Search: SSMs - ai.jp.net

Research Paper #Particle Physics, Supersymmetry, Lepton Flavor Violation 🔬 ResearchAnalyzed: Jan 3, 2026 18:20

Lepton Flavor Violation in Supersymmetric Seesaw Model

Published:Dec 30, 2025 11:49

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1 min read

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ArXiv

Analysis

This paper investigates lepton flavor violation (LFV) within the Minimal R-symmetric Supersymmetric Standard Model with Seesaw (MRSSMSeesaw). It's significant because LFV is a potential window to new physics beyond the Standard Model, and the MRSSMSeesaw provides a specific framework to explore this. The study focuses on various LFV processes and identifies key parameters influencing these processes, offering insights into the model's testability.

Key Takeaways

•The paper analyzes lepton flavor violation (LFV) within the MRSSMSeesaw model.
•It investigates specific LFV processes like $\ell_i ightarrow \ell_j γ$, $\ell_i ightarrow 3\ell_j$, and Higgs decays $h ightarrow \ell_i \ell_j$.
•The study identifies non-diagonal elements related to initial and final leptons as key parameters influencing LFV.
•The research provides insights into the testability of the MRSSMSeesaw model through LFV searches.

Reference

“The numerical results show that the non-diagonal elements involving the initial and final leptons are main sensitive parameters and LFV sources.”

Permalink ArXiv

Paper #llm 🔬 ResearchAnalyzed: Jan 3, 2026 16:00

MS-SSM: Multi-Scale State Space Model for Efficient Sequence Modeling

Published:Dec 29, 2025 19:36

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1 min read

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ArXiv

Analysis

This paper introduces MS-SSM, a multi-scale state space model designed to improve sequence modeling efficiency and long-range dependency capture. It addresses limitations of traditional SSMs by incorporating multi-resolution processing and a dynamic scale-mixer. The research is significant because it offers a novel approach to enhance memory efficiency and model complex structures in various data types, potentially improving performance in tasks like time series analysis, image recognition, and natural language processing.

Key Takeaways

•MS-SSM is a multi-scale state space model.
•It addresses limitations of traditional SSMs.
•It uses multi-resolution processing and a dynamic scale-mixer.
•It improves sequence modeling, especially in long-range and hierarchical tasks.
•It outperforms prior SSM-based models on various benchmarks.

Reference

“MS-SSM enhances memory efficiency and long-range modeling.”

Permalink ArXiv

Research Paper #Deep Learning, State Space Models, Memory Optimization 🔬 ResearchAnalyzed: Jan 3, 2026 19:16

Breaking the Memory Wall for SSMs with Phase Gradient Flow

Published:Dec 28, 2025 20:27

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1 min read

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ArXiv

Analysis

This paper addresses a critical memory bottleneck in the backpropagation of Selective State Space Models (SSMs), which limits their application to large-scale genomic and other long-sequence data. The proposed Phase Gradient Flow (PGF) framework offers a solution by computing exact analytical derivatives directly in the state-space manifold, avoiding the need to store intermediate computational graphs. This results in significant memory savings (O(1) memory complexity) and improved throughput, enabling the analysis of extremely long sequences that were previously infeasible. The stability of PGF, even in stiff ODE regimes, is a key advantage.

Key Takeaways

•Proposes Phase Gradient Flow (PGF) to overcome memory limitations in SSM backpropagation.
•PGF achieves O(1) memory complexity, significantly reducing VRAM usage and increasing throughput.
•Enables sensitivity analysis on extremely long sequences (e.g., chromosome-scale) that were previously infeasible.
•Maintains stability in stiff ODE regimes, unlike some alternative approaches.

Reference

“PGF delivers O(1) memory complexity relative to sequence length, yielding a 94% reduction in peak VRAM and a 23x increase in throughput compared to standard Autograd.”

Permalink ArXiv

Research Paper #Neuroscience, Brain-Computer Interfaces, Deep Learning 🔬 ResearchAnalyzed: Jan 3, 2026 19:35

DFINE for Nonlinear Modeling of Human iEEG Activity

Published:Dec 28, 2025 05:05

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1 min read

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ArXiv

Analysis

This paper introduces an extension of the DFINE framework for modeling human intracranial electroencephalography (iEEG) recordings. It addresses the limitations of linear dynamical models in capturing the nonlinear structure of neural activity and the inference challenges of recurrent neural networks when dealing with missing data, a common issue in brain-computer interfaces (BCIs). The study demonstrates that DFINE outperforms linear state-space models in forecasting future neural activity and matches or exceeds the accuracy of a GRU model, while also handling missing observations more robustly. This work is significant because it provides a flexible and accurate framework for modeling iEEG dynamics, with potential applications in next-generation BCIs.

Key Takeaways

•DFINE is a deep learning framework that integrates neural networks with linear state-space models.
•DFINE is extended for modeling multisite human intracranial electroencephalography (iEEG) recordings.
•DFINE outperforms linear state-space models in forecasting neural activity.
•DFINE handles missing observations more robustly than baseline models.
•DFINE's advantage is more pronounced in high gamma spectral bands.

Reference

“DFINE significantly outperforms linear state-space models (LSSMs) in forecasting future neural activity.”

