Pith. sign in

REVIEW 2 major objections 52 references

Query-based Cross-Modal Projector Bolstering Mamba Multimodal LLM

T0 review · 2 major / 0 minor · reviewed 2026-06-28 · grok-4.3

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A query-based cross-modal projector compresses visual tokens via cross-attention to improve Mamba multimodal LLMs without manual 2D scan design.

desk verdict The paper introduces a query-based projector to adapt Mamba for vision-language work without manual scan ordering, but the experimental claims rest on thin evidence. read the letter →

arxiv 2606.04719 v1 pith:FD6OCEEY submitted 2026-06-03 cs.CL

SooHwan Eom , Jay Shim , Gwanhyeong Koo , Haebin Na , Mark A. Hasegawa-Johnson , Sungwoong Kim , Chang D. Yoo This is my paper
classification cs.CL
keywords MambamultimodalLLMcross-modalprojectorcross-attentionvision-languagestate-spacemodeltokencompression
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper proposes replacing standard visual token handling in Mamba-based vision-language models with a query-based cross-modal projector. This projector uses cross-attention to compress image features into a shorter sequence that feeds directly into the Mamba LLM. By doing so it avoids the need to hand-craft a 2D-to-1D scan order for the image tokens. Experiments on standard vision-language benchmarks report gains in both accuracy and inference speed compared with prior Mamba multimodal setups. The approach therefore aims to make state-space-model LLMs more practical for multimodal tasks by reducing both compute and design overhead.

What carries the argument

The query-based cross-modal projector, which applies cross-attention between learnable queries and visual features to produce a compressed token sequence for the Mamba backbone.

What would settle it

A controlled experiment showing that replacing the projector with a fixed linear projection or random token selection produces equal or higher benchmark scores would falsify the claim that the cross-attention design is responsible for the reported gains.

Watch

Extended reading notes

Core claim

The central claim is that a query-based cross-modal projector built on cross-attention can compress visual tokens for input to a Mamba LLM, simultaneously raising task performance and throughput while eliminating the requirement to manually specify a 2D scan order for raw image features.

Load-bearing premise

Cross-attention compression keeps enough visual detail for the Mamba model to solve vision-language tasks correctly.

Editorial extensions

If this is right

  • Mamba multimodal models can be trained and run at higher throughput on the same hardware.
  • Vision-language pipelines no longer require per-dataset tuning of image scan orders.
  • The same projector architecture can be inserted into other state-space-model backbones without changing their internal recurrence.
  • Training data efficiency may improve because fewer visual tokens reach the LLM per image.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The design may generalize to other modalities such as audio or video if the cross-attention queries are adapted to the new feature type.
  • If the compression ratio can be made dynamic per image, inference cost could be further reduced on easy examples.
  • The removal of hand-designed scan orders suggests that future Mamba vision models could learn ordering implicitly from data rather than from architecture choices.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 0 minor

Summary. The paper proposes a query-based cross-modal projector for Mamba-based multimodal LLMs. The projector uses cross-attention to compress visual tokens conditioned on input queries, eliminating the need to manually design 2D scan orders when converting image features to sequences for the Mamba LLM. The central claim is that this projector improves both performance and throughput on vision-language understanding benchmarks compared to prior Mamba multimodal approaches.

Significance. If the experimental gains are reproducible and the information-retention assumption holds, the work could provide a practical route to scale multimodal LLMs beyond Transformer quadratic costs while preserving cross-modal fidelity. The removal of manual scan-order engineering is a concrete engineering contribution that would be widely useful if shown to be robust across tasks.

major comments (2)
  1. [Abstract] Abstract: the claim that the projector 'enhances Mamba-based multimodal LLMs, boosting both performance and throughput' is presented without any description of experimental setup, baselines, datasets, statistical significance, or ablation controls. This absence makes the central empirical claim impossible to evaluate and is load-bearing for the paper's contribution.
  2. [Abstract] The weakest assumption—that cross-attention compression retains sufficient visual information—is not accompanied by any analysis (e.g., reconstruction error, attention visualization, or controlled ablation on token count) that would demonstrate the assumption holds for downstream VQA or captioning accuracy. Without such evidence the performance claim cannot be separated from possible information loss.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful comments on the abstract and the supporting evidence for our claims. We address each major comment point-by-point below and indicate planned revisions where appropriate.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that the projector 'enhances Mamba-based multimodal LLMs, boosting both performance and throughput' is presented without any description of experimental setup, baselines, datasets, statistical significance, or ablation controls. This absence makes the central empirical claim impossible to evaluate and is load-bearing for the paper's contribution.

