LBMamba: Locally Bi-directional Mamba
Authors: Jingwei Zhang
Xi Han
Hong Qin
Mahdi S. Hosseini
Dimitris Samaras
Venue: Accepted by TMLR ()

Abstract: Mamba, a State Space Model (SSM) that accelerates training by recasting recurrence as a parallel selective scan, has recently emerged as a linearly-scaling, efficient alternative to self-attention. Because of its unidirectional nature, each state in Mamba only has information of its previous states and is blind to states after. Current Mamba-based computer-vision methods typically overcome this limitation by augmenting Mamba's global forward scan with a global backward scan, forming a bi-directional scan that restores a full receptive field. However, this operation doubles the computational load, eroding much of the efficiency advantage that originally Mamba have. To eliminate this extra scans, we introduce LBMamba, a locally bi-directional SSM block that embeds a lightweight locally backward scan inside the forward selective scan and executes it entirely in per-thread registers. Building on LBMamba, we present LBVim, a scalable vision backbone that alternates scan directions every two layers to recover a global receptive field without extra backward sweeps. We validate the versatility of our approach on both natural images and whole slide images (WSIs). We show that our LBVim constantly offers a superior performance–throughput trade-off. That is under the same throughput, LBVim achieves 0.8% to 1.6% higher top-1 accuracy on the ImageNet-1K classification dataset, 0.6% to 2.7% higher mIoU on the ADE20K semantic segmentation dataset, 0.9% higher AP$^b$ and 1.1% higher AP$^m$ on the COCO detection dataset. Our method serves as a general-purpose enhancement, boosting the accuracy of four SOTA Mamba models, namely VMamba, LocalVim, PlainMamba and Adventurer, by 0.5% to 3.4%. We also integrate LBMamba into the SOTA pathology multiple instance learning (MIL) approach, MambaMIL, which uses single directional scan. Experiments on 3 public WSI classification datasets show that our method achieves a relative improvement of up to 3.06% better AUC, 3.39% better F1, 1.67% better accuracy. Our code is available at https://github.com/cvlab-stonybrook/LBMamba.

Published in Transactions on Machine Learning Research (11/2025)
LBMamba: Locally Bi-directional Mamba
Jingwei Zhang'
jinguezhung@tessslommooooo. edu
Deportment of Computer Science, Stony Brook University, Stony Brook, NY, USA Xi Han"
zihan | (ac.. stongbrook. edu
Deportment of Computer Science, Stony Brook University, Stony Brook, NY, USA
Hong Qin
gin@Bcs.tongjrrok.co edu
Deportment of Computer Science, Stony Brook University, Stony Brook, NY, USA
Mahdi S. Hosscini
Concordia University, Montreal, Canada
Hila-Quebed Al Institute, Montreal, Canada
Dimitris Samaras
samamas@es..sonssrrooo.
Depariment of Computer Science, Slony Brook University, Slony Brook, NY, USA
Reviewed on OpenReview: https: // hptp://veppeveee..//m//e net/ forum? id= elaXalIblQ
Abstract
Mamba, a State Space Model (SSM) that accelerates training by recasting recurrence as a parallel selective scan, has recently emerged as a linearly-scaling, efficient alternative to self- attention. Because of its unidirectional nature, each state in Mamba only has information of its previous stales and is blind to states after. Current Mamba-based computer-vision methods typically overcome this limitation by augmenting Mamba's global forward scan with a global backward scan, forming bi-directional scan that restores full receptive field. However, this operation doubles the computational load, eroding much of the effi- ciency advantage that originally Mamba have. To eliminate this extra seans, we introduce LBMamba, a locally bi-directional SSM block that embeds a lightweight locally back- ward scan inside the forward selective scan and exccutes entirely per-thread registers. Building on LBMamba, we present LBVim, scalable vision backbone that alternates scan directions every two layers to recover global receptive field without extra backward sweeps. We validate the versatility of our approach on both natural images and whole slide images (WSIs). We show that our LBVim constantly offers superior perfrmmance-hhooughnee trade-off. That under the same throughput, LBVim achieves 0.8% to 1.6% higher top- accuracy on the ImageNet-IK -KK classification dataset, 0.6% to 2.7 higher mloU on the ADE20K semantic segmentation dataset, 0.9% higher AP¹ and 1.1% higher APM on the COCO detection dataset. Our method serves as a general-purpose purpose enhancement, boost- ing the accuracy of four SOTA Mamba models, namely VMamba, LocalVim, PlainMamba. and Adventurer, by 0.5% to 3.4%. We also integrate LBMamba into the SOTA pathology multiple instance learning (MIL) approach, MambaMIL, which uses single directional scan. Experiments on public WSI classification datasets show that our method achieves a rela- tive improvement of up to 3.06% better AUC, 3.39% better F1, 1.67% better accuracy. Our code is available al https///gdluubboomm///aaaaaaaaao LBMabba
1 Introduction
State-space models (SSMs) have emerged as a compelling alternative self-attention for sequence modeling because their hidden-state recurrence yields linear time and memory complexity with respect to sequence length (Gu et al. 2021a Wang et al. 2022). Yet, conventional SSMs trained with naïve recurrence is still limited by slow training and inference, they cannot leverage efficient parallelism of modern GPUs
*These authors contributed equally to this paper.

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[Baron et al. 2023 Mamba (Gu & Dao 2023) overcomes this by decoupling the state update from the hidden-to-output put convolution and reformulating the computation as a selective parallel scan that runs ef- ficiently on modern GPUs. Consequently, Mamba matches Transformer-level accuracy on long-range tasks while exhibiting far better resolution wise scaling charactcristics, making it an attractive choice for both research and production systems. Tt was first introduced for natural-language processing and has since been adaptod to computer vision (Zhu et al., 2024 Liu et al.) 2024 Huang et al. 2024). Vision models built on Mamba's 's selective-scan kernel delivers substantial GPU speed-ups and memory savings while consistently
outperforming Transformer based baselines.
Standard computer vision mamba. based models scan images multiple times from different directions to enhance their performance (Zhu et al. 2024 Liu et al. 2024 Yang et al. 2024a). There are two causes of such multiple scans: the first one is to overcome the 1D nature of Mamba. Mamba treats an image as a flattened 1D sequence, so a single left-to-right pass captures only row-wise context. To recover vertical dependencies, vision pipelines typically perform an additional scan on the column- wise ordering of patches, yiclding two orthogonal sweeps that logether approximate 2D spatial relationship (Liu el al. 2024 Yang et al.) 2024a) Several dedicated 2D Mamba/SSS /SSM methods have recently been proposed to address this structural mismatch more directly (Zhang et al. 2024 Wang et al. 2024). The second issue is the unidircctional nature of SSMs: the latent state at position t is conditioned only on past positions 1...f t (Gu & Dao 2023). Consequently, the model is blind to information occurring after position i, which often leads to sub-optimal performance on vision tasks. A common solution is to add a reverse (right-to-left or bottom-to-top) pass to restore access to future tokens, producing a bi-directional sean mechanism (Zhu al., 2024). Although this strategy re- establishes full receptive field, each extra sweep roughly doubles the computational load, eroding much of the efficiency advantage that originally Mamba offered.
