pith. sign in

arxiv: 2606.31383 · v1 · pith:JJMXHVWZnew · submitted 2026-06-30 · 💻 cs.CV

MS-Resampler: Multi-Scope Visual Resampling for Efficient Multimodal LLMs

Zhongyang Li , Yaqian Li , Faming Fang , Rinyoichi Takezoe , Zi-Hao Bo , Cheng Qian , Mo Guang , Guixu Zhang
show 1 more author
Kaiwen Long
This is my paper

Pith reviewed 2026-07-01 05:39 UTC · model grok-4.3

classification 💻 cs.CV
keywords multimodal large language modelsvisual resamplingmulti-scope aggregationspatial scope priorsadaptive fusionvisual understandingmultimodal reasoningefficient vision projectors
0
0 comments X

The pith

MS-Resampler improves visual understanding in multimodal LLMs by resampling features across multiple spatial scopes and fusing the results adaptively.

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

The paper introduces MS-Resampler as a way to turn dense visual features into compact tokens for language models. Instead of one global cross-attention pass that can lose fine details or miss broad context, it runs several parallel resamplers, each given an explicit spatial scope prior so it focuses on a different granularity from local patches to the whole image. These branch outputs are then combined through adaptive fusion before being fed to the language model. The design keeps the total token count fixed yet produces representations that support stronger visual understanding and reasoning. Tests on ten standard multimodal benchmarks show consistent gains over single-scope baselines with only small added computation.

Core claim

MS-Resampler instantiates multiple scope-specific resamplers by injecting explicit spatial scope priors into the resampling attention, enabling each branch to aggregate visual information at a particular granularity from local to global. The outputs of these scope-specific resamplers are then adaptively fused to produce the final visual representations for language modeling.

What carries the argument

Multiple parallel scope-specific resampling attentions, each conditioned on a distinct spatial scope prior, whose outputs are combined by adaptive fusion.

If this is right

  • Better scores on visual understanding and multimodal reasoning benchmarks
  • Improved ability to keep both fine local evidence and overall scene context inside a fixed token budget
  • Only minimal extra computation compared with conventional single-scope resamplers
  • Consistent gains across multiple public multimodal evaluation sets

Where Pith is reading between the lines

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

  • The same multi-scope idea could be tested on video or audio inputs to see whether scope-specific branches help there as well
  • If the adaptive fusion learns to down-weight certain scopes on particular tasks, that pattern might guide future fixed-scope designs
  • Higher-resolution input images might become usable without raising the token count, because local-scope branches already focus on detail

Load-bearing premise

That giving separate branches explicit spatial scope priors and then fusing their outputs will extract complementary local and global information that a single fixed-scope global attention cannot capture under the same token limit.

What would settle it

A controlled experiment on the same ten benchmarks in which a single-scope resampler, given the same token budget and training regime, matches or exceeds MS-Resampler accuracy on every task with equal or lower compute.

Figures

Figures reproduced from arXiv: 2606.31383 by Cheng Qian, Faming Fang, Guixu Zhang, Kaiwen Long, Mo Guang, Rinyoichi Takezoe, Yaqian Li, Zhongyang Li, Zi-Hao Bo.

Figure 1
Figure 1. Figure 1: Comparison of visual projectors in MLLMs. (a) MLP maps patch tokens inde￾pendently, leading to redundant inputs. (b) Global resampler aggregates with a single global scope, which may dilute local evidence. (c) MS-Resampler uses multiple scoped resamplers and fuses their outputs to capture local-to-global semantics under a fixed token budget. Early MLLMs commonly adopt MLP-based projectors, as exemplified b… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of MS-Resampler. Dense visual tokens extracted by the vision encoder are processed by multiple scoped resampling branches, each operating with a different spatial aggregation scope. The outputs from these branches are fused to produce the final compact visual tokens for the language model. scales to improve representational diversity, while MaxViT [29] combines local and global interactions throug… view at source ↗
Figure 3
Figure 3. Figure 3: Illustration of spatial attention bias construction for scoped resampling. Each query corresponds to an anchor position on the visual token grid. For branch e, a window of size k (e) defines its preferred spatial scope. 3.4 Spatial Attention Bias Construction Each scoped resampling branch encodes its preferred aggregation scope through a spatial attention bias matrix B(e) ∈ RM×N . This matrix specifies, fo… view at source ↗
read the original abstract

