---
title: "When Does In-Context Search Help? A Sampling-Complexity Theory of Reflection-Driven Reasoning — Stuff That Spins"
description: "arXiv:2607.06720v1 Announce Type: new Abstract: Training large language models (LLMs) with extended reasoning has enabled in-context search, in which models it…"
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date: "2026-07-09T04:00:00+00:00"
modified: "2026-07-09T06:02:20.30288+00:00"
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# When Does In-Context Search Help? A Sampling-Complexity Theory of Reflection-Driven Reasoning

**Source:** Unknown  
**Published:** July 9, 2026  
**Original:** https://arxiv.org/abs/2607.06720  

## On this page

- [Overview](#overview)

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## Overview

arXiv:2607.06720v1 Announce Type: new Abstract: Training large language models (LLMs) with extended reasoning has enabled in-context search, in which models iteratively generate, critique, and revise solution attempts. We provide a theoretical analysis of in-context search by modeling it as approximate inference over reasoning traces, where the base model defines a prior and self-reflection provides feedback for posterior updates, and study the resulting inference-time sampling complexity - the 

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