To eliminate this extra scans required bi-directional scan, we propose LBMamba, a locally bi-directional SSM, together with the LBVim framework for vision tasks, Our main contributions are summarized bellow.
We introduce a local backward scon and a locally bi-directional SSM architecture that integrates the backward update directly into Mamba's forward scan, thereby climinating the costly global backward scan and markedly improving computational efficiency.
We propose a fast herdware-aware thread-level bi-directional scanning operalor that performs the local backward scan entirely in thread- thread-private registers, incurring no additional high-bandwidth memory traffic or inter-thread communication.
We validate the speed (aka throughput)--ccuuaal tradc-off of our architecture by implementing it on two very different domains: natural images and Giga-pixel Whole Slide Images (WSI).
We show that. instead of conducting an extra backward scan, it is more beneficial to scale up the model size under fixed latency budget. Experiments on natural images show that L.VVim achieves better performance- throughput trade-off than the baselines. Under the same throughput, LBVim achieves 0.8% to 1.6% higher top-1 accuracy on the ImageNet-11e 1K classification dataset, 0.6% to 2.7 % higher mloU on the ADE20K semantic segmentation dataset, 0.9% higher AP' and 1.1% higher Apm on the COCO detection dataset. Applying our approach to four diverse state-of-the-are (SOTA) models: VMamba (Liu et al. 2024), LocalVim (Huang et al. 2024), PlainMamba (Yang el al., 2024a), and Adventurer (Wang et al. 2025b), improves their accuracy by 0.5% to 3.4%, demonstrating its effectiveness as a general-purpose enhancement for Mamba- basod models. We also integrate our scanning approach into the SOTA pathology multiple instance lcarning (MIL) approach, MambaMIL (Yang et al.] 20224b). Extensive experiments classification and survival analysis datasets show that our method achieves relative improvement of up to 3.06% better AUC, 3.39% better F1, 1.67% better accuracy.
public datasets for WSI
2 Related work
State Space Model (SSM). SSM (Klmann 1960 is an effective sequence model that represents systems evolving over time by defining hidden states and their transitions, which makes it particularly useful for
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capturing dynamic temporal behavior in sequential data. Gu et al.
(2021b) unified RNNs, temporal con-
volutions, and neural differential equations with a linear state-space
layer and demonstrated the potential of SSM-based models with the HiPPO initialization. Waang et al. (2022) proposed Bi-directional Gated SSM which is able to match BERT (Devlin et al., 2019) pretraining accuracy without attention. S4 (Gu et al. 202 normalized the parameter matrices into a diagonal structure and offered an option to use bi-directional convolution kernel. 2D-SSM (Baron et al. 2023) adopted a 2D-SSM recursion (Kung et al., 1977) and explored scanning image in two or four directions. Similarly, S4ND (Nguyen et al. 2022) extends S4 to images and videos by applying axis-wise updates successively along each spatial direction. Overall, SSM architectures developed prior to Mamba were constrained by slow training efficiency because there lacks an efficient parallel algorithm.
Mamba. Mamba (Gu & Dao 2023 proposed a selective mechanism that makes the model parameters imput-dependent and thus allows remembering important states and discard less relevant. ones, alleviating the forgetting in long sequences. It also introduced a hardware-aware parallel scan algorithm that drastically accelerates state compulation. [Hwang et al. (2024) proposed Hydra, bi-directional Mamba model that using generalized matrix mixers and showed a better performance than BERT. Vim (Zhu et al. (024)) and VMmmba [Liu et al. 2024) are the first two mamba based models in computer vision. Vim (Zhu ct al. 2024) introduced a Vision Mamba block that uses two independent selective SSMs for bi-directional aggregation of information and achieves a global receipt field. VMmmba (Liu et al., 2024) introduced a pyramid Mamba network with a 4-directional scan pattern to achieve a global receipt field and also enhance spatial understanding. It also proposed some solution to alleviate the instability issue in half precision training. Efficient EfficientVMambe (Pei 2024 introduced skip sampling in scanning and replace the majority of VMmaba layers to Inverted
et al. Residual blocks to speed up VMamba. Similarly, MSVMamba (Shi ct al. 2024 combines low resolution scales and high resolution scans to speed up VMamba. Groot (iioo et al. (022)) introduced tree style scanning patten based on the minimum spanning tree, but it did not utilize the parallel scan of Mamba. Mamba-B R
(Wang et al.) 2025a) investigated adding regiters to the Mamba network. Plain ( (Yang et al.) 2024a) used a 4-directional selective scan and adopted a more spatially continuous scan path. 2DMmaba (Zhang ltt al. 2024) introduced a 2D SSM and exlends Mamba's 's parallel scan algorithm into this 2D SSM. Tt also use the 4-directional scan of VMamba in their natural image applications. For all of these 4-directional scans, they are all two groups of bi-dircctional scans applied on two diffcrent ordering of image patches. This bi- directional scan is now the standard approach in most mamba based vision models. A previous work similar to our approach is Adventurer (Wang et al.) 2025b), where the sequence is also reversed every consecutive layers to remove the bi-directional scans. Adventurer enhances the global understanding by prepending an average-pooled token while our method focuses on the local understanding of the model.
Application of Mamba in Whole Slide Images (WSI) analysis. WSTs are usually Giga-pixel images in the pathology domain. Most slides annotated only on slide level, requiring Multi Instance Learning (MIL) methods for WSI classification. It aggregates embedded features from a WSI for slide-level representation.
MIL. approaches are usually based on some attention mechanism (Tsee et al. 2018 Lu et al. 2021 Li et al. 2021) and self-attention mechanism (Shao et al. (021). Recently, S4MIL (Fillioux et al. 2023) introduced S4 model to WSI analysis, which demonstrated the effectiveness of SSM in capturing long-range dependencies, but it does not utilize the parallel scan and thus slow. Yang et al. (Yang et al. 2024b) used Mamba and a sequence reordering mochanism to reduce overfitting and achieved even better performance. M3Mamba (Zheng al., 2025) used dynamic memory bank to overcome the catastrophic forgetting of Mamba for long-term context. Note that these SSM based MIL methods does not use bi-directional scan.