Multimodal large language models (MLLMs) typically employ resampling-based projectors to transform dense visual features into a compact token sequence for language modeling. Most existing resamplers adopt a single, fixed aggregation scope via global cross-attention, which can blur fine-grained local evidence and limit the ability to capture both local details and global context within a fixed token budget. In this work, we propose MS-Resampler, a multi-scope visual resampling framework for MLLMs. MS-Resampler instantiates multiple scope-specific resamplers by injecting explicit spatial scope priors into the resampling attention, enabling each branch to aggregate visual information at a particular granularity from local to global. The outputs of these scope-specific resamplers are then adaptively fused to produce the final visual representations for language modeling. Extensive experiments on ten public multimodal benchmarks show that MS-Resampler consistently improves visual understanding and multimodal reasoning over conventional single-scope resamplers, while introducing only minimal computational overhead.

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

1 major / 0 minor

Summary. The manuscript proposes MS-Resampler, a multi-scope visual resampling framework for multimodal large language models (MLLMs). It replaces conventional single-scope global cross-attention resamplers with multiple parallel branches, each receiving an explicit spatial scope prior to aggregate visual features at different granularities (local to global). Branch outputs are adaptively fused to produce the final visual token sequence under a fixed token budget. Experiments across ten public multimodal benchmarks are reported to show consistent gains in visual understanding and multimodal reasoning with only minimal computational overhead.

Significance. If the empirical improvements are robust, the work offers a practical architectural refinement for efficient MLLMs by explicitly addressing the trade-off between local detail and global context within a constrained token budget. The multi-branch design with injected spatial priors and adaptive fusion is a coherent extension of existing resampling projectors, and the fixed-budget framing supports the minimal-overhead claim. The approach could be adopted in resource-constrained multimodal settings if the gains generalize beyond the reported benchmarks.

major comments (1)
  1. Abstract: the central claim that MS-Resampler 'consistently improves visual understanding and multimodal reasoning' rests entirely on unreported empirical results from ten benchmarks; no quantitative deltas, baseline comparisons, ablation tables, error bars, or statistical significance tests are supplied, so the load-bearing performance assertion cannot be evaluated for soundness or reproducibility.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for highlighting the need for quantitative support in the abstract. We address this point directly below and will revise accordingly.

read point-by-point responses

  1. Referee: Abstract: the central claim that MS-Resampler 'consistently improves visual understanding and multimodal reasoning' rests entirely on unreported empirical results from ten benchmarks; no quantitative deltas, baseline comparisons, ablation tables, error bars, or statistical significance tests are supplied, so the load-bearing performance assertion cannot be evaluated for soundness or reproducibility.

    Authors: We agree that the abstract would be stronger with explicit quantitative backing. In the revised manuscript we will add concise performance deltas (e.g., average improvement over single-scope baselines across the ten benchmarks) and a pointer to the detailed tables, ablations, and multi-run statistics already present in the Experiments section. This makes the central claim directly evaluable from the abstract while preserving its brevity. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper presents MS-Resampler as an architectural proposal: multiple parallel resampling branches each conditioned on a distinct explicit spatial scope prior, with adaptive fusion of outputs under fixed token budget. The central claim is an empirical performance gain on external benchmarks, not a first-principles derivation or prediction. No equations, fitted parameters, or self-citations are shown that reduce the claimed improvement to a re-expression of the inputs by construction. The design is presented as a novel construction whose validity rests on experimental results rather than internal definitional equivalence.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review; no specific free parameters, axioms, or invented entities are enumerated in the provided text. The method implicitly relies on standard attention mechanisms being modifiable by spatial priors, but this is not detailed.

axioms (1)
  • domain assumption Standard cross-attention can be modified by explicit spatial scope priors to produce scope-specific aggregations
    Invoked when the abstract states that scope-specific resamplers are instantiated by injecting spatial scope priors into the resampling attention.

pith-pipeline@v0.9.1-grok · 5725 in / 1288 out tokens · 26300 ms · 2026-07-01T05:39:06.059427+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

