reviews: 
Comments: The paper discusses the inefficiencies of handling long sequences in machine learning models, particularly in audio tasks such as source separation or super-resolution. In these tasks, splitting sequences into overlapped chunks is commonly used to preserve contextual information. This overlapped processing method increases tensor size, redundant computations, and memory usage.
 
To address this and remove/reduce the overlap, the authors propose two strategies: random sequence shifting and variable chunk sizes between the network layers. These strategies ensure that context is not lost while being faster and less memory-hungry. However, this potentially leads to reduced performance due to inconsistencies in sequences between the layers, which the authors propose to overcome by increasing the model parameters, thus maintaining the model performance.

Experiments are conducted on two task-model combinations: 1) Speech Separation & SepFormer, and 2) Audio Super-Resolution and NU-Wave2. Experiments show reduced training/inference time and memory usage while maintaining model performance, albeit increasing model size and storage requirements.
Strengths: -	New approach to audio sequence processing task.
-	Applies specific modifications to two model architectures and shows memory and time improvements, enabling larger models to be created to maintain or improve performance
Weaknesses: **Limited Scope in Model Testing**: Although the paper tests two models, the scope remains narrow, limited to specific architectures for specific audio tasks. Moreover, the proposed methods require very careful engineering depending on the particular model. The dual-path model structure is a very specific architecture class used in source separation, there are several other models which do not apply overlapped sequence modeling, or even if they do, for example models working in spectrogram domain, as per the authors own discussions, it is not trivial to apply these methods directly to the STFT since it will break the invertibility.

**Trade-offs in Model Complexity**: By increasing parameters and layers to compensate for the lack of overlap, the model complexity and size are affected. While these modifications lead to computational efficiency, they come at the cost of increased model parameters, which may not always be viable for applications with strict size limitations.

**Insufficient Discussion on Frequency Domain Overlaps**: The approach in the frequency domain (NU-Wave2) is less straightforward and generalizable due to the complexities of handling spectral information. The proposed modifications reduce overlaps but also risk spectral leakage, making the findings less conclusive for broader applications.

**Limited Experimentation**:  They paper presents experimnetal results for each method-model on a single dataset and the results are without any confidence intervals. There is no attempt at evaluating the generalizbility of the proposed methods. There is no ablation study, e.g., how much the performance degrades with same model size, how does the performance degrade as the overlap is reduced, etc.

**Clarity**: The text descriptions are very dense and difficult to follow, since for each model the method is completely different, makes it even harder. While a lot of space has been given to overlapped waveform and STFT illustrations, the actual architecture diagrams are not very informative. Also, the more complex modifications of the NU-Wave2 architectural changes are not illustrated with a diagram.

**Lack of Training Details**: The paper lacks key details about the training infrastructure and hyperparameters for reproducibility.
Recommendation: 1
Reviewer confidence: 5
Soundness/correctness: 2
Clarity: 1