3 Method
We first revisit the recursions used in Mamba and existing standard bi-directional formulations. Then we present LBMamba designed for efficiency, and an associated vision mamba framework: LBVim. Finally, we introduce the low-level CUDA design of I.BMamba.
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Published in Transactions on Machine Learning Research (11/2025)
Mamba's 's unidirectional scan
Standard bi-directional scan
TBMamba scan
One time read/write
Two times read/ /write
One time read/writh /write
Figure 1: (Left): The unidirectional scanning mechanism of the vanilla Mamba (Gu & Dao 2023), where each state only has information of its previous/left states. (Center): Standard bi-directional scanning mechanism (Zhu et al. 2024), where a dedicated backward scan conducted and added to the forward scan. This operation involves an additional read/write of the data and thus doubles the running time. (Right): Our LBMamba scan conducts a locally backward scan which integrated into the forward sean process. This involves only one time read/ write operations and thus very fast.
3.1 SSM in Mamba and existing standard bi-directional SSM
The original SSM Mamba (uu & Dao 2023 a mathematical model used to capture the behavior of continuous dynamic systems. To be integrated into doep models, it discrotized
hy A h/_ + B'
(1)
vf C D12,
(2)
where 2t is the input token, h/ is the latent state at time t, yi is the output token, D is a parameter, Of the state dimension coefficient aggregate N state dimensions into a single output and we use the superscript f denote this is a forward scan instead of a backward one. In this paper, following previous conversion (Zhu et al. 2024), 'forward' and 'backward' represents the scan direction rather than the forward forward/backward /baccwawrd propagation in neural network training. The parameters Af, B and Õ are functions of the imput 2g, which allows the SSM to dynamically adapt to the input context (known as the selective mechanism Gu & Dao 2023 This aggregates important input into the hidden state while unimportant input can be ignored. Mathematically, they are:
Af exp(ArA"), = A,B(aa Of = C(() = softplus(A(zz))
(3)
where A, Bd, and Od are learnable linear functions of I₁. A, represents the time step of the discretization. The selective mechanism in the Mamba block is commonly referred to as selective scan.
From equation 1 we can find that the output vi only contains information of its previous inputs {zzlé < t). It also illustrated figure left. In order to enable global recept field, previous approaches (Zhu al. 2024 Liu al.] 20244 Yang al.] 2024a) commonly apply a backward and add them together as the output Yt (figure ceneter):
h A 4hhi41 + B'Ia
(4)
b ch1 D
(5)
y/ = v + yt
(6)
Note that the additional backward scan need to read/ wwrie the entire sequence with its parameters and thus expensive. For better distinguishing with our locally bi-direction method, we refer to this scan as global bé-direction scan.
3.2 Locally bi-directional SSM architecture
We detail the bi-directional SSM architecture, the key component of LBMamba. In contrast to the global bi-direction scan in figure center that conducts a separate backward scan from the end to the beginning of the sequence, LBMamba conducts local backward scan within sub-soquences (figure [ rrihtt). This process can be integrated into the forward scan process and thus only requires one time read/write, which saves a lot of time.

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As shown in figure 1 right, first conduct global forward scan equation | Then divide the imput sequence of length I into sub-sequences which all have a length of M (M=3 in the figure). M is set to the number of elements one thread processes and we detail it section 3.4 In each of these sub-sequences, we conduct a backward scan as that in equation Specifically, the state hy obtained during the local backward scan
if % = A BIx, otherwise
Blx
Since this backward scan is integrated into the forward scan, have to reuse the same parameters the forward scan. We then add this backward hidden state and forward hidden state h!_ Because the same B' x4 is added in both forward and backward state equation 7 and equation [ we deduct in the summation. This deduction does not count for computation as we can omit it by a simple programming trick (see Appendix
h4 h/. + (h) BIT)).
Ut = C + D'x,
(8)
3.3 Architecture of LBVim
Patch token!
GAP/MHA MLP
Class
LBMamba encoder
Position embeddingi
LBMamba Encoder x
proj
Reverse
Norm
Conv1D
Patch Embedding
Proj
LBMamba scan
Pro
Flatten
The overall architecture LBVim: The input image is split into patches and are embodded as patch lokens. These tokens, combined with positional embeddings, are fed to U LBMamba encoders. Finally, an global average pooling (GAP) layer or a multi-head attention layer (MHA), followed by an MLP head, prediets the image class. (Right): The architecture of LBVim encoder. We reverse the sequence in the end of each encoder such that the global scan direction in LBMamba is switched every two consecutive encoders, ensuring that each token achieves a global receptive field after every two encoders.
Figure 2:
Building upon LBMamba, we introduce the overall architecture of LBVim. LBVim is based on Vim Zhu lt al.) 2024). As shown in figure 2 The patch embedding and position embedding follow the same design as Vim. The embedded patches are then processed by U LBMamba encoders. We replace Vim's global bi- directional selective scan with our locally bidircctional variant. As LBMamba not a global bi-diroctional scan, in one encoder each token does not have a global view of the sequence. To alleviate this problem, we reverse the feature sequence the end of each encoder to alternate the global scan direction (forward backward), It allows each token achieve global receptive field after every two encoders.
Notably, we does not use the class token commonly used in (Dosovitskiy el. al. 2020 Zhu et al. 2024
it underperforms in our model. Instead, we employ an Global Average Pooling (GAP) layer followed by an MLP for final prediction in small sized models. When the size of the model scales up, we use an Multi- Attention with latent query mechanism (MHA) to aggregate features from the last LBMamba encoder:
9KT (N (KT(PU) Vd v(h
class MLP(softtma
where is a single learnable token, similar to the class token, A are the output tokens from the last LBMamba encoder, the dimension of features, K(-). V(.) are learnable lincar functions and class the final prediction. Unlike the self attention mechanism, this attention only has one query and thus has linear time complexity. We find that on larger models, MHA achieves better performance than GAP.
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3.4 Hardware- aware thread-leved bi-directional scanning operator
We present our hardware-qware scanning operator that accelerates LBMamba scans. We first revisit the GPU storage hierarchy and then present our novel operator in detail.
GPU storage hicrarchy. Figure 3 right illustrates the storage hicrarchy of modern GPUs. The green region denotes off-chip GPU memory, with low speed and high capacity. It is referred to as high bandwidth memory (HBM). The orange area denotes on-chip static RAM (SRAM), with high speed but low capacity. The blue region highlights the per-thread registers, the fastest tier yet restricted to at most 255 registers per thread. In typical GPU algorithms, data is transferred from HBM to registers for computation, and the results are stored back to HBM to free SRAM and registers for succeeding compulation. Because registers are private to each thread, inter-thread communication usually traverse the SRAM, which is substantially more costly than intra-thread computation. Large-scale HBM operations are even more expensive. As a consequence, many GPU algorithms (Dao et al.) 2022 Dao 2024), including Mamba, are bounded not by arithmetic computation but by memory bandwidth.
