arXiv:2606.19538v1 Announce Type: cross Abstract: Convolutional networks, recurrent networks, and transformers each encode different inductive biases -- locality, sequential memory, and content-dependent pairwise interaction -- and have remained mathematically distinct since their inception. We show that this fragmentation reflects not a fundamental diversity in how signals should be processed, but rather incomplete views of a single underlying mathematical object: a learnable integral transform. We introduce the Integral Transform Network (ITNet), a unified architecture built around a learnab
The continuous evolution of AI architectures, driven by the need for more efficient and generalizable models, leads to research exploring foundational mathematical structures that unify disparate approaches.
A unified mathematical framework for core AI architectures could simplify model design, accelerate research, and lead to more robust and powerful AI systems.
Traditional distinctions between convolutional, recurrent, and attention mechanisms may dissolve into a single, learnable integral transform, fundamentally altering how AI models are conceived and built.
- · AI researchers
- · Deep learning framework developers
- · Companies with diverse AI applications
- · Specialized AI architecture teams
- · Legacy AI hardware not optimized for integral transforms
The development of a unified architectural primitive for neural networks simplifies experimentation and reduces the effort required to build novel models.
This unification could lead to more general purpose AI models that are inherently more efficient and adaptable across different data modalities and tasks.
A foundational shift in AI architecture might accelerate the development of truly autonomous AI agents, blurring the lines between different AI capabilities.
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Read at arXiv cs.LG