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Memba: Membrane-driven Parameter-Efficient Fine-Tuning for Mamba

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Brain-inspired computingMambaFine-tuning
Abstract:

State Space Models (SSMs) have emerged as powerful alternatives to attention-based Transformers, with Mamba demonstrating impressive efficiency and scalability. As these models grow increasingly larger, the need for Parameter-Efficient Fine-Tuning (PEFT) methods becomes critical to adapt pre-trained Mamba to downstream tasks without prohibitive computational costs. However, previous approaches simply apply traditional Transformer-tailored PEFT methods without addressing the unique temporal processing dynamics of SSMs. To address this limitation, we propose Memba, a membrane-driven PEFT approach specifically designed for Mamba. Memba introduces Leaky Integrate Membrane (LIM) neurons as bio-inspired gating mechanisms that naturally accumulate membrane potentials over time, enhancing selective information retention. By strategically combining LIM neurons with Low-Rank Adaptations (LoRA) and cross-layer membrane transfer, our approach significantly improves Mamba's temporal modeling capabilities. Extensive experiments across language and vision tasks demonstrate that Memba achieves substantial improvements over existing PEFT methods.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes Memba, a membrane-driven PEFT method for Mamba that introduces Leaky Integrate Membrane neurons combined with LoRA and cross-layer membrane transfer. It resides in the State-Based PEFT Approaches leaf, which contains only two papers including this one and State-offset Tuning. This is a notably sparse research direction within the broader PEFT Method Design for SSMs branch, suggesting the state-based adaptation paradigm for SSMs remains relatively underexplored compared to weight-space or hybrid approaches.

The taxonomy reveals that PEFT Method Design for SSMs divides into three sibling leaves: State-Based PEFT Approaches (2 papers), Weight-Space PEFT (1 paper applying low-rank decomposition), and Hybrid PEFT Mechanisms (1 paper combining state modulation with low-rank updates). Neighboring branches include PEFT Empirical Analysis and Benchmarking (3 papers evaluating existing methods) and SSM Architecture Design (covering foundational models like Mamba and domain-adapted variants). Memba's bio-inspired gating mechanism diverges from the weight-decomposition focus of SSMLoRA and the hybrid strategy of combining state modulation with low-rank updates, instead emphasizing temporal accumulation through membrane potentials.

Among 30 candidates examined across three contributions, none were found to clearly refute any of Memba's claims. The core Memba approach examined 10 candidates with 0 refutable matches, as did the LIM neuron mechanism and the performance claims. The single sibling paper, State-offset Tuning, manipulates state offsets rather than introducing membrane-based temporal accumulation, suggesting conceptual differentiation within the sparse state-based PEFT space. The limited search scope means these findings reflect top-30 semantic matches and citation expansion, not exhaustive coverage of all SSM adaptation literature.

Based on the limited search scope of 30 candidates, Memba appears to occupy a relatively novel position within state-based PEFT for SSMs, a sparsely populated research direction. The bio-inspired membrane mechanism and cross-layer transfer represent distinct design choices compared to the single identified sibling work. However, the analysis does not cover the full landscape of neuroscience-inspired adaptation methods or all possible SSM tuning strategies beyond the examined candidates.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Parameter-efficient fine-tuning for state space models. The field has evolved around adapting state space models (SSMs) such as Mamba[8] and its variants to downstream tasks without full retraining. The taxonomy reveals several main branches: PEFT Method Design for SSMs explores novel tuning strategies tailored to SSM architectures, while PEFT Empirical Analysis and Benchmarking evaluates these methods across diverse settings. SSM Architecture Design investigates foundational model structures like Structured State Spaces[15] and Selective State Spaces[19], and SSM Applications demonstrates deployment in vision (Vision Mamba[3], VMamba[5], VideoMamba[7]), graph learning (Graph-mamba[13]), and specialized domains (InsectMamba[26], Hyperspectral Anomaly Detection[28]). Test-Time and Online Adaptation addresses dynamic scenarios (Test-time SSM[4], Temporal Test-Time[29]), while Model Compression and Efficiency and State-Space Modeling Theory and Methods provide complementary perspectives on optimization and theoretical grounding. Within PEFT Method Design, a particularly active line focuses on state-based tuning approaches that modify or augment the internal state dynamics of SSMs rather than traditional weight updates. Memba[0] exemplifies this direction by introducing memory-based parameter-efficient mechanisms that operate on state representations, closely aligning with State-offset Tuning[1] which adjusts state offsets to achieve efficient adaptation. These state-centric methods contrast with adapter-style approaches like Mamba-Adaptor[41] and low-rank techniques such as SSMLoRA[16], which insert trainable modules or decompose weight matrices. The trade-off centers on whether to preserve SSM recurrence properties through state manipulation or to leverage established PEFT paradigms from transformers. Memba[0] sits squarely in the State-Based PEFT cluster, sharing conceptual ground with State-offset Tuning[1] but differing in how memory structures interact with the state evolution, offering a distinct angle on balancing expressiveness and parameter economy in SSM fine-tuning.

Claimed Contributions

Memba: membrane-driven PEFT approach for Mamba

The authors introduce Memba, a parameter-efficient fine-tuning method specifically designed for Mamba models. It enhances temporal processing in the gating branch without altering the selective scan components, addressing limitations of applying Transformer-tailored PEFT methods to state space models.

10 retrieved papers
Leaky Integrate Membrane (LIM) neuron with cross-layer membrane propagation

The authors propose a bio-inspired gating mechanism called LIM neuron that accumulates membrane potentials over time to enhance selective information retention. It includes a chunking strategy for efficient long-sequence processing and transfers averaged membrane states across layers to maintain temporal coherence throughout the network.

10 retrieved papers
State-of-the-art PEFT performance on language and vision tasks

The authors demonstrate through comprehensive experiments that Memba achieves superior performance over existing parameter-efficient fine-tuning methods on both commonsense reasoning benchmarks and visual task adaptation datasets, while using fewer trainable parameters than competing approaches.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Memba: membrane-driven PEFT approach for Mamba

The authors introduce Memba, a parameter-efficient fine-tuning method specifically designed for Mamba models. It enhances temporal processing in the gating branch without altering the selective scan components, addressing limitations of applying Transformer-tailored PEFT methods to state space models.

Contribution

Leaky Integrate Membrane (LIM) neuron with cross-layer membrane propagation

The authors propose a bio-inspired gating mechanism called LIM neuron that accumulates membrane potentials over time to enhance selective information retention. It includes a chunking strategy for efficient long-sequence processing and transfers averaged membrane states across layers to maintain temporal coherence throughout the network.

Contribution

State-of-the-art PEFT performance on language and vision tasks

The authors demonstrate through comprehensive experiments that Memba achieves superior performance over existing parameter-efficient fine-tuning methods on both commonsense reasoning benchmarks and visual task adaptation datasets, while using fewer trainable parameters than competing approaches.

Table of Contents