Mamba's global forward scan. Figure 3 outlines the vanilla Mamba's global forward scan algorithm in the blue box. Each GPU thread first fetches tile of P sequence elements from HBM into its registers (P=3 3 in the example). The thread then performs an in-register prefix scan over these P elements. We denote the partial result by hę-tj the hiddon state obtained by scanning from time stop i to To extend the scan across the entire sequence, threads exchange their partial results through SRAM. Specifically, thread j acquires a prefir that represents the scan of all elements preceding its own tile. For instance, thread T2 in the figure receives h1-3, the scan of xı to T3. Because this step requires inter-thread communication, it is significantly more expensive than intra-thread compulation. After obtaining the prefix, each thread combines it with its private elements, completing the global scan, and finally writes the results back to HBM. Note that both the local scan and the prefix application traverse the same P elements, doubling the arithmetic cost relative to a purely sequential scan. The conventional bi-directional scan defined by Eq. 44) is very expensive because it executes two full forward scans, thereby incurring twice the HBM traffic and inter-thread communication overhead.
Thread level bi-directional scan. The proposed locally bidirectional scan operator executes the backward scan entirely within each thread, avoiding any extra synchronization. As illustrated in figure 3 we begin with the same global forward scan used in vanilla Mamba. Before the results are written back to HBM, cach thread performs a second sean over its private tile in the reverse (backward) direction (highlighted by the red box). This backward pass mirrors the thread scan in the forward pass but processes elements in a reversed order. The forward and backward partial sums are then added and streamed to HBM. This backward scan is thread level and does not need to apply prefix, reducing half the computation compared with global scan. Also, because the backward scan never leaves the registers, it introduces additional HBM traffic thread communication and is therefore extremely fast. Although this extra pass increases the overall arithmetic workload by 27%, the running time rises by only 2% (sce section 4.2
4 Experiments
In this section, we present series of experiments to evaluate the performance of LBVim and compare it mainly to DeiT (Tovrron et al. 2021 ) and Vim (Zhu et al. 2024 across various visual tasks. We also apply LBMamba to SOTA MIL method MambaMLL and SRMambaMLL to evaluate its performance on WSI datasets (Yang et al. 2024b). Following Mamba Gu & Dao 2023). we set the number of elements cach thread thread process (M) based on the sequence length L: when L > 256 (images larger than 256 x 256), a thread processes M- = 16 elements; for 128 < L < 256 (images between 256 x 256 and 256 x 128), it processes M 8 clements; and when L < 128, the workload is reduced M 4. All natural image models are trained on 4/2 Nvidia A100/H100 GPUs. All throughput, GPU memory consumption and pathology Nvidia Quadro RTX 8000 GPU. Other implementation details are in Appendix.
models are
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Mamba's Global forward scan (Reg. and SRAM)
Local backward scan (Reg. only)
Thread 7
h1-1
11-1
M3-1
Read data
Thread forward
Prefix 0
Apply prefix
Thread backward
Write result
h1-2
11-2
1--2
Reg
Scan
Thread- thread Interaction
hz-3
scan
fg--3
SRAM
Thread T
HBM
Thread
h
Apply
44
Thread
66-4
Read
data
forward
h4-5
prefix
111-5
backward
R6-55
result
Write
Reg.: 330TB/s
:
scan
h--6
Prefix=/
h
scan
h16-1
HBM: 1.5TB/s
Figure 3: Our hardware-aware LBMamba CUDA operator with thread-level locally bi-directional scan. Blue color represents operations registers (Reg.), Orange color represents operations SRAM and green color represents those on HBM. Blue box shows the scanning operations by the vanilla Mamba: two threads Ti and T2 first loads 3 elements from HBM to registers. The global forward scan is then conducted as follows: 1) Each thread performs an in-register prefix scan over 3 elements. 2) Threads exchange their partial results through SRAM to get the prefix of each thread. 3) Each thread combines its prefix with its private elements, completing the global scan. Finally, the scanned results are write back HBM. Red box highlights the extra scanning operations by the LBMamba: Each thread performs an in-register backward scan over 3 elements (the same as step 1 except the direction) and add it to the forward scan results. All the extra operations are in registers and thus it is very fast. hi-j is the the partial result, the hidden state obtained by scanning from time step i to j.
4.1 Natural image classification
We first evaluate LBVin on the ImageNet-1K dataset (Deng et al. 2009), which contains 1.28M training images and 50K validation images from 1,000 categories. All models are trained on the training set, and top-1 accuracy on the validation set is reported. For fair comparisons, we follow the training setting in (Zhu et al. 2024). To be specific, we train our models for 300 epochs using a batch size of 1,024 for tiny model and a batch size of 512 otherwise. We use the AdamW optimizer (Loshchilov & Hutter 2017] with momentum of 0.9, cosine annealing learning with an initial value of 10 5-epoch warmup period and a weight decay of 0.05. For data augmentation, we apply standard techniques: random cropping, horizontal flipping, label-smoothing regularization, mixup, and random erasing.
Table 1 shows our LBVim family with similar sized Vim basclines. At the tiny scale (192 feature dimension), with out a global backward scan, LBVin-Ti delivers a 82% higher throughput than Vim-Ti and while using I M fewer parameters. The top-1 accuracy trails Vim-T by 2.4%, which we attribute to the current inability of our architecture process the class token. Comparing with Vim-Ti with global average pooling, our method achieves comparable performance (only 0.2% lower). Moving to the small setting (384 feature dimension). the accuracy gap narrows to 0.7% as the model capacity increases, yet throughput remains markedly (69%) higher.
Compared with Vim, LBVim has better speed-accuracy trade-off. We introduce two intermediate model configurations, LBVim-300 and LBVim-528. Where 300 and 528 are the dimension features. As shown in table their throughput closely match those of Vim-T and Vim-S, but surpassing the corresponding baselines by 1.6% and 0.8% in accuracy, respectively. As illustrated in figure 4 the securacy-throughhee curve of our models consistently lies in the upper-right quadrant relative to Vim, highlighting more favorable trade-off across wide operating range. These findings also show that method enables novel way to develop Mamba-based methods by scaling the model for higher accuracy, without slowing down the speed.
