REVIEW 3 major objections 6 minor 78 references
Mamba-Adaptor: State Space Model Adaptor for Visual Recognition
T0 review · 3 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash
Pith's one-line read Memory and convolution add-ons help vision Mamba top ImageNet and COCO
desk verdict The core idea—learned memory selection on Mamba hidden states plus multi-scale depthwise conv on the output—is worth a look, but Algorithm 1 can't work as written and the tables have enough errors that the reported gains are unverifiable. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The two modules: Adaptor-T replaces the Mamba solver's default hidden-state update with a learned memory-selection step, where a linear layer $\phi_p$ predicts $K$ coordinates $p_k$ per hidden state and a softmax-weighted sum aggregates those earlier states; Adaptor-S applies multi-scale dilated depthwise convolutions to the reshaped 2D output. The implementation decouples the optimized Mamba solver by setting $C$ to an identity matrix and $D$ to zero, so the solver outputs hidden states, letting Adaptor-T operate on them and Adaptor-S operate on the matrix-multiplication output.
What would settle it
Train Mamba-Adaptor-b1 with Adaptor-T's selected coordinates replaced by fixed, randomly chosen coordinates while keeping everything else identical; if top-1 accuracy on ImageNet stays above roughly 78%, the learned selection is not load-bearing, and the reported gain over the static-selection ablation would collapse.
Extended reading notes
Core claim
The central claim is that vision Mamba can match or beat transformer backbones if the SSM solver is augmented at two points: during hidden-state recurrence, a lightweight linear predictor (Adaptor-T) identifies and aggregates the most-forgotten earlier states; at the output, multi-scale dilated depthwise convolutions (Adaptor-S) inject 2D locality. The paper reports that Mamba-Adaptor-b2 reaches 83.0% top-1 accuracy on ImageNet-1K, exceeding VMamba-T by 0.4 points while using fewer FLOPs, and that as a transfer adapter it recovers most of full fine-tuning's accuracy on CIFAR-100 and SVHN with under 10% of the parameters.
Load-bearing premise
The whole gain rests on Adaptor-T's linear layer being able to learn which earlier hidden states to blend, yet the paper never specifies how the predicted coordinates $p_k$ are made discrete or how gradients pass through that selection; if that step is not differentiable, the memory-retention mechanism cannot be trained as described.
Editorial extensions
If this is right
- Mamba backbones with the adaptor match or exceed Swin-T and VMamba-T on ImageNet at comparable FLOPs (83.0 vs 82.6 top-1).
- On COCO Mask R-CNN, Mamba-Adaptor-b2 improves over VMamba-T by about 0.8 points in APb and 1.3 points in APm under the 1x schedule.
- The adaptor can be inserted into a pretrained VMamba and retrained for only 10 additional epochs, raising top-1 accuracy by 0.1-0.2 points with small parameter growth.
- As a transfer-learning adapter, it outperforms linear probing and visual prompt tuning, coming within 0.7-1.5 points of full fine-tuning on CIFAR-100 while using 5.6-9.3% of the parameters.
Reading between the lines
- Adaptor-T's learned index selection is effectively a sparse attention over past states; the same mechanism could transfer to other recurrent layers, not just SSMs.
- If the index prediction is made differentiable explicitly (e.g., via Gumbel-softmax or straight-through estimation), the memory-retention gain might exceed the 0.1-0.4 point ImageNet improvement reported here.
- The zero-initialization and weight-sharing choices suggest the adaptor's benefit concentrates early in fine-tuning; testing with fewer than 10 epochs could reveal how quickly the gains accumulate.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript proposes Mamba-Adaptor, a plug-and-play extension for vision Mamba backbones. It consists of Adaptor-T, a learnable memory-retention module that selects and aggregates a small set of previous hidden states to mitigate long-range forgetting, and Adaptor-S, a multi-scale dilated depthwise-convolution module applied to the 2D-reshaped output to inject spatial inductive bias. The paper reports three usage modes: a from-scratch backbone for ImageNet-1K classification and COCO detection/segmentation with Mask R-CNN, a booster that continues training a pretrained VMamba for 10 additional epochs, and a parameter-efficient transfer-learning adaptor on CIFAR-100, SVHN, and Food-101. The stated contributions are efficiency and performance gains over Mamba baselines.
