Paper Citation Record · LEDGER

Transformers trained on proteins can learn to attend to Euclidean distance

As of 10 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2502.01533.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.01533 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T15:05:55.234901Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

34 of 34 outbound references displayed

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verified fuzzy6
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cec9a389-078c-4589-95fc-6368b079f764 · outbound

This paper cites Ballard, Joshua Bambrick, Sebastian W.

Transformers trained on proteins can learn to attend to Euclidean distance Ballard, Joshua Bambrick, Sebastian W

Reference 1

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Observation f0bc3c69-72e2-44f1-86db-83394ca8f515 · outbound

This paper cites Ball, Judith A.

Transformers trained on proteins can learn to attend to Euclidean distance Ball, Judith A

Reference 2

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Observation 77707385-2a00-4677-8238-3fce1784945b · outbound

This paper cites Layer Normalization.

Transformers trained on proteins can learn to attend to Euclidean distance Layer Normalization

Reference 3

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Observation 32010b0a-b786-4f28-8786-e7f332998bdf · outbound

This paper cites an unresolved cited work.

Transformers trained on proteins can learn to attend to Euclidean distance Unresolved cited work

Reference 4

Resolution: verified exact; doi, observed 2026-08-09T15:05:55.397183Z

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Observation a3fc7813-4d07-42d8-af33-220db03e6134 · outbound

This paper cites FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning.

Transformers trained on proteins can learn to attend to Euclidean distance FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

Reference 5

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Observation 91fb7360-09ae-47eb-9425-6e3a6823883f · outbound

This paper cites FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness.

Transformers trained on proteins can learn to attend to Euclidean distance FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness

Reference 6

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Observation e4f067ba-69d1-4a23-90a7-71b31bb0f9b2 · outbound

This paper cites Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology.

Transformers trained on proteins can learn to attend to Euclidean distance Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:05:55.793620Z

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Observation 2548e4bd-dc3c-4a3b-af79-50e3a222369b · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Transformers trained on proteins can learn to attend to Euclidean distance BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 8

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Observation 3bd55c82-7ea1-484e-9523-ee0896205322 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Transformers trained on proteins can learn to attend to Euclidean distance An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 9

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Observation a966298a-58c4-48b8-b3b6-721fb59f79f2 · outbound

This paper cites SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks.

Transformers trained on proteins can learn to attend to Euclidean distance SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks

Reference 10

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Observation db5b0c8c-5d44-4203-bf1e-e3b896682c62 · outbound

This paper cites Douglas Renfrew, Tomasz Kosciolek, Julia Koehler Leman, Daniel Berenberg, Tommi Vatanen, Chris Chandler, Bryn C.

Transformers trained on proteins can learn to attend to Euclidean distance Douglas Renfrew, Tomasz Kosciolek, Julia Koehler Leman, Daniel Berenberg, Tommi Vatanen, Chris Chandler, Bryn C

Reference 11

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Observation 10e9ed16-5565-455f-b40e-9cee022977fb · outbound

This paper cites Generating Sequences With Recurrent Neural Networks.

Transformers trained on proteins can learn to attend to Euclidean distance Generating Sequences With Recurrent Neural Networks

Reference 12

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Observation 9830d1bd-810c-458d-82c3-5ab4885cdf06 · outbound

This paper cites Sofroniew, Deniz Oktay, Zeming Lin, Robert Verkuil, Vincent Q.

Transformers trained on proteins can learn to attend to Euclidean distance Sofroniew, Deniz Oktay, Zeming Lin, Robert Verkuil, Vincent Q

Reference 13

Resolution: verified exact; doi, observed 2026-08-09T15:05:55.776360Z

Source-reported events for the cited work

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Observation aa32a15f-dad2-4856-9362-40e95434b5a8 · outbound

This paper cites Gaussian Error Linear Units (GELUs).

Transformers trained on proteins can learn to attend to Euclidean distance Gaussian Error Linear Units (GELUs)

Reference 14

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Observation 872a2033-dada-499a-b921-1a0d960ed064 · outbound

This paper cites Ho and Robert Brasseur.

Transformers trained on proteins can learn to attend to Euclidean distance Ho and Robert Brasseur

Reference 15

Resolution: verified exact; doi, observed 2026-08-09T15:05:55.368921Z

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Observation 9ae44dc8-e85c-4361-a491-a3b758aa9ebf · outbound

This paper cites Learning inverse folding from millions of predicted structures.