4.2 FLOPs and speed analysis
Although LBMamba performs significant additional computations compared to vanilla Mamba, it maintains fast processing spoeds. At the CUDA-kernel level (table 2 LBMamba kernel exccutes 27% more floating- point operations than the vanilla Mamba kernel for all tested resolutions. Despite this arithmetic inflation, throughput drops by only 1.9-2.3. 2.3% across all tested resolutions and the GPU memory consumption remains
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Accuracy-Throughout Trade-off
Table 1: Top-1 accuracy (%) and throughput (images/second, de- noted as T.P.) of LBVim variants on ImageNet-1K with 224 x 224 inputs. LBVim-Ti matches Vim-Ti (with global average pooling) while delivering an 82% higher throughput. LBVim-S is only 0.7
02
LBVim
Top-1 Top-1 Accuracy
percentage points below Vim-S yet runs 69% faster. LBVim-300 and LBVin-528 attain substantially higher accuracy than Vim-
Ti and Vim-S, respectively, at comparable throughput.
Method
#Param
FLOPs
T.P.
Top-1 1 acc%
74
DefT-Ti
6M
1.3G
2256
72.2
150
400
1600
Throughput (images/s)
Vim-ITi
7M
1.6G
889
76.1
Vim-Ti (GAP)
7M
1.6G
897
73.9
Figure 4: The accuracy-throughput trade- off curve of Vim and LBVim. The curve of LBVim consistently lies in the upper-right quadrant relative to Vim, highlighting a more favorable trade-off. We include the base version of Vim better illustrate the trends on larger models.
LBVim-Ti
6M
1,4G
1621
73.7
LBVim-300
15M
3.1G
906
77.7
DeiT-S
22M
4.6G
917
79.8
Vim-S
26M
5.3G
392
80.3
LBVim-S
24M
4.9G
663
79.6
LBVim-528
44M
9.0G
398
81.1
Table 2: Comparison of poating--pinn operations per image (FLOPs), throughput. (T.P., images feature maps per second), and GPU memory consumption (Mem.) during inference with batch size of 128. Input images are preloaded to GPU. For fair comparison, use global average pooling for Vim-Ti.
Image size Method
256 x 256
512 x 512
1024 x 1024
FLOPs
T.P.
Mem.
FLOPs
T.P.
Mem.
FLOPs
T.P.
Mem.
Mamba CUDA kernel
17M
87.4K
209M
69M
14.3K
827M
277M
3.8K
3.3G
LBMamba CUDA kernel
22M
85.4K
209M
88M
14.0K
827M
352M
3.7K
3.3G
DeiT-Ti
1.7G
1638
564M
10.5G
226
5.6G
99.8G
20
248G
Vim-Ti
2.1G
795
755M
8.3G
162
2.9G
33.4G
42
11.0G
LBVim-Ti
1.9G
1421
608M
7.4G
296
2.2G
29.6G
77
8.6G
LBVim-300
4.1G
799
862M
16.4G
169
3.4G
65.4G
47
12.6G
unchanged. The minimal slowdown stems from the fact that the extra computations are confined to on- chip registers, incur no thread-thread interaction, and bypass the HBM or SRAM traffic. Zooming out to the model, LBVim-Ti, leveraging the LBMMambakernel and omits the additional scan operation required by Vim-Ti, achieves 79%-83% higher throughput, accompanied with a 19-22% reduction in GPU memory. LBVim-300 has nearly doubled FLOPs compared with Vim-Ti but is as fast as Vim-Ti. It achieves superior performance (section 4.1 with only 14-17% additional GPU memory. This confirms that LBVim delivers a superior efficiency without sacrificing scalability. We show that this superior efficiency scale to training well (see Appendix B
4.3
Downstream tasks on natural images
We evaluate the performance of LBVim on downstream tasks, including semantic segmentation on ADE20K dataset (Zhou et al.] 2019), and object detection and instance segmentation the COCO 2017 dataset (Lin
et al., 2014). The training framework is based the MMSegmenation (Contributors, 2020 and MMDe-
tection (Chen et al.)
2019 librarics, following (Zhu et al. 2024h in utilizing UperNet (iaao et al. 2018)
and Cascade Mask R-CNN (Cai & Vasconcelos 2019) as the segmentation and detection networks, respec- tively. For a fair comparison, we add Linear layers to LBVim-300 and LBVim-528, adjusting their output dimension be the same Vim-Ti and Vim-S, respectively. We find that for these two downsteam tasks, which operates on dense features and therefore does not rely on dedicated class token, the performance gap between LBVim and the Vim baseline narrows markedly.
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Semantic Segmentation. TableB3eet presents the mloU results the ADE20K dataset. The lightweight LBVim-Ti reaches 40.2 mloU, only 0.8% below its Vim-Ti counterpart, while LBVim-S attains 44.2 mIoU, trailing Vim-S by 0.7%. When we modestly increase capacity to match Vim throughput, the gains become pronouncod: LBVim-300 achieves 43.7 mloU, 2.7 % above Vim-Ti. Similarly, LBVim-528 pushes the score to 45.5 mIoU, surpassing Vim-S by 0.6%.
Object Detection and Instance Segmentation. Table 3 right reports the average precision results the COCO 2017 dataset. For box AP (AP"), LBVim-Ti attains 45.4%, which is par with Vim-Ti (45.7%) and 1.1% above DeiT-Ti. Mask accuracy (AP") follows the same trend: LBVim-ID Timmii matches Vim-Ti at 39.2% and outperforms the DeiT bascline by 1.1%. As to LBVim-300, which has similar through put as Vim- Ti, achieves 0.9% higher AP and 1.1% higher AP". These results on segmentation and detection further demonstrate that LBVimdelivers a more favorable seerracy-fffciennyy trade-off on downstream tasks.
4.4 Ablation study
We ablate the two principal designs in LBVim the I.MMambakrneel and the sequence reversing operation on the ImageNet-1K classification dataset. Table 4 shows that removing the LBMamba kernel (w.o. LBMamba) lowers accuracy by 1% with negligible impact (0.4%) on throughput. The drop confirms that the locally backward scan embedded in LBMamba improves feature propagation. On the other hand, eliminating sequence reversing operation (w.o. sequence reverse) results in larger accuracy degradation of 4.5%. The sequence reversing operation alternates the scan direction of LBVim layers, granting each token global receptive field every two layers, and thus strengthening long-range context modeling. Without this global receptive field, the performance drop dramatically.
