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arxiv: 2605.27786 · v2 · pith:Q4H4SNKFnew · submitted 2026-05-27 · 💻 cs.LG · cs.AI

Locality-Aware Redundancy Pruning for LLM Depth Compression

Vincent-Daniel Yun , Youngrae Kim , Woosang Lim , YoungJin Heo , Minkyu Kim , Sunwoo Lee This is my paper

Pith reviewed 2026-06-29 13:50 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords LLM pruningdepth compressionrepresentation localityredundancy pruningone-shot pruningmodel compressionhidden-state similaritylayer clustering
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The pith

LoRP prunes LLM layers by clustering them according to global hidden-state similarity.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces Locality-Aware Redundancy Pruning (LoRP), a training-free method that removes entire layers from large language models while trying to keep performance intact. It measures how similar the representations are between layers using a small set of calibration data, then groups layers into clusters where redundancy is concentrated. Pruning decisions are made inside those clusters rather than by scoring individual layers in isolation. This matters because many LLMs contain overlapping computations across depth, and a better way to locate removable layers could lower inference cost without the need for retraining. Experiments on several model families report gains in both perplexity and accuracy on downstream tasks compared with prior local-importance baselines.

Core claim

LoRP computes pairwise similarities between hidden states across layers on a calibration set, derives a Representation Locality Score that distinguishes localized from globally distributed redundancy, clusters layers by representational similarity, and allocates the number of layers to prune according to the residual redundancy inside each cluster.

What carries the argument

Representation Locality Score (RLS), computed from global inter-layer hidden-state similarity, which both characterizes redundancy distribution and determines how many layers to remove from each similarity cluster.

If this is right

  • Pruning can be adapted automatically to whether an architecture shows localized or distributed redundancy.
  • One-shot depth compression becomes viable across families without architecture-specific tuning.
  • A small calibration set suffices to decide layer removals that preserve both perplexity and task accuracy.
  • Models whose redundancy is globally distributed benefit more from the clustering step than from per-layer scoring.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same similarity clustering could be tested as a diagnostic tool to decide how many layers an architecture should have before training.
  • If the calibration set is expanded or chosen differently, the identified clusters might shift and change the pruning outcome.
  • Extending the method to prune non-consecutive layers within a cluster could be checked against the current consecutive-removal rule.

Load-bearing premise

Pairwise hidden-state similarity measured on a small calibration set is enough to identify which layers inside a cluster can be removed without harming overall model capability more than local baselines.

What would settle it

Running LoRP and a local-importance baseline on the same new LLM family and calibration set, then finding that the local baseline yields lower perplexity or higher downstream accuracy, would falsify the central claim.

Figures

Figures reproduced from arXiv: 2605.27786 by Minkyu Kim, Sunwoo Lee, Vincent-Daniel Yun, Woosang Lim, Youngjin Heo, Youngrae Kim.

Figure 1
Figure 1. Figure 1: Pairwise inter-layer hidden-state cosine similarity matrices [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Mean inter-layer hidden-state cosine similarity as a function of normalized layer distance. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of LoRP. (1) Input hidden states are collected from every Transformer block using a small calibration set. (2) A pairwise inter-layer similarity matrix S reveals whether redundancy is localized or distributed across depth. (3) The Representation Locality Score (RLS) summarizes this structure from the global off-diagonal similarity. (4) Layers are grouped into clusters via spectral clustering on th… view at source ↗
Figure 4
Figure 4. Figure 4: Visualization of pruned layer indices selected by different training-free depth pruning meth [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Effect of the number of representational clusters [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
read the original 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. Official github repository: https://github.com/daniel-eai/LoRP-Locality-Aware-Redundancy-Pruning/

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes Locality-Aware Redundancy Pruning (LoRP), a training-free one-shot depth pruning framework for LLMs. It introduces the Representation Locality Score (RLS) computed from pairwise inter-layer hidden-state similarities on a small calibration set, clusters layers according to representational similarity, and allocates pruning to exploit residual intra-cluster redundancy. The central claim is that this locality-aware allocation yields better perplexity and downstream-task accuracy than local-importance baselines across multiple LLM families.

