Kalman Linear Attention: Parallel Bayesian Filtering For Efficient Language Modelling and State Tracking
arXiv:2602.10743v2 Announce Type: replace Abstract: State-space language models such as Mamba and gated linear attention (GLA) offer linear-complexity, parallelisable alternatives to transformers, but their linear state updates limit expressivity and robust state tracking. We close this gap from a probabilistic angle, casting sequence mixing as exact Bayesian filtering with the Kalman filter as the core primitive. Classical Kalman filters give principled state and uncertainty estimates but are viewed as inherently sequential; we show that reparameterising them in information form turns their u
This paper presents a novel approach to improving the efficiency and expressivity of state-space language models, building on recent advances in this field which challenge the dominance of transformers.
Improving the architectural foundations of AI models can lead to significant breakthroughs in performance, efficiency, and the types of problems AI can solve, impacting a wide range of applications from language processing to state tracking.
This research introduces a parallelizable, linear-complexity method for improved state tracking in language models, potentially making these models more robust and scalable than current approaches while retaining computational advantages over transformers.
- · AI researchers
- · NLP developers
- · Data centers
- · AI hardware manufacturers
- · Less efficient AI architectures
More powerful and efficient language models become available for various applications.
Reduced computational costs for training and deploying advanced AI models could accelerate AI development and accessibility.
New classes of AI applications become feasible due to enhanced state-tracking and reduced computational overhead, driving broader AI integration into critical systems.
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