We also conduct an ablation on the output aggregation scheme: global average pooling (GAP) versus multi- head attention pooling (MAP), across four capacity levels of I.BVimin table right. For the tiny, 300 and small variants, GAP delivers 0.7%, 0.4% and 0.2% higher top-1 accuracy, respectively. It clear that when the model scales up in dimension, the advantage of GAP narrows. When the model scales to LBVim-528, the MAP gains 1.1% al negligible 0.7 % slowdown over GAP, indicating that the expressive benefit of learned pooling emerges only when sufficient parameters are available. This size-dependent trend aligns with prior observations that simple pooling is preferable for some compact models (Pan et al. 2021), whereas attention- based aggregation becomes advantageous in larger models (Dosovitskiy et al. 2020
4.5 Generalizability to SOTA methods
In addition to its strong performance on the Vim baseline, LBMamba demonstrates robust. generalizability when applied to other SOTA models. We integrated LBMamba with four diverse architectures: VMamba Liu et al. 2024), LocalVim (Huang al. 2024.. PlainMamba (Yang al., 2024a), and Adventurer (Wang
Table 3: Left: The performance of our LBVim the ADE20K semantic segmentation dataset. FLOPs and and throughput (T.P.) are measured with an input size of 512 x 2048. Right: The performance of LBVim on the COCO detection dataset (image are of size 1024x 1024). APb and Apm denote the average precision for bonding boxes and masks, respectively. T.P. denotes average throughput.
Backbone
#Param.
Apd
AP
AP
APÊ
AP
APł
Backbone
# Param.
FLOPs
T.P.
mloU
DefT-Ti
64M
44.4
63.0
47.8
26.1
47.4
61.8
DeiT-Ti
11M
221G
16
39.2
Vim-Ti
66M
45.7
63.9
49.6
26.1
49.0
63.2
Vim-Ti
13M
145G
25
41.0
LBVim-Ti
66M
45.4
63.8
49.3
25.5
49.4
62.4
LBVim-Ti
12M
141G
35
40.2
LBVim-300
74M
46.6
65.2
50.5
27.0
50.6
63.6
VB
21M
178G
26
43.7
Backbone
T.P.
APM
AP
AP%
AP
APm
APT
DeiT-S
43M
359G
9
41.0
Vim-S
46M
227G
15
44.9
DeiT-Ti
7.1
38.1
59.9
40.5
18.1
40.5
58.4
LBVim-S
44M
219G
21
44.2
Vim-T
7.3
39.2
60.9
11.7
18.2
41.8
60.2
Vim--8528
65M
306G
15
45.5
LB LBVim-ii
7.7
39.2
60.9
41.8
17.9
42.1
60.0
VBV-m0300
7.3
40.3
62.5
43.1
19.3
43.5
60.4

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Published in Transactions on Machine Learning Research (11/2025)
Table 4: Left:Thee ablation study of LBMamba and the sequence reserving operation in LBVim on the ImageNet-Ik classification dataset. Right: Ablation of global average pooling (GAP) vs, multi-head atten- tion pooling (MAP) in LBVim on the ImageNet-ll classification dataset. T.P. denotes inference throughput (imagcs/sccond).
GAP
MAP
Model
Method
T.P.
Top-1 acc%
T.P.
Acc%
T.P
Acc%
LBVim-T
1621
73.7
LBVin-Ti
1621
73.7
1610
73.0
w.o. LBMamba
1628
72.7
LBVim-300
906
77.7
901
77.3
w.o. sequence reverse
1711
69.2
LBVimm-
663
79.6
658
79.4
LBVim-528
401
80.0
398
81.1
ct al. 2025b). designating the enhanced versions as LBVMamba, LBLocalVim, LBPlainMamba, and LBAd- venturer, respectively. For the bidirectional models (VMamba, LocalVim, and PlainMamba), we applied both LBMamba and the sequence reversal operation. For the unidirectional Adventurer, only LBMamba was ap- plied. Each enhanced model was configured ensure its throughput remained comparable to its respective baseline, facilitating fair comparison. Notably, while the original LocalVin employs search for the optimal scanning direction ('w. search"), should be considered a lower bound. As shown in Table 5 our method consistently improves the Top-1 ac- curacy by 0.5% to 3.4% across all baselines, highlighting its effectiveness as general-purpose enhancement for Mamba-basee models. Detailed experimental configurations are provided in Appendix
LBLocalVim does not. Therefore, the performance of L.BLocalVim-T
Table 5: Top-1 accuracy comparison on the ImageNet-11K 1K dataset after applying LBMamba to four SOTA baselines. Models are evaluated at comparable throughput (T.P.) to demonstrate performance gains. Adven- turer is a unidirectional scan model, while the others are bidirectional. "w./w.o. search' indicates whether the LocalVim model uses its scan direction search.
Method
# Param.
T.P.
Top-1 acc.
Method
# Param.
T.P.
Top-1 1 acc.
VMamba-Nano
7M
998
78.1
PlainMamba-Li LBPlainMamba-LI
7M
385
77.9
ABVMamba-NMe
9M
1003
79.2
15M
385
80.6
LocalVim-T (w.o. search)
8M
331
75.8
Adventurer
12M
1555
78.2
LocalVim-T (w. search)
8M
331
76.2
LBAdventurer
12M
1545
78.7
LBLocalVim-I (w.o. search)
15M
356
79.6
4.6 WSI classification
To verify LBMamba beyond natural images, we embed it into the SOTA Multiple-Instann Instance Learning (MIL) framework, MambaMIL and SRMambaMIL (Yang al. 2024b), and name them LBMambaMIL and SRLB- MambaMIL, respectively. We evaluate them
public available Whole Slide Image datasets, PANDA (prostate grade asscssment) (Bulten et al. 2022 TCGA-NSCLC (adenocarcinoma
squamous lung can- cer) and TCGA-BRCA (breast invasive carcinoma sub-typing) (log). Dataset and training details are listed in Appendix D and F As shown in table 6 with an additional locally backward scan, LBMambaMIL gener- ally performans better than MambaMIL, achieves up to 3.06% higher accuracy, up to 3.39% higher F1 and up to 1.67% higher AUC. Similar improvement also observed on SRMamba, SRLEMambaMIL achieves achieves up 2.98% higher accuracy, up 2.90% higher F1 and up 1.48% higher AUC, demonstrating that the LBMamba able improve the performance of unidirectional scans. It also shows that our method generalize well on Giga-pixel pathology images.
4.7 Visualization of Effective Receptive Fields.
The Effective Receptive Field (ERF) [Luo al. 2016) refers to the region in the input space that contributes to the activation of specific output unit. We conduct comparative analysis of the central pixel's ERF
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Table 6: The comparison of accuracy (Ace), Fl and AUC on five WSI classification datasets. We conducted
each exporiment five times using five different random soeds and reported their mean. The highest metrics are marked as bold.