    Authors: We agree that the abstract is concise and does not enumerate experimental details. The full manuscript contains dedicated sections describing the experimental setup, baselines (prior Mamba multimodal LLMs), datasets (vision-language understanding benchmarks), and ablations. To improve evaluability from the abstract alone, we will revise it to briefly reference the evaluation protocol, key baselines, and the nature of the reported gains. revision: yes

  2. Referee: [Abstract] The weakest assumption—that cross-attention compression retains sufficient visual information—is not accompanied by any analysis (e.g., reconstruction error, attention visualization, or controlled ablation on token count) that would demonstrate the assumption holds for downstream VQA or captioning accuracy. Without such evidence the performance claim cannot be separated from possible information loss.

    Authors: The manuscript reports consistent performance improvements over baselines, which provides indirect support that the compression preserves task-relevant information. We acknowledge that direct analyses such as reconstruction error or token-count ablations would strengthen the claim. We will add a controlled ablation on the number of compressed tokens together with attention-map visualizations in the revised manuscript. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity

full rationale

The paper introduces a query-based cross-modal projector for Mamba-based multimodal LLMs, describing its design for visual token compression via cross-attention and removal of manual 2D scan ordering, with performance claims resting solely on experimental benchmark results. No equations, derivations, fitted parameters presented as predictions, or load-bearing self-citations appear in the provided text. The method is presented as an empirical engineering contribution rather than a closed mathematical chain, making the central claims externally falsifiable via replication on the stated benchmarks.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

With only the abstract available, no specific free parameters, axioms, or invented entities can be identified from the text.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Query-based Cross-Modal Projector Bolstering Mamba Multimodal LLM." pith.science (2026). https://pith.science/paper/FD6OCEEY

@misc{pith2026260604719,
  author       = {Pith},
  title        = {Pith review of: Query-based Cross-Modal Projector Bolstering Mamba Multimodal LLM},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FD6OCEEY}},
  note         = {Machine review of arXiv:2606.04719}
}
read the original abstract

The Transformer's quadratic complexity with input length imposes an unsustainable computational load on large language models (LLMs). In contrast, the Selective Scan Structured State-Space Model, or Mamba, addresses this computational challenge effectively. This paper explores a query-based cross-modal projector designed to bolster Mamba's efficiency for vision-language modeling by compressing visual tokens based on input through the cross-attention mechanism. This innovative projector also removes the need for manually designing the 2D scan order of original image features when converting them into an input sequence for Mamba LLM. Experimental results across various vision-language understanding benchmarks show that the proposed cross-modal projector enhances Mamba-based multimodal LLMs, boosting both performance and throughput.

Figures

Figures reproduced from arXiv: 2606.04719 by the authors.

Figure 1
Figure 1. Model comparison between (a) LLaVA (Liu et al., 2023a), (b) BLIP-2 (Li et al., 2023a), (c) Cobra (Zhao et al., 2024), (d) VL-Mamba (Qiao et al., 2024), and (e) ours. The key differences stem from the choice of LLM backbone architecture, the design of the projector architecture, and the incorporation of learnable queries for flexibility. vision information from image features into 1D causal tokens by interleaving the… view at source ↗
Figure 2
Figure 2. Overall architecture of Querying Mamba (left) and the Multimodal Mamba LLM (right) based on the [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Example of local attention mask applied in [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

52 extracted references · 6 canonical work pages

  1. [1]

    2023 , eprint=

    Mamba: Linear-Time Sequence Modeling with Selective State Spaces , author=. 2023 , eprint=

    2023

  2. [2]

    Neural Information Processing Systems , year=

    Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers , author=. Neural Information Processing Systems , year=

  3. [3]

    HiPPO: Recurrent Memory with Optimal Polynomial Projections , url =

    Gu, Albert and Dao, Tri and Ermon, Stefano and Rudra, Atri and R\'. HiPPO: Recurrent Memory with Optimal Polynomial Projections , url =. Advances in Neural Information Processing Systems , editor =

  4. [4]

    International Conference on Learning Representations , year=

    Efficiently Modeling Long Sequences with Structured State Spaces , author=. International Conference on Learning Representations , year=