Significance. If the empirical gains are robust, the work offers a practical, architecture-sensitive alternative to uniform or locally greedy depth pruning. Distinguishing localized versus globally distributed redundancy via RLS is a useful conceptual contribution, and the public GitHub repository supports reproducibility. The training-free, one-shot nature aligns with common deployment constraints.

major comments (2)
  1. [Method] Method section (RLS and clustering description): the central claim that pre-pruning pairwise similarities on a calibration set correctly identify removable intra-cluster redundancy is load-bearing, yet the manuscript provides no analysis of how layer removal changes the input distribution to downstream layers. Because depth pruning is sequential, similarities measured on the intact model need not predict post-pruning behavior; an ablation or sensitivity study on calibration-set size, prompt distribution, or similarity metric is required to substantiate the allocation rule.
  2. [Experiments] Experiments section (results tables): the reported improvements in perplexity and accuracy lack error bars, multiple random seeds, or explicit variation over calibration-set size and similarity metric. Without these, it is impossible to determine whether the gains over local-importance baselines are statistically reliable or sensitive to the narrow calibration distribution.
minor comments (1)
  1. [Abstract / Method] The abstract states that RLS is 'derived from global inter-layer hidden-state similarity' but does not specify the exact similarity function or normalization; this notation should be clarified in the main text with an equation.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the major comments point by point below and commit to revisions that will strengthen the empirical support for our claims.

read point-by-point responses

  1. Referee: [Method] Method section (RLS and clustering description): the central claim that pre-pruning pairwise similarities on a calibration set correctly identify removable intra-cluster redundancy is load-bearing, yet the manuscript provides no analysis of how layer removal changes the input distribution to downstream layers. Because depth pruning is sequential, similarities measured on the intact model need not predict post-pruning behavior; an ablation or sensitivity study on calibration-set size, prompt distribution, or similarity metric is required to substantiate the allocation rule.

    Authors: We acknowledge that sequential layer removal can induce distribution shifts to downstream layers, and that pre-pruning similarities on the intact model do not automatically guarantee post-pruning behavior. Our design choice rests on the empirical finding that RLS-derived clusters identify redundancies whose removal improves perplexity and accuracy across architectures; however, to directly address the concern we will add a sensitivity analysis on calibration-set size, prompt distribution, and similarity metric in the revised manuscript. revision: yes

  2. Referee: [Experiments] Experiments section (results tables): the reported improvements in perplexity and accuracy lack error bars, multiple random seeds, or explicit variation over calibration-set size and similarity metric. Without these, it is impossible to determine whether the gains over local-importance baselines are statistically reliable or sensitive to the narrow calibration distribution.

    Authors: The gains are observed consistently across multiple LLM families and tasks. To establish statistical reliability we will report error bars from multiple random seeds and include explicit ablations over calibration-set size and similarity metric in the revised experiments. revision: yes

Circularity Check

0 steps flagged

No circularity: RLS and pruning allocation derived from observable calibration-set similarities without self-referential reduction.

full rationale

The paper computes pairwise layer similarities on a small calibration set, derives RLS from global inter-layer hidden-state similarity, clusters layers, and allocates pruning by residual intra-cluster redundancy. No quoted equations or steps show the allocation reducing to a fitted parameter renamed as prediction, a self-citation load-bearing premise, or an ansatz smuggled via prior work; the central procedure remains an explicit computation from data rather than a definitional loop or imported uniqueness claim. Experimental results are presented as empirical outcomes, not forced by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

Abstract-only review limits visibility into any fitted parameters or additional assumptions; the method rests on the domain assumption that hidden-state similarity captures removable redundancy.

axioms (1)
  • domain assumption LLMs contain representational redundancy across network depth that can be measured via hidden-state similarity
    Foundational premise for both RLS and the pruning allocation step.
invented entities (2)
  • Representation Locality Score (RLS) no independent evidence
    purpose: Quantify whether inter-layer redundancy is localized or globally distributed
    New metric introduced to guide clustering and pruning decisions.
  • LoRP framework no independent evidence
    purpose: Training-free one-shot depth pruning guided by locality
    The overall proposed algorithm and its clustering-plus-allocation procedure.

pith-pipeline@v0.9.1-grok · 5699 in / 1379 out tokens · 39025 ms · 2026-06-29T13:50:46.414241+00:00 · methodology

discussion (0)

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