Title: WKV-sharing embraced random shuffle RWKV high-order modeling for pan-sharpening
Venue: NeurIPS 2025
Accepted: yes
Keywords: Pan-sharpening

Image Fusion
Abstract: Pan-sharpening aims to generate a spatially and spectrally enriched multi-spectral image by integrating complementary
cross-modality information from low-resolution multi-spectral image and texture-rich panchromatic counterpart. In this work, we propose a
WKV-sharing embraced random shuffle RWKV high-order modeling paradigm for pan-sharpening from Bayesian perspective, coupled with random weight manifold distribution training strategy derived from Functional theory to regularize the solution space adhering to the
following principles: 1) Random-shuffle RWKV. Recently, the Vision RWKV model, with its inherent linear complexity in global modeling,
has inspired us to explore its untapped potential in pan-sharpening tasks. However, its attention mechanism, relying on a recurrent
bidirectional scanning strategy, suffers from biased effects and demands significant processing time. To address this, we propose a novel
Bayesian-inspired scanning strategy called Random Shuffle, complemented by a theoretically-sound inverse shuffle to preserve
information coordination invariance, effectively eliminating biases associated with fixed sequence scanning. The Random Shuffle
approach mitigates preconceptions in global 2D dependencies in mathematical expectation, providing the model with an unbiased prior.
In line with similar spirit of Dropout, we introduce a testing methodology based on Monte Carlo averaging to ensure the model’s output
aligns more closely with expected results. 2) WKV-sharing high-order. Regarding KV’s attention score calculation in spatial mixer of RWKV, we leverage WKV-sharing mechanism to transfer KV activations across RWKV layers, achieving lower latency and improved trainability, and revisit the channel mixer in RWKV, originally a first-order weighting function, and redevelop its high-order potential by sharing the gate mechanism across RWKV layer. Comprehensive experiments across pan-sharpening benchmarks demonstrate our model’s effectiveness, consistently outperforming state-of-the-art alternatives


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes an efficient Random-Shuffle RWKV approach for pan-sharpening, addressing computational cost and attention bias issues. The key innovation lies in combining a Bayesian-inspired random shuffle strategy with WKV-sharing for high-order modeling, achieving linear complexity while improving performance. Experimental results demonstrate superior efficiency and accuracy compared to existing methods. The work offers both theoretical contributions and practical benefits for remote sensing image fusion.\n\nSTRENGTHS AND WEAKNESSES:\nThe paper makes theoretical contributions by introducing a Bayesian perspective to address RWKV's recurrent scanning bias, with the Random Shuffle strategy and Monte Carlo averaging providing a principled solution. The high-order WKV-sharing mechanism is innovative, enabling efficient higher-order interactions while maintaining linear complexity. From a computational perspective, the work convincingly demonstrates the advantages of linear complexity over traditional Transformer architectures. The incorporation of random weight manifold training based on Functional theory offers a rigorous alternative to conventional regularization approaches. The experimental validation is thorough and compelling, with consistent performance improvements demonstrated across standard pan-sharpening benchmarks.\n\nMinor concerns:\n1）While the authors aim to provide readers with preliminary concepts in the introduction by including some equations, there is some redundancy between the formulas in the introduction and the method section. Consider simplifying to avoid repetition.\n2）In the loss function section, the authors specifically discuss the differences between random manifold loss and fixed point loss. They should further validate its effectiveness by maintaining the L1 loss while varying the fixed point loss (e.g., including Fourier transform and wavelet-based losses).\n3）The core design is detailed in Fig. 4, but the figure quality is suboptimal and requires further improvement.\n\nQUESTIONS:\nPlease address the above questions in weakness part, especially for random weights manifold optimization function.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces RS-RWKV, a novel deep learning framework for pan-sharpening based on the RWKV architecture. The core problem it addresses is the trade-off between the global modeling capability of transformers and their quadratic computational complexity, which makes them challenging for high-resolution remote sensing imagery. The authors propose three main contributions: \n\n(1) A \"Random Shuffle\" scanning strategy for the RWKV model to eliminate the biases inherent in fixed-sequence processing, providing a more robust global context. \n\n(2) A \"WKV-sharing\" mechanism for both spatial and channel mixers, which the authors frame as enabling \"high-order\" interactions by sharing activations and gate mechanisms across layers to reduce latency and improve modeling capacity. \n\n(3) A \"Random Weight Manifold Loss\" based on Taylor's unfolding to regularize the optimization process, aiming for better performance than traditional loss functions. Extensive experiments on several benchmark datasets demonstrate that the proposed RS-RWKV model significantly outperforms a wide range of state-of-the-art methods in both accuracy and computational efficiency.\n\nSTRENGTHS AND WEAKNESSES:\nPaper Strength\n\n(1) Innovative and Well-Motivated Methodology: The paper presents a compelling solution to a significant problem in applying attention-based models to high-resolution imagery. The use of a Random Shuffle strategy to debias the RWKV's recurrent scanning is a clever and theoretically sound idea inspired by Bayesian principles. This, combined with the efficiency of the underlying RWKV architecture, provides a strong foundation for the work.\n\n(2) Strong Empirical Performance and Efficiency: The experimental results are a major strength. The proposed RS-RWKV model demonstrates consistent and significant improvements over numerous strong baselines across three different datasets (Table 1). Furthermore, the analysis of computational complexity (Figure 1) and effective receptive field (Figure 7) convincingly showcases the model's practical advantages in terms of memory, FLOPs, and inference speed, which are critical for real-world applications like pan-sharpening.\n\n(3) Comprehensive Evaluation and Ablation Studies: The authors have conducted a thorough evaluation of their proposed method. The ablation studies in Table 3 and Figure 9 systematically validate the contribution of each component (KV-cache, random shuffle, manifold loss, etc.). These studies provide clear evidence that each proposed innovation contributes positively to the final performance, strengthening the paper's claims.\n\nPaper Weakness\n\n(1) Clarity of \"High-Order\" Modeling: The concept of \"high-order modeling\" is central to the paper's narrative but lacks a clear and rigorous definition. While the paper describes the mechanisms for sharing WKV activations and gate mechanisms across layers (e.g., Eq. 51, 55, 58), it does not adequately explain how this translates mathematically into higher-order statistical interactions. The connection feels more descriptive than formal, making it difficult for the reader to grasp the precise theoretical underpinnings of this claim.\n\n(2) Explanation of Manifold Loss: The \"Random Weight Manifold Loss\" is presented as a key component of the supervision, but its explanation in the main paper is very brief (Section 2.1). The concept, based on Taylor's unfolding from external work, is non-trivial. Without a more detailed, self-contained explanation of its intuition and mechanism in the main body, readers may struggle to understand its function and why it is superior to more common frequency-domain or perceptual losses.\n\n(3) Justification of Monte Carlo Sampling: The paper finds that a single sample (M=1) is sufficient for inference (Figure 9), which is an excellent practical result. However, this finding is somewhat counter-intuitive given the motivation of using Monte Carlo averaging to approximate the expectation over all random shuffles. The paper would be strengthened by a more in-depth discussion of why the performance saturates so quickly. This could be due to the network learning to be invariant to the shuffle order, or the variance being very low, but this is not explored.\n\nQUESTIONS:\n(1) Regarding the \"high-order modeling,\" could you please provide a more formal or at least a more intuitive mathematical explanation of how sharing the gate mechanism in the channel mixer and wkv activations in the spatial mixer across layers leads to interactions beyond the second order? For example, can you show how this is analogous to capturing higher-order moments of the feature distribution?\n\n(2) The Random Weight Manifold Loss is an interesting but complex concept. Could you please elaborate on the intuition behind using a Taylor's unfolding manifold for regularization in the specific context of pan-sharpening? What specific image characteristics (e.g., texture, spectral artifacts) does this loss function target more effectively than other advanced losses (e.g., LPIPS, Charbonnier, frequency-domain losses)?\n\n(3) The result that a single sample is sufficient for Monte Carlo inference is very promising. Could you offer deeper insights into this phenomenon? Have you analyzed the variance of the model's output with respect to different random shuffles at test time? Does the network effectively learn to be invariant to the token processing order, and if so, how?\n\n(4) For reproducibility, could you please specify the architecture of the Mapping() MLPs used throughout the model (e.g., number of layers, expansion factors)? Additionally, what value or range of values was used for the hyperparameter λ that balances the structure loss and manifold loss in Equation 28?\n\nLIMITATIONS:\nNo, it is better to state the limitations of the proposed method in the paper.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 1, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-27"}