39 extracted references · 18 canonical work pages · 8 internal anchors

  1. [1]

    GPT-4 Technical Report

    Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al.: Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023) 1

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  2. [2]

    Advances in neural information processing systems35, 23716– 23736 (2022) 3

    Alayrac, J.B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Men- sch, A., Millican, K., Reynolds, M., et al.: Flamingo: a visual language model for few-shot learning. Advances in neural information processing systems35, 23716– 23736 (2022) 3

    2022

  3. [3]

    Alvar, S.R., Singh, G., Akbari, M., Zhang, Y.: Divprune: Diversity-based visual tokenpruningforlargemultimodalmodels.In:ProceedingsoftheComputerVision and Pattern Recognition Conference. pp. 9392–9401 (2025) 4

    2025

  4. [4]

    In: Proceedings of the AAAI Conference on Artificial In- telligence

    Arif, K.H.I., Yoon, J., Nikolopoulos, D.S., Vandierendonck, H., John, D., Ji, B.: Hired: Attention-guided token dropping for efficient inference of high-resolution vision-language models. In: Proceedings of the AAAI Conference on Artificial In- telligence. vol. 39, pp. 1773–1781 (2025) 10

    2025

  5. [5]

    Qwen Technical Report

    Bai, J., Bai, S., Chu, Y., Cui, Z., Dang, K., Deng, X., Fan, Y., Ge, W., Han, Y., Huang, F., et al.: Qwen technical report. arXiv preprint arXiv:2309.16609 (2023) 1

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  6. [6]

    Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

    Bai, J., Bai, S., Yang, S., Wang, S., Tan, S., Wang, P., Lin, J., Zhou, C., Zhou, J.: Qwen-vl: A frontier large vision-language model with versatile abilities. arXiv preprint arXiv:2308.129661(2), 3 (2023) 2

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  7. [7]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Cha, J., Kang, W., Mun, J., Roh, B.: Honeybee: Locality-enhanced projector for multimodal llm. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 13817–13827 (2024) 2, 4, 9, 10, 11

    2024

  8. [8]

    In: European Conference on Computer Vision

    Chen, L., Zhao, H., Liu, T., Bai, S., Lin, J., Zhou, C., Chang, B.: An image is worth 1/2 tokens after layer 2: Plug-and-play inference acceleration for large vision-language models. In: European Conference on Computer Vision. pp. 19–35. Springer (2024) 10

    2024

  9. [9]

    Chen, Z., Wang, W., Tian, H., Ye, S., Gao, Z., Cui, E., Tong, W., Hu, K., Luo, J., Ma, Z., et al.: How far are we to gpt-4v? closing the gap to commercial multimodal modelswith open-source suites.Science China InformationSciences67(12),220101 (2024) 9, 10, 11

    2024

  10. [10]

    See https://vicuna

    Chiang, W.L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J.E., et al.: Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality. See https://vicuna. lmsys. org (accessed 14 April 2023)2(3), 6 (2023) 9

    2023

  11. [11]

    MobileVLM V2: Faster and Stronger Baseline for Vision Language Model

    Chu, X., Qiao, L., Zhang, X., Xu, S., Wei, F., Yang, Y., Sun, X., Hu, Y., Lin, X., Zhang, B., et al.: Mobilevlm v2: Faster and stronger baseline for vision language model. arXiv preprint arXiv:2402.03766 (2024) 2, 4, 9, 10, 11

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  12. [12]

    Advances in Neural Information Pro- cessing Systems37, 50168–50188 (2024) 2, 9, 10

    Hu, W., Dou, Z.Y., Li, L., Kamath, A., Peng, N., Chang, K.W.: Matryoshka query transformer for large vision-language models. Advances in Neural Information Pro- cessing Systems37, 50168–50188 (2024) 2, 9, 10

    2024

  13. [13]

    Qwen2.5-Coder Technical Report

    Hui, B., Yang, J., Cui, Z., Yang, J., Liu, D., Zhang, L., Liu, T., Zhang, J., Yu, B., Lu, K., et al.: Qwen2. 5-coder technical report. arXiv preprint arXiv:2409.12186 (2024) 9, 11

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  14. [14]

