Transformers are Stateless Differentiable Neural Computers
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 has no recurrent internal state, (2) the external memory is a write-once matrix of value vectors, (3) content-based addressing via keys implements attention, and (4) multi-head attention corresponds to multiple parallel read heads. We further extend this equivalence to cross-attention, showing that encoder-decoder Transformers are precisely sDNCs with distinct read-from and write-to memories. Our results provide a unified memory-centric interpretation of Transformers and contribute to the ongoi
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 has no recurrent internal state, (2) the external memory is a write-once matrix of value vectors, (3) content-based addressing via keys implements attention, and (4) multi-head attention corresponds to multiple parallel read heads. We further extend this equivalence to cross-attention, showing that encoder-decoder Transformers are precisely sDNCs with distinct read-from and write-to memories. Our results provide a unified memory-centric interpretation of Transformers and contribute to the ongoing effort to place modern large language models in a principled computational framework.
Executive Summary
This article provides a formal derivation showing that a causal Transformer layer is exactly a stateless Differentiable Neural Computer (sDNC). The authors demonstrate that the controller in the Transformer has no recurrent internal state, the external memory is a write-once matrix of value vectors, content-based addressing via keys implements attention, and multi-head attention corresponds to multiple parallel read heads. This equivalence is extended to cross-attention, showing that encoder-decoder Transformers are precisely sDNCs with distinct read-from and write-to memories. The results contribute to the ongoing effort to place modern large language models in a principled computational framework, offering a unified memory-centric interpretation of Transformers.
Key Points
- ▸ Transformers can be viewed as stateless Differentiable Neural Computers (sDNCs)
- ▸ Causal Transformer layers are equivalent to sDNCs with specific properties
- ▸ Cross-attention in encoder-decoder Transformers is equivalent to sDNCs with distinct read-from and write-to memories
Merits
Unified Memory-Centric Interpretation
The article provides a unified memory-centric interpretation of Transformers, contributing to the ongoing effort to understand large language models.
Principled Computational Framework
The results offer a principled computational framework for understanding and analyzing modern large language models.
Demerits
Limited Generalizability
The article's findings may be limited to specific types of Transformers and may not generalize to all architectures.
Lack of Experimental Validation
The article does not provide experimental validation of the proposed equivalences, which may limit its practical impact.
Expert Commentary
The article provides a significant contribution to the field of natural language processing by offering a unified memory-centric interpretation of Transformers. The proposed equivalences between Transformers and sDNCs provide a principled computational framework for understanding and analyzing modern large language models. The results have implications for the design of more efficient and effective neural architectures, as well as for the regulation of large language models. However, the article's findings may be limited to specific types of Transformers and may not generalize to all architectures. Additionally, the lack of experimental validation of the proposed equivalences may limit the article's practical impact. Nevertheless, the article's results are a significant step forward in our understanding of large language models and have the potential to inform the development of more effective neural architectures and more effective regulations.
Recommendations
- ✓ Further experimental validation of the proposed equivalences is necessary to fully establish the article's findings.
- ✓ The article's results should be extended to other types of neural architectures to determine their equivalence to sDNCs.
Sources
Original: arXiv - cs.AI