  5. [5]

    ArXiv , year=

    On the Parameterization and Initialization of Diagonal State Space Models , author=. ArXiv , year=

  6. [6]

    Advances in Neural Information Processing Systems , editor=

    Diagonal State Spaces are as Effective as Structured State Spaces , author=. Advances in Neural Information Processing Systems , editor=. 2022 , url=

    2022

  7. [7]

    The Eleventh International Conference on Learning Representations , year=

    Hungry Hungry Hippos: Towards Language Modeling with State Space Models , author=. The Eleventh International Conference on Learning Representations , year=

  8. [8]

    The Eleventh International Conference on Learning Representations , year=

    Simplified State Space Layers for Sequence Modeling , author=. The Eleventh International Conference on Learning Representations , year=

Show all 52 references
  1. [9]

    2024 , eprint=

    Cobra: Extending Mamba to Multi-Modal Large Language Model for Efficient Inference , author=. 2024 , eprint=

    2024

  2. [10]

    2024 , eprint=

    VL-Mamba: Exploring State Space Models for Multimodal Learning , author=. 2024 , eprint=

    2024

  3. [11]

    Neural Information Processing Systems , year=

    Attention is All you Need , author=. Neural Information Processing Systems , year=

  4. [12]

    Effective Approaches to Attention-based Neural Machine Translation

    Luong, Thang and Pham, Hieu and Manning, Christopher D. Effective Approaches to Attention-based Neural Machine Translation. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 2015. doi:10.18653/v1/D15-1166

    2015 doi

  5. [13]

    Neural Machine Translation by Jointly Learning to Align and Translate , booktitle =

    Dzmitry Bahdanau and Kyunghyun Cho and Yoshua Bengio , editor =. Neural Machine Translation by Jointly Learning to Align and Translate , booktitle =. 2015 , url =

    2015

  6. [14]

    and Ermon, Stefano and Rudra, Atri and R

    Dao, Tri and Fu, Daniel Y. and Ermon, Stefano and Rudra, Atri and R. Flash. Advances in Neural Information Processing Systems , year=

  7. [15]

    Dao, Tri , year=. Flash

  8. [16]

    Long Short-term Memory , volume =

    Hochreiter, Sepp and Schmidhuber, Jürgen , year =. Long Short-term Memory , volume =. Neural computation , doi =

  9. [17]

    2022 , eprint=

    Training language models to follow instructions with human feedback , author=. 2022 , eprint=

    2022

  10. [18]

    2023 , eprint=

    LLaMA: Open and Efficient Foundation Language Models , author=. 2023 , eprint=

    2023

  11. [19]

    2024 , url=

    Textbooks Are All You Need , author=. 2024 , url=

    2024

  12. [20]

    2023 , eprint=

    Textbooks Are All You Need II: phi-1.5 technical report , author=. 2023 , eprint=

    2023

  13. [21]

    2022 , eprint=

    OPT: Open Pre-trained Transformer Language Models , author=. 2022 , eprint=

    2022

  14. [22]

    Hashimoto , title =

    Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto , title =. GitHub repository , howpublished =. 2023 , publisher =

    2023

  15. [23]

    Proceedings of the 40th International Conference on Machine Learning , articleno =

    Li, Junnan and Li, Dongxu and Savarese, Silvio and Hoi, Steven , title =. Proceedings of the 40th International Conference on Machine Learning , articleno =. 2023 , publisher =

    2023

  16. [24]

    International Conference on Machine Learning , year=

    BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation , author=. International Conference on Machine Learning , year=

  17. [25]

    NeurIPS , year =

    Liu, Haotian and Li, Chunyuan and Wu, Qingyang and Lee, Yong Jae , title =. NeurIPS , year =

  18. [26]

    2024 , eprint=

    GPT-4 Technical Report , author=. 2024 , eprint=

    2024

  19. [27]

    2024 , url=

    Renrui Zhang and Jiaming Han and Chris Liu and Aojun Zhou and Pan Lu and Yu Qiao and Hongsheng Li and Peng Gao , booktitle=. 2024 , url=

    2024

  20. [28]

    2024 , eprint=

    TinyLLaVA: A Framework of Small-scale Large Multimodal Models , author=. 2024 , eprint=

    2024

  21. [29]

