@inproceedings{yun-etal-2026-locality,
title = "Locality-Aware Redundancy Pruning for {LLM} Depth Compression",
author = "Yun, Vincent-Daniel and
Kim, Youngrae and
Lim, Woosang and
Heo, YoungJin and
Kim, Minkyu and
Lee, Sunwoo",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1979/",
pages = "45024--45037",
ISBN = "979-8-89176-459-0",
abstract = "Large language models are known to contain representational redundancy across network depth, making depth pruning an effective approach for improving inference efficiency. Existing one-shot pruning methods rely on local layer importance or fixed redundancy assumptions across architectures. We propose Locality-Aware Redundancy Pruning (LoRP), a training-free one-shot depth pruning framework guided by representation locality. We show that inter-layer redundancy can be either localized or globally distributed depending on the LLM architecture. To characterize this phenomenon, we introduce Representation Locality Score (RLS), derived from global inter-layer hidden-state similarity. Using a small calibration set, LoRP computes pairwise layer similarity, clusters layers by representational similarity, and allocates pruning according to residual intra-cluster redundancy. Experiments across diverse LLM families show improvements in both perplexity and downstream task accuracy."
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<abstract>Large language models are known to contain representational redundancy across network depth, making depth pruning an effective approach for improving inference efficiency. Existing one-shot pruning methods rely on local layer importance or fixed redundancy assumptions across architectures. We propose Locality-Aware Redundancy Pruning (LoRP), a training-free one-shot depth pruning framework guided by representation locality. We show that inter-layer redundancy can be either localized or globally distributed depending on the LLM architecture. To characterize this phenomenon, we introduce Representation Locality Score (RLS), derived from global inter-layer hidden-state similarity. Using a small calibration set, LoRP computes pairwise layer similarity, clusters layers by representational similarity, and allocates pruning according to residual intra-cluster redundancy. Experiments across diverse LLM families show improvements in both perplexity and downstream task accuracy.</abstract>
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%0 Conference Proceedings
%T Locality-Aware Redundancy Pruning for LLM Depth Compression
%A Yun, Vincent-Daniel
%A Kim, Youngrae
%A Lim, Woosang
%A Heo, YoungJin
%A Kim, Minkyu
%A Lee, Sunwoo
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F yun-etal-2026-locality
%X Large language models are known to contain representational redundancy across network depth, making depth pruning an effective approach for improving inference efficiency. Existing one-shot pruning methods rely on local layer importance or fixed redundancy assumptions across architectures. We propose Locality-Aware Redundancy Pruning (LoRP), a training-free one-shot depth pruning framework guided by representation locality. We show that inter-layer redundancy can be either localized or globally distributed depending on the LLM architecture. To characterize this phenomenon, we introduce Representation Locality Score (RLS), derived from global inter-layer hidden-state similarity. Using a small calibration set, LoRP computes pairwise layer similarity, clusters layers by representational similarity, and allocates pruning according to residual intra-cluster redundancy. Experiments across diverse LLM families show improvements in both perplexity and downstream task accuracy.
%U https://aclanthology.org/2026.emnlp-main.1979/
%P 45024-45037
Markdown (Informal)
[Locality-Aware Redundancy Pruning for LLM Depth Compression](https://aclanthology.org/2026.emnlp-main.1979/) (Yun et al., EMNLP 2026)
ACL
- Vincent-Daniel Yun, Youngrae Kim, Woosang Lim, YoungJin Heo, Minkyu Kim, and Sunwoo Lee. 2026. Locality-Aware Redundancy Pruning for LLM Depth Compression. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 45024–45037, Budapest, Hungary. Association for Computational Linguistics.