为并行子任务，显著提升准确率和速度，适合金融、供应链等大规模数据分析场景。

## English Version

**BatchDAG Uses LLM-Planned Execution Graphs for Enterprise-Scale Analysis**

A new arXiv paper introduces BatchDAG, which uses LLM-planned DAG execution graphs to handle exhaustive cross-entity analysis on enterprise-scale data. It overcomes context overflow, loss of attribution, and linear latency in traditional LLM approaches by decomposing queries into parallel subtasks. It’s designed for financial, supply chain, and other large-scale analytical workloads.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2607.18241)

**详情页**：https://ai.daily.yangsir.net/daily/20260724-T0-07

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