Significance. The conceptual direction is timely and potentially useful: lightweight adaptors targeted at known SSM weaknesses (temporal forgetting and missing 2D locality) are a natural complement to scanning-strategy work, and the three usage settings give the evaluation practical breadth. The ablations in Tables 5 and 6 provide some evidence that each module contributes. However, the central Adaptor-T mechanism is not implementable from the paper as written: the described identity-matrix trick cannot return per-token hidden states from the stock Mamba selective-scan kernel, and the discrete memory-coordinate selection is left underspecified. The booster experiment also lacks a continuation-training control. These issues bear directly on the headline claims, so the paper cannot be accepted without major revision and, ideally, a release of code and checkpoints.
major comments (3)
- [§3.5, Algorithm 1] The claimed identity-matrix decoupling cannot retrieve per-token hidden states from the standard Mamba selective-scan solver. With the usual input-dependent C of shape (B, L, N), setting C_identity = torch.ones_like(C) makes the kernel compute a scalar sum over the N state components rather than the N-vector h_t, and the returned tensor has the scan-output shape, not the recurrent-state shape. If C were instead a matrix, ones_like(C) would not be an identity matrix. Equations (5)-(9) require the full vector h_i for every token, so Adaptor-T is not implementable as written with the stated solver unless D = N, which does not hold for the reported b1/b2 backbones. No custom kernel, code, or checkpoints are provided to close this gap. Because Adaptor-T is the paper's core memory-retention mechanism, the headline ImageNet/COCO results cannot be verified from the manuscript.
- [§3.3, Eq. (8)] The learnable memory-selection scheme predicts continuous coordinates p_k and coefficients c_k, but the paper never states how p_k is converted to discrete hidden-state indices h(p_k) or how gradients flow through this discrete selection. Algorithm 1 omits this step entirely. Without this specification, the claimed dynamic memory retention is underspecified and cannot be reproduced by a reader.
- [§4.2, Table 3] The booster experiment trains the adapted VMamba models for an additional 10 epochs, but it reports no control in which VMamba-T/S/B is trained for the same 10 additional epochs without the adaptor. Since the reported gains are only 0.1-0.2% top-1 accuracy, they are within the range one would expect from additional training alone, so the booster claim is not supported without this control.
minor comments (6)
- [Table 2] The value -41.2 for APm of Mamba-Adaptor-b1 under the 3× schedule is impossible; please correct the entry and recheck all numbers in this table.
- [Table 1] The rows for CoAtNet-T, UniRepLKNet-T, and ConvNeXt-T report identical parameters, FLOPs, and accuracy (29/4.5/82.1); these are likely copy errors and should be replaced with the actual reported numbers.
- [§4.1] The text says Mamba-Adaptor-b2 reaches 82.9 top-1 and exceeds VMamba-T by 0.2% and Swin-T by 2.6%, but Table 1 shows 83.0, with differences of 0.4 and 1.7, respectively; please align the text and table.
- [Abstract and Section 1] The text repeatedly uses 'casual' where 'causal' is intended, and there are several other typos ('Adapator', 'Apdator', 'Mamble', 'paramters', 'matric'); a careful proofread is needed.
- [Abstract] The claim of state-of-the-art performance is stronger than Table 1 supports (for example, InternImage-T reaches 83.5 and Mamba-Adaptor-b2 reports 83.0); please qualify the claim to the compared Mamba baselines or provide a more complete comparison.
- [§5] The paper states that scaling is open and that application to other vision Mamba variants is future work; given the scope of the three claimed usages, stating these limitations clearly is helpful, but Table 7 should be referenced in the main text where the generalization experiments are described.