PANDA
TCGA-NSCLC
TCGA-BRCA
Method
Acc
F1
AUC
Ace
F1
AUC
Acc
F1
AUC
AB-MIL
0.4883
0.4269
0.7797
0.8758
0.8756
0.9572
0.9292
0.8893
0.9747
DSMIL
0.4633
0.3847
0.7660
0.8782
0.8780
0.9567
0.9375
0.8961
0.9770
CLAM
0.4802
0.4224
0.7820
0.8804
0.8803
0.9536
0.9333
0.8960
0.9753
DTFD-MIL
0.4704
0.3853
0.7665
0.8736
0.8732
0.9559
0.9271
0.8809
0.9633
TransMIL
0.4636
0.3970
0.7728
0.8850
0.8845
0.9626
0.9375
0.9028
0.9763
MambaMIL
0.4679
0.4216
0.7781
0.8758
0.8756
0.9582
0.9333
0.8939
0.9657
LBMambaMIL
0.4985
0.4499
0.7948
0.8874
0.8870
0.9582
0.9333
0.9015
0.9673
SRMambaMIL
0.4711
0.4209
0.7776
0.8850
0.8849
0.9592
0.9313
0.8900
0.9657
SRLBMambaMII,
0.5009
0.4499
0.
0.9035
0.9032
0.9619
0.9375
0.9042 0.9681
DeiT, Vim and LBVim at tiny and small scale, both before and after training. The results presented in figure iilustrate that DeiTT shows global ERE's but it suffers from the quadratic complexity of self attention. Vim shows global ERFs and LBVim also shows global ERFs, proving that LBMamba with sequence reversing operation does not have side effect on ERF.
Before training
1.0
0.8
After After training
0.2
DeiT-Ti
Vim-Ti
LBVim-Ti
DeiT-S
Vim-S
LBVim-S
Comparison of Effective Receptive Fields (ERF) (Luo et al. 2016 on DeiT, Vim and LBVim at tiny and small scale. Pixels with higher intensity indicate larger responses related to the central pixel.
Figure 5:
5 Conclusion
We proposed LBMamba, thread-level bi-directional state-space module that marries the linear complex- ity of Mamba with a register-resident local backward scan. The resulting LBVim backbone dispenses with costly global reverse passes yet still attains a full receptive field by alternating scan directions across con- secutive layers. Extensive experiments on four diverse vision tasks confirm three key findings: Efficiency: LBMamba adds negligible runtime overhead (2%) while eliminating one full sweep, translating to up to 83% higher throughput. Accuracy: Under equal or lower latency budgets, LBVim surpassos Vim by up 1.6% ImageNet top-1 accuracy, 2.7% mloU on ADE20K, 0.9% APd and 1.1% Apm gains on COCO, 1.67% in AUC gains WSI benchmarks. Scalability: When the size of model scales up, the accuracy throughput Pareto front consistently dominates global bi-directional basclines, showing better accuracy-throughpe trade-off. Together, these advances indicate that local bi-directionality plus sequence reversing operation is sufficient for strong global context modeling while preserving the hallmark efficiency of SSMs.
Limitation. LBMamba less effective when dedicated class token appended the sequence. We systematically evaluated four common variants: head class token (prepended), middle class token (inserted at the sequence midpoint), and double class token (both prepended and appended) strategy, but none of them
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achieves better performance than simple global average pooling (see Appendix C A plausible explanation is that the local backward scan treats the class token as an ordinary feature vector whose receptive field is confined to its local window; this reinforces local patterns while diluting the holistic summary that the token meant to capture. This also limits the application of LBMamba in tokens similar to the class token, like the registers in Mamba-R (Wang al. 2025a). Unifying local bi-directionality with an effective global summarization mechanism therefore remains an open rescarch question, and we leave a deeper investigation of this to future work.
Acknowledgement
This work was partially supported by USA NSF grants IIS-2123920 [D.S], ITS-2212046 [D.S], IIS-1715985 [H.Q], 11S-1812606 [H.Q], NSERC-DG 202-5570 [M.S.H] and Amazon Research Award [M.S.HH] H.].
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15


Reviews:
[{"review_id": "MC23y3TSDy", "reviewer": "Reviewer_TUr6", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n(1) This paper proposes a novel scanning strategy for Mamba that speeds up the throughput with about 2% runtime overhead.\n(2) This paper is well-organized and easy to follow.\n\nWeaknesses:\n(1) While achieving superior throughput-accuracy trade-offs, LBVim-300 demonstrates notably higher model complexity than Vim-Ti (+14% parameters, +50% FLOPs) under comparable throughput conditions.\n(2) In Section 4, the authors say that \"we set the number of elements each thread thread process (M ) based on the sequence length L: when L > 256 (images larger than 256 ×256), a thread processes M = 16 elements; for 128 < L ≤256 (images between 256 ×256 and 256 ×128), it processes M = 8 elements; and when L ≤128, the workload is reduced to M = 4.\" How is the value of M determined? More releated ablation studies should be added.\n(3) it lacks visualized results for Semantic Segmentation, Object Detection, and Instance Segmentation, which could provide a clearer understanding of the model’s performance and effectiveness in these tasks.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "LBVim introduces a thread-level bidirectional state-space module combining Mamba’s linear complexity with register-optimized local backward scans. The LBVim backbone achieves full receptive fields via alternating scan directions across layers, eliminating costly global reverse passes. Key results across four vision tasks show (1) Efficiency: Just 2% runtime overhead for 83% higher throughput\n(2) Accuracy: Outperforms Vim by +1.6% (ImageNet), +2.7% mIoU (ADE20K), +0.9% APb/+1.1% APm (COCO), +1.67% AUC (WSI)\n(3) Scalability: Superior accuracy-throughput Pareto frontiers at scale", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "（1）I think the parameters, FLOPs, throughput, and accuracy should be balanced, and thus the structure of LBVim should be modified.\n（2）The visulized results of Semantic Segmentation, Object Detection and Instance Segmentation should be added.", "broader_impact_concerns": "WSI performance gains (↑3.06% AUC) might encourage deployment without clinical trials, risking false positives in diagnostics.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "iRYU3D9tOt", "reviewer": "Reviewer_dvCR", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "**Strengths**:\n- An Efficient Architecture: The introduction of LBMamba, which integrates a local backward scan into Mamba’s forward pass, is a hardware-aware optimization and seems novel in the field. By avoiding a full global backward scan, the method significantly reduces computational overhead while maintaining performance.\n- Strong Empirical Results: This paper demonstrates consistent improvements across multiple benchmarks (ImageNet, ADE20K, COCO, and three WSI datasets), showing superior accuracy-throughput trade-offs compared to existing Mamba-based and Transformer-based models.\n- Hardware-Aware Optimization: The thread-level bi-directional scan seems good, as it minimizes memory traffic by keeping computations in registers. The related experimental results are promising and well-justified (Table 2).\n\n**Weaknesses**:\n- Limitation with Class Tokens: This paper mentioned that LBMamba underperforms when a class token is used, which is a common design in many vision models (*e.g.*, ViTs). This could restrict its applicability in architectures relying on such tokens.