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title: "Hierarchical Sparse Attention Done Right: Toward Infinite Context Modeling — Stuff That Spins"
description: "arXiv:2607.02980v1 Announce Type: new Abstract: Scaling modern large language models (LLMs) to long contexts is limited by the quadratic computation cost, and …"
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keywords: ["narrative intelligence", "SpinGraph", "AI recall"]
date: "2026-07-07T04:00:00+00:00"
modified: "2026-07-07T06:04:39.629366+00:00"
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# Hierarchical Sparse Attention Done Right: Toward Infinite Context Modeling

**Source:** Unknown  
**Published:** July 7, 2026  
**Original:** https://arxiv.org/abs/2607.02980  

## On this page

- [Overview](#overview)

<a id="overview"></a>

## Overview

arXiv:2607.02980v1 Announce Type: new Abstract: Scaling modern large language models (LLMs) to long contexts is limited by the quadratic computation cost, and poor length extrapolation of dense attention. Chunk-wise sparse attention offers a promising alternative, but all existing methods fall short of full attention because of their inaccurate chunk selection. We propose Hierarchical Landmark Sparse (HiLS) Attention, a chunk-wise sparse attention mechanism that learns chunk selection end-to-end

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