---
title: "Think Big, Search Small: Where Capacity Matters in Hierarchical Search Agents? — Stuff That Spins"
description: "arXiv:2607.07548v1 Announce Type: new Abstract: Large language model based search agents increasingly adopt multi-agent architectures in which a main agent dec…"
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keywords: ["narrative intelligence", "SpinGraph", "AI recall"]
date: "2026-07-09T04:00:00+00:00"
modified: "2026-07-09T06:03:51.646069+00:00"
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# Think Big, Search Small: Where Capacity Matters in Hierarchical Search Agents?

**Source:** Unknown  
**Published:** July 9, 2026  
**Original:** https://arxiv.org/abs/2607.07548  

## On this page

- [Overview](#overview)

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

## Overview

arXiv:2607.07548v1 Announce Type: new Abstract: Large language model based search agents increasingly adopt multi-agent architectures in which a main agent decomposes a complex question into sub-queries and dispatches them to parallel sub-agents. However, existing systems instantiate all roles from a single model of identical scale, leaving open how model capacity should be distributed across roles. We factorize hierarchical search into three roles: a delegation role responsible for task decompo

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