InfoMamba: An Attention-Free Hybrid Mamba-Transformer Model

2026-03-20 · Source: cs.LG updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Expert, medium

Summary

InfoMamba is a novel attention-free hybrid architecture designed to overcome the computational limitations of Transformers and the interaction weaknesses of Mamba-style selective state-space models (SSMs) in sequence modeling. It addresses the challenge of balancing fine-grained local modeling with long-range dependency capture under computational constraints. The architecture is grounded in a "consistency boundary" analysis, which characterizes when diagonal short-memory SSMs can approximate causal attention and identifies structural gaps. InfoMamba integrates a concept-bottleneck linear filtering layer for minimal-bandwidth global interaction, reducing complexity to O(nk+k^2), with a selective recurrent stream. These are coupled via "information-maximizing fusion" (IMF), which dynamically injects global context into SSMs and uses a mutual-information-inspired objective to enforce complementary information usage. Experiments across classification, dense prediction, and non-vision tasks demonstrate InfoMamba's superior accuracy-efficiency trade-offs and near-linear scaling compared to state-of-the-art Transformer and SSM baselines.

Key takeaway

For AI Scientists and Research Scientists developing sequence models, InfoMamba offers a principled approach to achieve strong accuracy with near-linear scaling, avoiding the quadratic complexity of Transformers. You should consider integrating a concept-bottleneck global filtering layer with selective recurrent SSMs, leveraging information-maximizing fusion to ensure complementary information processing and overcome the limitations of purely attention-based or SSM-based architectures.

Key insights

InfoMamba combines linear filtering and selective SSMs via information-maximizing fusion for efficient, high-performance sequence modeling.

Principles

• Diagonal SSMs struggle with high-rank global interactions.
• A concept bottleneck can provide minimal-bandwidth global interfacing.
• Information-maximizing fusion encourages complementary pathway specialization.

Method

InfoMamba uses a concept-bottleneck linear filtering layer for global context, coupled with a selective recurrent SSM stream via Information-Maximizing Fusion (IMF), guided by a mutual-information-inspired objective to reduce redundancy.

In practice

• Use consistency boundary analysis to identify SSM limitations.
• Implement concept-bottleneck filtering for global context.
• Apply information-maximizing fusion for hybrid architecture integration.

Topics

• InfoMamba
• State-Space Models
• Transformers
• Hybrid Architectures
• Consistency Boundary Analysis

Best for: AI Scientist, Research Scientist, AI Researcher, AI Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.