---
title: "Structured Pruning of Large Language Models via Power Transformation and Sign-Preserving Score Aggregation with Adaptive Feature Retention — Stuff That Spins"
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keywords: ["narrative intelligence", "SpinGraph", "AI recall"]
date: "2026-07-10T04:00:00+00:00"
modified: "2026-07-10T06:03:33.317082+00:00"
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# Structured Pruning of Large Language Models via Power Transformation and Sign-Preserving Score Aggregation with Adaptive Feature Retention

**Source:** Unknown  
**Published:** July 10, 2026  
**Original:** https://arxiv.org/abs/2607.08027  

## On this page

- [Overview](#overview)

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

## Overview

arXiv:2607.08027v1 Announce Type: new Abstract: This paper proposes an improved structured pruning method for large language models (LLMs) that addresses key challenges in adapting Adaptive Feature Retention (AFR), an unstructured pruning technique, to structured pruning. When applying AFR to structured pruning, three major problems arise: distribution mismatch between heterogeneous pruning scores, loss of sign information indicating optimization direction consistency, and influence of outliers.

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