    Microsoft Research Blog1(3), 3 (2023) 4 16 Authors Suppressed Due to Excessive Length

    Javaheripi, M., Bubeck, S., Abdin, M., Aneja, J., Bubeck, S., Mendes, C.C.T., Chen, W., Del Giorno, A., Eldan, R., Gopi, S., et al.: Phi-2: The surprising power of small language models. Microsoft Research Blog1(3), 3 (2023) 4 16 Authors Suppressed Due to Excessive Length

    2023

  15. [15]

    In: International conference on machine learning

    Jia, C., Yang, Y., Xia, Y., Chen, Y.T., Parekh, Z., Pham, H., Le, Q., Sung, Y.H., Li, Z., Duerig, T.: Scaling up visual and vision-language representation learning with noisy text supervision. In: International conference on machine learning. pp. 4904–4916. PMLR (2021) 3

    2021

  16. [16]

    In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

    Lee, Y., Kim, J., Willette, J., Hwang, S.J.: Mpvit: Multi-path vision transformer for dense prediction. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 7287–7296 (2022) 4

    2022

  17. [17]

    arXiv e-prints pp

    Li, H., Zhang, J., Liao, W., Peng, D., Ding, K., Jin, L.: Beyond token compression: A training-free reduction framework for efficient visual processing in mllms. arXiv e-prints pp. arXiv–2501 (2025) 4

    2025

  18. [18]

    In: International conference on machine learning

    Li, J., Li, D., Savarese, S., Hoi, S.: Blip-2: Bootstrapping language-image pre- training with frozen image encoders and large language models. In: International conference on machine learning. pp. 19730–19742. PMLR (2023) 2, 3

    2023

  19. [19]

    arXiv preprint arXiv:2407.02392 (2024) 2, 4, 9, 10, 11

    Li, W., Yuan, Y., Liu, J., Tang, D., Wang, S., Qin, J., Zhu, J., Zhang, L.: Tokenpacker: Efficient visual projector for multimodal llm. arXiv preprint arXiv:2407.02392 (2024) 2, 4, 9, 10, 11

    work page arXiv 2024

  20. [20]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Liu, H., Li, C., Li, Y., Lee, Y.J.: Improved baselines with visual instruction tuning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 26296–26306 (2024) 2, 3, 4, 9

    2024

  21. [21]

    Advances in neural information processing systems36, 34892–34916 (2023) 2, 3

    Liu, H., Li, C., Wu, Q., Lee, Y.J.: Visual instruction tuning. Advances in neural information processing systems36, 34892–34916 (2023) 2, 3

    2023

  22. [22]

    arXiv preprint arXiv:2411.10803 (2024) 10

    Liu, T., Shi, L., Hong, R., Hu, Y., Yin, Q., Zhang, L.: Multi-stage vision to- ken dropping: Towards efficient multimodal large language model. arXiv preprint arXiv:2411.10803 (2024) 10

    work page arXiv 2024

  23. [23]

    Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer:Hierarchicalvisiontransformerusingshiftedwindows.In:Proceedings of the IEEE/CVF international conference on computer vision. pp. 10012–10022 (2021) 4

    2021

  24. [24]

    Pattern Recognition153, 110470 (2024) 5

    Nie, X., Jin, H., Yan, Y., Chen, X., Zhu, Z., Qi, D.: Scopevit: Scale-aware vision transformer. Pattern Recognition153, 110470 (2024) 5

    2024

  25. [25]

    arXiv preprint arXiv:2410.10319 (2024) 4, 9, 10

    Qian, S., Liu, B., Sun, C., Xu, Z., Wang, B.: Spatial-aware efficient projector for mllms via multi-layer feature aggregation. arXiv preprint arXiv:2410.10319 (2024) 4, 9, 10

    work page arXiv 2024

  26. [26]

    In: International conference on machine learning

    Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PmLR (2021) 1, 3, 4, 9

    2021

  27. [27]

    arXiv preprint arXiv:2403.15388 (2024) 10

    Shang, Y., Cai, M., Xu, B., Lee, Y.J., Yan, Y.: Llava-prumerge: Adaptive token reduction for efficient large multimodal models. arXiv preprint arXiv:2403.15388 (2024) 10

    work page arXiv 2024

  28. [28]

