arXiv:2605.23603v1 Announce Type: new Abstract: We introduce the Preisach Attention Layer (PAL), a novel sequence modelling architecture grounded in the classical Preisach hysteresis operator from mathematical physics. PAL replaces the softmax attention mechanism with a binary relay operator parameterised by learned activation and deactivation thresholds, maintaining a stack of local extrema as its internal state. A single-layer PAL-Transformer with O(1) depth is Turing-complete under arbitrary precision arithmetic, achievable through simulation of a two-stack pushdown automaton -- in contrast
The continuous drive for more efficient and capable AI architectures motivates novel approaches to fundamental components like attention mechanisms.
This development represents a significant architectural innovation in AI, potentially leading to more advanced and efficient sequence modeling with implications for various AI applications.
The proposed 'Preisach Attention Layer' introduces a hysteretic, state-maintaining mechanism that could fundamentally alter how models process sequential data, offering Turing-complete capabilities within a single layer.
- · AI researchers and developers
- · Companies building advanced AI models
- · Sectors requiring sophisticated sequence processing
- · Developers reliant solely on traditional softmax attention
- · Systems with high computational constraints that cannot leverage new architectur
More powerful and efficient AI models for tasks involving sequential data.
Reduced computational overhead or improved performance in specific AI applications due to novel attention mechanisms.
The development of entirely new AI capabilities unlocked by a more nuanced understanding and implementation of sequential memory.
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