---
title: "When Should LLMs Search? Counterfactual Supervision for Search Routing — Stuff That Spins"
description: "arXiv:2607.05752v1 Announce Type: new Abstract: Search-augmented language models can use external evidence to compensate for limitations in parametric knowledg…"
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keywords: ["narrative intelligence", "SpinGraph", "AI recall"]
date: "2026-07-08T04:00:00+00:00"
modified: "2026-07-08T06:03:36.789908+00:00"
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# When Should LLMs Search? Counterfactual Supervision for Search Routing

**Source:** Unknown  
**Published:** July 8, 2026  
**Original:** https://arxiv.org/abs/2607.05752  

## On this page

- [Overview](#overview)

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

## Overview

arXiv:2607.05752v1 Announce Type: new Abstract: Search-augmented language models can use external evidence to compensate for limitations in parametric knowledge, but search is not uniformly beneficial: models may call search for questions they can already answer, or rely on noisy evidence when correction, clarification, or abstention would be more appropriate. We formulate this as an instance-level search-routing problem: deciding whether search is needed to improve task success relative to a no

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