\n\n- Missing Baselines: For natural image-based experiments, the primary comparisons are conducted against Vim and DeiT, but recent efficient architectures (*e.g.*, EfficientVMamba [1] and VMamba [2]) are not included. For WSI-based ones, a recent Mamba-based approach, M3amba [3], is missing. A broader comparison would better situate LBVim’s efficiency gains.\n\n- Training Efficiency: This paper focuses on inference speed but does not provide a detailed comparison of training time or memory usage during training, which is important for large-scale applications. The authors are encouraged to discuss it.\n\nReference:\n\n[1] Pei et al., EfficientVMamba: Atrous Selective Scan for Light Weight Visual Mamba, arXiv Preprint, 2024.\n\n[2] Liu et al., VMamba: Visual State Space Model, NeurIPS, 2024.\n\n[3] Zheng et al., M3amba: Memory Mamba is All You Need for Whole Slide Image Classification, CVPR, 2025.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper introduces LBMamba, a new locally bi-directional State Space Model (SSM) block divsed to improve the efficiency and performance of Mamba-based models in vision tasks. In LBMamba, the authors propose a lightweight local backward scan integrated within the forward selective scan of Mamba, eliminating the need for computationally expensive global backward scans. Moreover, the authors propose a thread-level bi-directional scanning operator that performs backward scans entirely in thread-private registers, minimizing memory traffic and inter-thread communication. Based on these, LBVim is introduced as a vision backbone that alternates scan directions every two layers to achieve global context modeling without additional backward sweeps. Extensive experiments show that the proposed methods can reduce computational overhead while maintaining a full receptive field. Besides, the proposed framework demonstrates superior performance-throughput trade-offs across multiple vision tasks like classical cv tasks and WSI-based tasks.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "No other requested changes. Please refer to Weakness", "broader_impact_concerns": "No any broader impact concerns.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "XZdKGIo1Yb", "reviewer": "Reviewer_QtS8", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "### Strengths\n\n1.  **Novel Architectural Concept:** The core idea of embedding a local bi-directional scan within a single, efficient forward pass is an interesting and potentially valuable contribution to the design of SSMs.\n2.  **Hardware-Aware Implementation:** The development of a custom CUDA kernel demonstrates a thoughtful approach to making the proposed architecture practical and computationally efficient.\n\n### Weaknesses\n\nThe paper, in its current form, has several major weaknesses that limit its impact and call its contributions into question.\n\n1.  **Insufficient Comparison with State-of-the-Art:** The experimental evaluation is not comprehensive enough to situate LBMamba within the current landscape of SSMs.\n    *   **Missing Baselines:** The paper fails to discuss or compare against several highly relevant and more advanced SSM architectures, such as GrootVL [1], MSVMamba [2], and Adventure [3]. Furthermore, comparisons with variants like plainmamba, localmamba, and mamba-r [4] are necessary for a complete evaluation.\n    *   **Incomplete Reporting:** Key performance metrics, such as Frames Per Second (FPS) for the DeiT baseline, are missing, which prevents a fair and thorough comparison of throughput.\n\n2.  **Limited Empirical Performance and Ablation:** The experimental results provided are not convincing and, in some cases, undermine the paper's claims.\n    *   **Marginal Gains:** The performance improvement over the Vim baseline is minimal.\n    *   **Contribution Misattribution:** The ablation study in Table 4 is particularly concerning, as it suggests that the primary source of performance gain is the sequence reverse operation, not the proposed LBMamba block. This raises significant questions about the actual value of the core contribution.\n    *   **Poor Downstream Performance:** The LBMamba-based model (LBVim) performs worse than the Vim baseline on object detection and segmentation tasks, indicating a potential lack of generalizability.\n    *   **Kernel Efficiency:** The reported improvement from the custom CUDA kernel is described as marginal, which diminishes the impact of the hardware-related contribution.\n\n3.  **Unclear Motivation and Novelty:**\n    *   The motivation behind the sequence reverse operation is not well-explained.\n    *   This operation appears to have a significant overlap with the method presented in Adventure [3], but the authors fail to discuss this, making the novelty of this component unclear.\n\n4.  **Clarity and Presentation:** The manuscript suffers from several presentation issues that hinder readability.\n    *   **Inconsistent Notation:** Symbols are used inconsistently (e.g., lowercase `q` vs. uppercase `K` in Equation 9) and are sometimes confusing (e.g., the `ft` term).\n    *   **Missing Citations:** Key terms and related works are not cited properly (e.g., \"MambaMIL\" at the end of Section 1).\n\n[1]. Xiao, Yicheng, et al. \"Grootvl: Tree topology is all you need in state space model.\" NeurIPS (2024).\n\n[2]. Shi, Yuheng, et al. \"Multi-scale vmamba: Hierarchy in hierarchy visual state space model.\" NeurIPS (2024).\n\n[3]. Wang, Feng, et al. \"Adventurer: Optimizing Vision Mamba Architecture Designs for Efficiency.\" CVPR (2025).\n\n[4]. Wang, Feng et al. “Mamba-R: Vision Mamba ALSO Needs Registers.” CVPR (2025).", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper introduces LBMamba, a State Space Model (SSM) block that incorporates a lightweight, locally bi-directional scan within the standard forward selective scan mechanism. The authors claim this design, executed efficiently using per-thread registers, enhances model performance. To support this, they also propose a hardware-aware CUDA operator designed to accelerate the local backward scan.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "To be considered for publication, the paper requires a major revision to address the following points:\n\n1.  **Strengthen Experimental Evaluation:**\n    *   Conduct a comprehensive comparison against the aforementioned state-of-the-art SSMs (GrootVL, MSVMamba, Adventure, etc.) on all reported tasks.\n    *   Report all relevant metrics, including FPS, for all models to ensure a fair comparison.\n\n2.  **Justify Core Contribution:**\n    *   Provide a more detailed analysis and discussion of the ablation study results. The authors must clearly demonstrate the value added by the LBMamba block itself, independent of the sequence reverse operation.\n    *   Investigate and explain the performance degradation on downstream tasks.\n\n3.  **Clarify Novelty:**\n    *   Provide a clear motivation for the sequence reverse operation.\n    *   Explicitly discuss the relationship and distinguish the novelty of your work from Adventure [3].\n\n4.  **Improve Presentation:**\n    *   Perform a thorough proofread of the manuscript to correct all notational inconsistencies, define all terms, and ensure consistent formatting.\n    *   Add all missing citations.", "broader_impact_concerns": "NA", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)