Permalink ArXiv

Research Paper #Dynamical Systems, Time Delay Systems, Spectral Submanifolds, Inertial Manifolds 🔬 ResearchAnalyzed: Jan 3, 2026 20:14

Existence of Spectral Submanifolds in Time Delay Systems

Published:Dec 26, 2025 15:32

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1 min read

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ArXiv

Analysis

This paper investigates the existence and properties of spectral submanifolds (SSMs) in time delay systems. SSMs are important for understanding the long-term behavior of these systems. The paper's contribution lies in proving the existence of SSMs for a broad class of spectral subspaces, generalizing criteria for inertial manifolds, and demonstrating the applicability of the results with examples. This is significant because it provides a theoretical foundation for analyzing and simplifying the dynamics of complex time delay systems.

Key Takeaways

•Proves the existence of spectral submanifolds (SSMs) in time delay systems.
•Establishes properties like smoothness, attractivity, and conditional uniqueness of SSMs.
•Generalizes criteria for the existence of inertial manifolds.
•Demonstrates the applicability of the results with examples.

Reference

“The paper shows existence, smoothness, attractivity and conditional uniqueness of SSMs associated to a large class of spectral subspaces in time delay systems.”

Permalink ArXiv

Research #SSM 🔬 ResearchAnalyzed: Jan 10, 2026 08:51

Lag Operator SSMs: A Geometric Framework for Structured State Space Modeling

Published:Dec 22, 2025 02:25

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1 min read

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ArXiv

Analysis

This ArXiv article proposes a novel geometric framework for structured state space modeling using Lag Operator SSMs. The core of the paper likely involves theoretical contributions related to the mathematical properties and potential applications within the field of AI and machine learning.

Key Takeaways

•Focuses on structured state space modeling.
•Utilizes a geometric framework.
•Employs Lag Operator SSMs.

Reference

“The article is an ArXiv submission.”

Permalink ArXiv

Research #llm 📝 BlogAnalyzed: Dec 24, 2025 07:57

Adobe Research Achieves Long-Term Video Memory Breakthrough

Published:May 28, 2025 09:31

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1 min read

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Synced

Analysis

This article highlights a significant advancement in video generation, specifically addressing the challenge of long-term memory. By integrating State-Space Models (SSMs) with dense local attention, Adobe Research has seemingly overcome a major hurdle in creating more coherent and realistic video world models. The use of diffusion forcing and frame local attention during training further contributes to the model's ability to maintain consistency over extended periods. This breakthrough could have significant implications for various applications, including video editing, content creation, and virtual reality, enabling the generation of more complex and engaging video content. The article could benefit from providing more technical details about the specific architecture and training methodologies employed.

Key Takeaways

Reference

“By combining State-Space Models (SSMs) for efficient long-range dependency modeling with dense local attention for coherence...”

Permalink Synced

Research #llm 📝 BlogAnalyzed: Jan 3, 2026 07:49

Mamba Explained

Published:Mar 28, 2024 01:24

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1 min read

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The Gradient

Analysis

The article introduces Mamba, a new AI model based on State Space Models (SSMs), as a potential competitor to Transformer models. It highlights Mamba's advantage in handling long sequences, addressing a key inefficiency of Transformers.

Key Takeaways

•Mamba is a new AI model based on State Space Models (SSMs).
•It is presented as an alternative to Transformer models.
•Mamba addresses the inefficiency of Transformers in processing long sequences.

Reference

“Is Attention all you need? Mamba, a novel AI model based on State Space Models (SSMs), emerges as a formidable alternative to the widely used Transformer models, addressing their inefficiency in processing long sequences.”

Permalink The Gradient

Research #llm 📝 BlogAnalyzed: Dec 26, 2025 14:26

A Visual Guide to Mamba and State Space Models: An Alternative to Transformers for Language Modeling

Published:Feb 19, 2024 14:50

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1 min read

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Maarten Grootendorst

Analysis

This article provides a visual explanation of Mamba and State Space Models (SSMs) as a potential alternative to Transformers in language modeling. It likely breaks down the complex mathematical concepts behind SSMs and Mamba into more digestible visual representations, making it easier for readers to understand their architecture and functionality. The article's value lies in its ability to demystify these emerging technologies and highlight their potential advantages over Transformers, such as improved efficiency and handling of long-range dependencies. However, the article's impact depends on the depth of the visual explanations and the clarity of the comparisons with Transformers.

Key Takeaways

•Mamba and SSMs are presented as alternatives to Transformers.
•The article uses visual aids to explain complex concepts.
•Potential benefits of Mamba include improved efficiency and long-range dependency handling.

Reference

“(Assuming a relevant quote exists in the article) "Mamba offers a promising approach to address the limitations of Transformers in handling long sequences."”

Permalink Maarten Grootendorst

Lepton Flavor Violation in Supersymmetric Seesaw Model

Analysis

Key Takeaways

MS-SSM: Multi-Scale State Space Model for Efficient Sequence Modeling

Analysis

Key Takeaways

Breaking the Memory Wall for SSMs with Phase Gradient Flow

Analysis

Key Takeaways

DFINE for Nonlinear Modeling of Human iEEG Activity

Analysis

Key Takeaways

Existence of Spectral Submanifolds in Time Delay Systems

Analysis

Key Takeaways

Lag Operator SSMs: A Geometric Framework for Structured State Space Modeling

Analysis

Key Takeaways

Adobe Research Achieves Long-Term Video Memory Breakthrough

Analysis

Key Takeaways

Mamba Explained

Analysis

Key Takeaways

A Visual Guide to Mamba and State Space Models: An Alternative to Transformers for Language Modeling

Analysis

Key Takeaways

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