    2024 , eprint=

    MobileVLM V2: Faster and Stronger Baseline for Vision Language Model , author=. 2024 , eprint=

    2024

  22. [30]

    2024 , eprint=

    Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model , author=. 2024 , eprint=

    2024

  23. [31]

    arXiv preprint arXiv:2401.10166 , year=

    VMamba: Visual State Space Model , author=. arXiv preprint arXiv:2401.10166 , year=

    arXiv

  24. [32]

    2024 , eprint=

    SiMBA: Simplified Mamba-Based Architecture for Vision and Multivariate Time series , author=. 2024 , eprint=

    2024

  25. [33]

    arXiv preprint arXiv:2403.18257 , year=

    Dual-path Mamba: Short and Long-term Bidirectional Selective Structured State Space Models for Speech Separation , author=. arXiv preprint arXiv:2403.18257 , year=

  26. [34]

    arXiv preprint arXiv:2404.02063 , year=

    SPMamba: State-space model is all you need in speech separation , author=. arXiv preprint arXiv:2404.02063 , year=

  27. [35]

    2024 , eprint=

    VideoMamba: State Space Model for Efficient Video Understanding , author=. 2024 , eprint=

    2024

  28. [36]

    Goyal and T

    Y. Goyal and T. Khot and D. Summers-Stay and D. Batra and D. Parikh , booktitle =. Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering , year =. doi:10.1109/CVPR.2017.670 , url =

    2017 doi

  29. [37]

    2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , year=

    GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering , author=. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , year=

    2019

  30. [38]

    2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , year=

    VizWiz Grand Challenge: Answering Visual Questions from Blind People , author=. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , year=

    2018

  31. [39]

    2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , year=

    Towards VQA Models That Can Read , author=. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , year=

    2019

  32. [40]

    The 2023 Conference on Empirical Methods in Natural Language Processing , year=

    Evaluating Object Hallucination in Large Vision-Language Models , author=. The 2023 Conference on Empirical Methods in Natural Language Processing , year=

    2023

  33. [41]

    MMBench: Is Your Multi-modal Model an All-around Player? , year =

    Liu, Yuan and Duan, Haodong and Zhang, Yuanhan and Li, Bo and Zhang, Songyang and Zhao, Wangbo and Yuan, Yike and Wang, Jiaqi and He, Conghui and Liu, Ziwei and Chen, Kai and Lin, Dahua , journal =. MMBench: Is Your Multi-modal Model an All-around Player? , year =

  34. [42]

    Transactions on Machine Learning Research , issn=

    Maxime Oquab and Timoth. Transactions on Machine Learning Research , issn=. 2024 , url=

    2024

  35. [43]

    Zhai and B

    X. Zhai and B. Mustafa and A. Kolesnikov and L. Beyer , booktitle =. Sigmoid Loss for Language Image Pre-Training , year =. doi:10.1109/ICCV51070.2023.01100 , url =

    2023 doi

  36. [44]

    Soboleva, Daria and Al-Khateeb, Faisal and Myers, Robert and Steeves, Jacob R and Hestness, Joel and Dey, Nolan , title =

  37. [45]

    2023 , eprint=

    UltraFeedback: Boosting Language Models with High-quality Feedback , author=. 2023 , eprint=

    2023

  38. [46]

    2023 , eprint=

    Enhancing Chat Language Models by Scaling High-quality Instructional Conversations , author=. 2023 , eprint=

    2023

  39. [47]

    2023 , eprint=

    Zephyr: Direct Distillation of LM Alignment , author=. 2023 , eprint=

    2023

  40. [48]

    Thirty-seventh Conference on Neural Information Processing Systems , year=

    Direct Preference Optimization: Your Language Model is Secretly a Reward Model , author=. Thirty-seventh Conference on Neural Information Processing Systems , year=

  41. [49]

    ArXiv , year=

    To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning , author=. ArXiv , year=

  42. [50]

    The Twelfth International Conference on Learning Representations , year=

    Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning , author=. The Twelfth International Conference on Learning Representations , year=

  43. [51]

    International Conference on Learning Representations , year=

    On the Variance of the Adaptive Learning Rate and Beyond , author=. International Conference on Learning Representations , year=

  44. [52]

    2023 , eprint=

    PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel , author=. 2023 , eprint=

    2023

Pith tools

Reviewed June 28, 2026 · model on record in the stance chip above.