Transformers are Stateless Differentiable Neural Computers

📰 ArXiv cs.AI

Transformers can be viewed as stateless Differentiable Neural Computers (DNCs) with a formal derivation showing equivalence between causal Transformer layers and sDNCs

advanced Published 23 Mar 2026

Action Steps

Derive the formal equivalence between causal Transformer layers and stateless Differentiable Neural Computers (sDNCs)
Analyze the implications of this equivalence for the design and training of transformer-based models
Explore potential applications of this insight in areas such as natural language processing and computer vision

Who Needs to Know This

AI researchers and engineers working on transformer architectures and differentiable neural computers can benefit from this insight to improve their understanding of these models and their applications

Key Insight

💡 Transformers can be viewed as a type of stateless Differentiable Neural Computer

Key Takeaways

Transformers can be viewed as stateless Differentiable Neural Computers (DNCs) with a formal derivation showing equivalence between causal Transformer layers and sDNCs

Full Article

Title: Transformers are Stateless Differentiable Neural Computers

Abstract:
arXiv:2603.19272v1 Announce Type: cross Abstract: Differentiable Neural Computers (DNCs) were introduced as recurrent architectures equipped with an addressable external memory supporting differentiable read and write operations. Transformers, in contrast, are nominally feedforward architectures based on multi-head self-attention. In this work we give a formal derivation showing that a causal Transformer layer is exactly a stateless Differentiable Neural Computer (sDNC) where (1) the controller

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