---
id: 20260425-T0-01
title: "Absorber LLM：用因果同步解决长文本内存瓶颈"
title_en: "Absorber LLM Solves Long-Context Memory Bottleneck"
url: https://ai.daily.yangsir.net/daily/20260425-T0-01
issue_date: 2026-04-25
publish_date: 2026-04-24T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2604.20915
---

# Absorber LLM：用因果同步解决长文本内存瓶颈

斯坦福研究提出Absorber LLM模型，通过因果同步机制将长文本推理内存消耗降至常数级。该模型采用压缩历史状态的方法，解决了Transformer模型随序列长度增加导致计算成本激增的问题。实验表明，在保持高准确率的同时，内存占用仅为传统方法的1/10，特别适合实时处理长文本流的应用场景。

## English Version

**Absorber LLM Solves Long-Context Memory Bottleneck**

Stanford researchers introduce Absorber LLM, a model that uses causal synchronization to reduce memory consumption for long-context inference to constant levels. By compressing historical states, it solves the computational cost explosion in Transformers. Tests show it maintains high accuracy while using 10x less memory, ideal for real-time long-text processing.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2604.20915)

**详情页**：https://ai.daily.yangsir.net/daily/20260425-T0-01

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