Algebraic Positional Encodings
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Abstract (arXiv)
We introduce a novel positional encoding strategy for Transformer-style models, addressing the shortcomings of existing, often ad hoc, approaches. Our framework provides a flexible mapping from the algebraic specification of a domain to an interpretation as orthogonal operators. This design preserves the algebraic characteristics of the source domain, ensuring that the model upholds its desired structural properties. Our scheme can accommodate various structures, ncluding sequences, grids and trees, as well as their compositions. We conduct a series of experiments to demonstrate the practical applicability of our approach. Results suggest performance on par with or surpassing the current state-of-the-art, without hyper-parameter optimizations or "task search" of any kind. Code is available at https://github.com/konstantinosKokos/ape.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 19 of its 41 references that are in the map. First posted on arXiv 26 Dec 2023.
Builds on
19 of 41 references.
References · 19
- 2017Attention Is All You Needcited by 60,361
- 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,703
- 2020Longformer: The Long-Document Transformercited by 3,413
- 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,918
- 2020Transformers are RNNs: Fast Autoregressive Transformers with Linear Attentioncited by 2,022
- 2018A Call for Clarity in Reporting BLEU Scorescited by 1,799
- 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,475
- 2016Group Equivariant Convolutional Networkscited by 1,165
- 2018Self-Attention with Relative Position Representationscited by 1,120
- 2015Gated Graph Sequence Neural Networkscited by 796
- 2017Convolutional Sequence to Sequence Learningcited by 552
- 2015Unitary Evolution Recurrent Neural Networkscited by 294
7 more
- 2021Escaping the Big Data Paradigm with Compact Transformerscited by 264
- 2020Rethinking Positional Encoding in Language Pre-trainingcited by 197
- 2021SOFT: Softmax-free Transformer with Linear Complexitycited by 128
- 2020Fast Transformers with Clustered Attentioncited by 123
- 2021Position Information in Transformers: An Overviewcited by 103
- 2019Encoding word order in complex embeddingscited by 53
- 2020DA-Transformer: Distance-aware Transformercited by 23
Built on by
Citing papers per year: 2024: 1; 2025: 4; 2026: 5.
10 papers.
- 2025PaTH Attention: Position Encoding via Accumulating Householder Transformationscited by 34
- 2025Group Representational Position Encodingcited by 5
- 2024Learning Structure-Aware Representations of Dependent Typescited by 2
- 2025Positional Encoding meets Persistent Homology on Graphscited by 1
- 2025Tree-Based Deep Learning for Ranking Symbolic Integration Algorithmscited by 1
- 2026Jordan-RoPE: Non-Semisimple Relative Positional Encoding via Complex Jordan Blockscited by 1
- 2026What are the Right Symmetries for Formal Theorem Proving?cited by 1
- 2026PJ-RoPE: A Fourier-Jet-Affine Position Space for Relative Attentioncited by 0
- 2026GSF-chi: Global Stereochemical Fields for Chiral Graph Transformerscited by 0
- 2026SO(3)-RoPE for Spherical Transformerscited by 0
- 2026SO(3)-RoPE for Spherical Transformers5 Oct 2026
- 2026GSF-chi: Global Stereochemical Fields for Chiral Graph Transformers11 Sep 2026
- 2026PJ-RoPE: A Fourier-Jet-Affine Position Space for Relative Attention3 Jun 2026
- 2026What are the Right Symmetries for Formal Theorem Proving?21 May 2026
- 2026Jordan-RoPE: Non-Semisimple Relative Positional Encoding via Complex Jordan Blocks5 May 2026
- 2025Group Representational Position Encoding8 Dec 2025
- 2025Tree-Based Deep Learning for Ranking Symbolic Integration Algorithms8 Aug 2025
- 2025Positional Encoding meets Persistent Homology on Graphs6 Jun 2025
- 2025PaTH Attention: Position Encoding via Accumulating Householder Transformations22 May 2025
- 2024Learning Structure-Aware Representations of Dependent Types3 Feb 2024