Circularity Check
No circularity identified; empirical gains are not reduced to the method's inputs by construction, though Algorithm 1 raises implementability concerns.
full rationale
The paper's Adaptor-T and Adaptor-S are introduced as learnable modules through Equations 5-10 and Algorithm 1, and the headline ImageNet/COCO results are reported as empirical comparisons against external baselines. No prediction is derived from a premise that already contains the reported accuracy, and no fitted parameter is renamed as a prediction. The identity-matrix trick in Algorithm 1 is a potential correctness/implementability defect: setting C_identiy = torch.ones_like(C) does not yield an identity map over the state dimension, and the selective-scan call with this C returns an output-projected scalar rather than the hidden-state vector h_t required by Adaptor_T. However, this is a reproducibility concern, not circularity, because it does not make the claimed results equal to the inputs by construction. Likewise, the booster experiment in Section 4.2 lacks a control that trains the baseline for the same additional 10 epochs without the adaptor, so the 0.1-0.2% gains may reflect extra training rather than the module; again, this is an experimental control deficiency, not a definitional reduction. Self-citations appear only in related-work and baselines and are not load-bearing for the central claim. Under the required standard that circularity must be exhibited as a specific equation-to-equation or fit-to-prediction reduction, no circular step is found.
Assumptions & free parameters
free parameters (4)
- K (number of selected memory states in Adaptor-T) =
not reported
- Adaptor-S dilation rates and number of scales =
not reported (ablation uses 1 vs 2 scales)
- Adaptor-S kernel size =
not reported (described as generally K×K)
- Weight-sharing coefficient strategy in Adaptor-T =
not reported
assumptions (4)
- standard math The standard Mamba selective scan formulation (Eq. 1-4) is correct and used as the base solver.
- standard math Zero-Order Hold discretization (Eq. 2) is a valid conversion from continuous to discrete SSM parameters.
- domain assumption The three identified constraints (causal access, long-range forgetting, weak spatial modeling) are the main performance bottlenecks for vision Mamba.
- domain assumption The experimental settings replicate those of VMamba [32] and Swin [33] as stated.
Cite this review
Pith. "Pith review of Mamba-Adaptor: State Space Model Adaptor for Visual Recognition." pith.science (2026). https://pith.science/paper/5CWAMRME
@misc{pith2026250512685,
author = {Pith},
title = {Pith review of: Mamba-Adaptor: State Space Model Adaptor for Visual Recognition},
year = {2026},
howpublished = {\url{https://pith.science/paper/5CWAMRME}},
note = {Machine review of arXiv:2505.12685}
}
read the original abstract
Recent State Space Models (SSM), especially Mamba, have demonstrated impressive performance in visual modeling and possess superior model efficiency. However, the application of Mamba to visual tasks suffers inferior performance due to three main constraints existing in the sequential model: 1) Casual computing is incapable of accessing global context; 2) Long-range forgetting when computing the current hidden states; 3) Weak spatial structural modeling due to the transformed sequential input. To address these issues, we investigate a simple yet powerful vision task Adaptor for Mamba models, which consists of two functional modules: Adaptor-T and Adaptor-S. When solving the hidden states for SSM, we apply a lightweight prediction module Adaptor-T to select a set of learnable locations as memory augmentations to ease long-range forgetting issues. Moreover, we leverage Adapator-S, composed of multi-scale dilated convolutional kernels, to enhance the spatial modeling and introduce the image inductive bias into the feature output. Both modules can enlarge the context modeling in casual computing, as the output is enhanced by the inaccessible features. We explore three usages of Mamba-Adaptor: A general visual backbone for various vision tasks; A booster module to raise the performance of pretrained backbones; A highly efficient fine-tuning module that adapts the base model for transfer learning tasks. Extensive experiments verify the effectiveness of Mamba-Adaptor in three settings. Notably, our Mamba-Adaptor achieves state-of the-art performance on the ImageNet and COCO benchmarks.
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Reviewed August 15, 2026 · model on record in the stance chip above.
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