comments: SUMMARY:
This work introduces a Random-Shuffle RWKV framework for pan-sharpening. Especially, the proposed method integrates Bayesian randomization into the RWKV architecture, ensuring unbiased global modeling while maintaining linear computational complexity. Additionally, the WKV-sharing mechanism enhances high-order feature interactions without sacrificing efficiency. The experimental results demonstrates clear improvements in both computational performance and fusion quality compared to existing methods.

STRENGTHS AND WEAKNESSES:
Strength:

1) This work presents an innovative RWKV-based approach for pan-sharpening. The proposed random shuffle strategy effectively addresses sequence bias in RWKV, supported by solid Bayesian reasoning. 

2) The WKV-sharing mechanism for high-order modeling is well-motivated. Computational efficiency claims are convincingly demonstrated through comprehensive benchmarks. 

Weakness:

While the methodology shows promise, several aspects require clarification and improvement：

1) This paper employs Monte Carlo sampling for expectation estimation while uses only a single sample. However, standard MC theory typically employs more samples to yield better accuracy. The authors should provide explanation for this design.

2) Regarding the random shuffle operation applied to (R,K,V) components, the manuscript does not specify whether all components use the same shuffle pattern or they are shuffled independently.

QUESTIONS:
1) What is the rationale for using single-sample Monte Carlo estimation instead of conventional multi-sample approaches?

2) Whether the random shuffling of (R, K,V) components follows identical patterns? What is its impact on the model performance?

LIMITATIONS:
Yes.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 4
DATE: 2025-06-29

comments: SUMMARY:
This paper proposes a novel framework named RS-RWKV for pan-sharpening, aiming to generate spatially and spectrally enhanced multi-spectral images by fusing low-resolution multi-spectral and panchromatic images. Experiments on WorldView-II, WorldView-III, and GaoFen2 datasets show RS-RWKV significantly outperforms state-of-the-art methods in PSNR, SSIM, and computational efficiency, with linear complexity and reduced memory usage.

STRENGTHS AND WEAKNESSES:
Achieves superior performance across benchmarks, especially in large-scale images where traditional Transformers face memory issues. Theoretically grounded in Bayesian theory and functional analysis, ensuring principled design.
Parameters like u and w in Re-WKV (Eq. 33) lack explanation for selection logic.

QUESTIONS:
How does the inverse shuffle strictly preserve information invariance mathematically? Are there edge cases where information loss occurs?  Provide proofs or simulations verifying information preservation under various data distributions.  
Can authors analyze gradient flow during training or compare convergence with/without WKV-sharing via ablation.

LIMITATIONS:
Random weight manifold loss may increase training iterations, but convergence speed vs. traditional losses is unaddressed. Performance under noisy conditions (e.g., low SNR) lacks validation. Monte Carlo averaging relies on modern accelerators, limiting resource-constrained deployments.  Expand the limitations section to discuss training efficiency, noise resilience, and hardware adaptability, proposing future lightweight optimizations.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-05