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arxiv: 2606.01711 · v2 · pith:5SOIS25Rnew · submitted 2026-06-01 · 💻 cs.CV

Improving Visual Token Reduction via Rectifying Distortions for Efficient Multimodal LLM Inference

Hyeonwoo Cho , Donghyeon Baek , Yewon Kim , Bumsub Ham This is my paper

Pith reviewed 2026-06-28 15:51 UTC · model grok-4.3

classification 💻 cs.CV
keywords visual token reductionmultimodal large language modelsattention calibrationtoken mergingefficient inferencepositional distortionattentional consistency
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The pith

RESTORE rectifies positional and attentional distortions in visual token reduction to raise MLLM accuracy.

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

The paper seeks to establish that existing visual token reduction methods in multimodal LLMs distort the positional and attentional consistency between full and reduced sequences, causing information loss. It introduces the RESTORE framework with a calibration step that augments attention weights using relative distances and a distinctive anchor selection step for token merging. This combination is shown to improve the accuracy of multiple reduction techniques on vision-language benchmarks while preserving computational efficiency. A sympathetic reader would care because MLLMs face memory and latency limits from quadratic attention over many visual tokens, and a fix that works across methods could make these models more practical. If the claim holds, reduced token counts become viable without the usual accuracy penalty.

Core claim

RESTORE is a visual token reduction framework that rectifies positional and attentional distortions by a calibration method that restores lost visual attention through augmentation of attention weights based on relative distances, together with a distinctive anchor selection for token merging that mitigates information loss during feature averaging.

What carries the argument

RESTORE calibration method using relative distances to augment attention weights, combined with anchor selection for token merging to preserve consistency between full and reduced sequences.

If this is right

  • Accuracy of multiple existing visual token reduction methods increases when RESTORE is applied.
  • State-of-the-art results appear on multiple vision-language benchmarks.
  • Computational efficiency remains intact with lower memory and latency than full-token baselines.
  • The improvements hold across different reduction strategies without task-specific tuning.

Where Pith is reading between the lines

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

  • The same distortion-rectification steps could be tested on text-only token pruning in standard LLMs.
  • Integration with quantization or pruning pipelines might further extend usable context lengths.
  • Deployment on mobile or edge hardware becomes more feasible if the accuracy recovery scales.

Load-bearing premise

The calibration based on relative distances and anchor selection will restore lost visual attention and mitigate information loss without introducing new distortions or needing per-task adjustments.

What would settle it

Apply RESTORE to an existing reduction method on a standard benchmark and observe no accuracy gain or added latency compared with the same reduction method without RESTORE.

Figures

Figures reproduced from arXiv: 2606.01711 by Bumsub Ham, DongHyeon Baek, Hyeonwoo Cho, Yewon Kim.

Figure 1
Figure 1. Figure 1: Illustration of the impact of visual token reduction on the internal attention mechanism of the LLM within MLLMs (e.g., LLaVA (Liu et al., 2024a)). (a) The full token sequence. (b) Reindexing position indices assigns contiguous indices to the reduced sequence. (c) Retaining position indices preserves the original indices of the retained tokens from (a). (d) We rectify distortions by retaining original posi… view at source ↗
Figure 2
Figure 2. Figure 2: (b) visual-to-visual and text-to-visual scenarios, re￾spectively. These figures demonstrate that neither position assignment preserves the total attention weights of visual tokens at the baseline level. The reindexing strategy (dashed red line) exhibits a decline in attention weights, while retain￾ing the original position indices (dashed green line) leads to a further substantial drop. The attention atten… view at source ↗
Figure 3
Figure 3. Figure 3: Visualizations of the long-term decay D(|m − n|) (blue) and our calibration term (orange). The calibration term increases with relative distance to counteract the long-term decay. from two limitations: (1) Selecting anchor tokens with low correlation to non-anchor tokens results in poor represen￾tativeness. In this scenario, the anchor token fails to serve as a centroid for its cluster, exacerbating inform… view at source ↗
Figure 4
Figure 4. Figure 4: Accuracy-latency trade-off curves on (a) GQA (Hudson & Manning, 2019) and (b) MMBench (Liu et al., 2024b) by varying the number of retained visual tokens. calibration mechanism. We have also introduced a distinc￾tive anchor selection for token merging to mitigate infor￾mation loss during token merging. Extensive experiments demonstrate that integrating our framework with various baselines consistently yiel… view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of average attention proportions assigned to visual tokens within the LLM (a) when the query is a visual token and (b) when the query is a text token. We add a case of reindexing position indices with our proposed calibration [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗
read the original abstract

Recent advancements in Multimodal Large Language Models (MLLMs) have achieved remarkable success in vision-language tasks, yet the quadratic computational complexity arising from the vast number of visual tokens incurs significant memory and latency bottlenecks. While visual token reduction (VTR) strategies have been explored to mitigate this burden, existing methods overlook the positional and attentional consistency between the full and reduced sequences, resulting in a distorted representation. To this end, we propose RESTORE, a novel VTR framework that rectifies the positional and attentional distortions while maintaining efficiency. Specifically, we present a simple yet effective calibration method that restores lost visual attention by augmenting attention weights based on relative distances. We also introduce a distinctive anchor selection for token merging to mitigate information loss during feature averaging. Experimental results on multiple benchmarks demonstrate that our method consistently improves the accuracy of various reduction methods, achieving state-of-the-art performance while maintaining computational efficiency. Project page is available at https://cvlab.yonsei.ac.kr/projects/RESTORE

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

0 major / 3 minor

Summary. The manuscript introduces RESTORE, a visual token reduction (VTR) framework for Multimodal Large Language Models that rectifies positional and attentional distortions between full and reduced token sequences. It proposes a calibration step that augments attention weights using relative distances and an anchor-based selection strategy for token merging. The central empirical claim is that RESTORE consistently improves accuracy across multiple existing VTR baselines, reaches SOTA performance on standard vision-language benchmarks, and preserves computational efficiency.

Significance. If the reported gains hold under the provided ablations and cross-benchmark evaluation, the work offers a practical, additive improvement to existing VTR techniques without introducing new hyperparameters or substantial overhead. The empirical focus, with multiple reduction baselines and ablation studies, strengthens the contribution for efficient MLLM inference.

minor comments (3)
  1. [Abstract] Abstract: the claim of 'consistent' accuracy gains and SOTA results would be stronger if the abstract named the specific benchmarks and reported the magnitude of improvements (e.g., average accuracy delta).
  2. [Method] Method section: the anchor selection procedure and relative-distance augmentation would benefit from a short pseudocode listing or explicit algorithmic steps to aid reproducibility.
  3. [Experiments] Experiments: while ablations are supplied, adding error bars or reporting the number of runs would further substantiate the 'consistent' improvement claim across reduction methods.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive assessment of our manuscript and the recommendation for minor revision. We appreciate the recognition that RESTORE provides a practical, additive improvement to existing VTR techniques.

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper's contribution is an empirical calibration method (relative-distance attention augmentation and anchor-based merging) added to existing VTR techniques. No equations, derivations, or self-citations are presented that reduce any claimed result to fitted parameters or prior self-work by construction. The central claims rest on benchmark experiments with ablations, not on any internal reduction or uniqueness theorem imported from the authors' prior papers. This is the common case of a self-contained empirical addition.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no explicit free parameters, axioms, or invented entities; all details are high-level descriptions of the calibration and selection steps.

pith-pipeline@v0.9.1-grok · 5713 in / 951 out tokens · 21918 ms · 2026-06-28T15:51:16.502167+00:00 · methodology

discussion (0)

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Reference graph

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