    LLaMA: Open and Efficient Foundation Language Models

    Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) 4

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  29. [29]

    In: European conference on computer vision

    Tu, Z., Talebi, H., Zhang, H., Yang, F., Milanfar, P., Bovik, A., Li, Y.: Maxvit: Multi-axis vision transformer. In: European conference on computer vision. pp. 459–479. Springer (2022) 5

    2022

  30. [30]

    arXiv preprint arXiv:2502.11494 (2025) 10 MS-Resampler 17

    Wen, Z., Gao, Y., Wang, S., Zhang, J., Zhang, Q., Li, W., He, C., Zhang, L.: Stop looking for important tokens in multimodal language models: Duplication matters more. arXiv preprint arXiv:2502.11494 (2025) 10 MS-Resampler 17

    work page arXiv 2025

  31. [31]

    PyramidDrop: Accelerating Your Large Vision-Language Models via Pyramid Visual Redundancy Reduction

    Xing, L., Huang, Q., Dong, X., Lu, J., Zhang, P., Zang, Y., Cao, Y., He, C., Wang, J., Wu, F., et al.: Pyramiddrop: Accelerating your large vision-language models via pyramid visual redundancy reduction. arXiv preprint arXiv:2410.17247 (2024) 10

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  32. [32]

    arXiv preprint arXiv:2409.10197 (2024) 10

    Ye, W., Wu, Q., Lin, W., Zhou, Y.: Fit and prune: Fast and training-free visual token pruning for multi-modal large language models. arXiv preprint arXiv:2409.10197 (2024) 10

    work page arXiv 2024

  33. [33]

    arXiv preprint arXiv:2512.18910 (2025) 4

    Zamini, M., Shukla, D.: Delta-llava: Base-then-specialize alignment for token- efficient vision-language models. arXiv preprint arXiv:2512.18910 (2025) 4

    work page arXiv 2025

  34. [34]

    In: Proceedings of the IEEE/CVF international conference on computer vision

    Zhai, X., Mustafa, B., Kolesnikov, A., Beyer, L.: Sigmoid loss for language im- age pre-training. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 11975–11986 (2023) 1, 4

    2023

  35. [35]

    arXiv preprint arXiv:2412.01818 (2024) 9, 10

    Zhang, Q., Cheng, A., Lu, M., Zhuo, Z., Wang, M., Cao, J., Guo, S., She, Q., Zhang, S.: [cls] attention is all you need for training-free visual token pruning: Make vlm inference faster. arXiv preprint arXiv:2412.01818 (2024) 9, 10

    work page arXiv 2024

  36. [36]

    arXiv preprint arXiv:2501.03895 (2025) 4

    Zhang, S., Fang, Q., Yang, Z., Feng, Y.: Llava-mini: Efficient image and video large multimodal models with one vision token. arXiv preprint arXiv:2501.03895 (2025) 4

    work page arXiv 2025

  37. [37]

    SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference

    Zhang, Y., Fan, C.K., Ma, J., Zheng, W., Huang, T., Cheng, K., Gudovskiy, D., Okuno, T., Nakata, Y., Keutzer, K., et al.: Sparsevlm: Visual token sparsifica- tion for efficient vision-language model inference. arXiv preprint arXiv:2410.04417 (2024) 10

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  38. [38]

    In: Proceedings of the Computer Vision and Pattern Recognition Conference

    Zhao, S., Wang, Z., Juefei-Xu, F., Xia, X., Liu, M., Wang, X., Liang, M., Zhang, N., Metaxas, D.N., Yu, L.: Accelerating multimodal large language models by search- ing optimal vision token reduction. In: Proceedings of the Computer Vision and Pattern Recognition Conference. pp. 29869–29879 (2025) 4

    2025

  39. [39]

    arXiv preprint arXiv:2510.16598 (2025) 4

    Zhu, J., Zhu, Y., Lu, X., Yan, W., Li, D., Liu, K., Fu, X., Zha, Z.J.: Visionselector: End-to-end learnable visual token compression for efficient multimodal llms. arXiv preprint arXiv:2510.16598 (2025) 4

    work page arXiv 2025