Transformers trained on proteins can learn to attend to Euclidean distance Learning inverse folding from millions of predicted structures

Reference 16

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Observation 263c5809-73cf-445b-904f-9d5a2bf3b3c9 · outbound

This paper cites Generative Models for Graph - Based Protein Design.

Transformers trained on proteins can learn to attend to Euclidean distance Generative Models for Graph - Based Protein Design

Reference 17

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Observation b26bf774-8690-4479-918b-eb035a78ff26 · outbound

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Transformers trained on proteins can learn to attend to Euclidean distance Unresolved cited work

Reference 18

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Observation ccc431c4-611f-4b26-bbf1-28a86311959a · outbound

This paper cites Kipf and Max Welling.

Transformers trained on proteins can learn to attend to Euclidean distance Kipf and Max Welling

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:05:55.728590Z

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Observation 0eec96d7-4b54-4c15-93d9-8726c1380527 · outbound

This paper cites DeepGO : predicting protein functions from sequence and interactions using a deep ontology-aware classifier.

Transformers trained on proteins can learn to attend to Euclidean distance DeepGO : predicting protein functions from sequence and interactions using a deep ontology-aware classifier

Reference 20

Resolution: verified exact; doi, observed 2026-08-09T15:05:55.342665Z

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Observation 965efc6a-3351-46d9-bbef-153d8799ad0a · outbound

This paper cites Guzmán-Vega, Paula Duek Roggli, Lydie Lane, Stefan T.

Transformers trained on proteins can learn to attend to Euclidean distance Guzmán-Vega, Paula Duek Roggli, Lydie Lane, Stefan T

Reference 21

Resolution: verified exact; doi, observed 2026-08-09T15:05:55.326349Z

Source-reported events for the cited work

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Observation 27cee5cb-fa3b-4b15-8467-275224534646 · outbound

This paper cites Convolutional networks for images, speech, and time series.

Transformers trained on proteins can learn to attend to Euclidean distance Convolutional networks for images, speech, and time series

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:05:55.711796Z

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Observation a4f1a797-d5c9-48ee-b8b6-b299be8715d4 · outbound

This paper cites ProSST : Protein Language Modeling with Quantized Structure and Disentangled Attention , May 2024.

Transformers trained on proteins can learn to attend to Euclidean distance ProSST : Protein Language Modeling with Quantized Structure and Disentangled Attention , May 2024

Reference 23

Resolution: verified exact; doi, observed 2026-08-09T15:05:55.310388Z

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Observation 7814665c-8ed0-43d9-9bad-60fd8b5da0b2 · outbound

This paper cites Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs.

Transformers trained on proteins can learn to attend to Euclidean distance Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs

Reference 24

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Observation 6562c4b4-1143-40b7-9749-8fdcd0bec829 · outbound

This paper cites EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations.

Transformers trained on proteins can learn to attend to Euclidean distance EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 25

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Observation b3a080ea-8f3b-4d4d-9581-7a4b40a43ab8 · outbound

This paper cites Evolutionary-scale prediction of atomic-level protein structure with a language model.

Transformers trained on proteins can learn to attend to Euclidean distance Evolutionary-scale prediction of atomic-level protein structure with a language model

Reference 26

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Observation 58add12b-9683-4974-ab52-ecc2142bde1b · outbound

This paper cites Lawrence Zitnick, Jerry Ma, and Rob Fergus.

Transformers trained on proteins can learn to attend to Euclidean distance Lawrence Zitnick, Jerry Ma, and Rob Fergus

Reference 27

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Observation 1f17ad80-5d21-4fdf-ad27-ba8f7ed52eca · outbound

This paper cites GLU Variants Improve Transformer.

Transformers trained on proteins can learn to attend to Euclidean distance GLU Variants Improve Transformer

Reference 28

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Observation 0d21a303-f9bf-4234-a5f0-07c1a25a7a8d · outbound

This paper cites MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets.

Transformers trained on proteins can learn to attend to Euclidean distance MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets

Reference 29

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Source-reported events for the cited work

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Observation ed616e3d-94ec-4189-a9bc-762ee444f5e5 · outbound

This paper cites RoFormer: Enhanced Transformer with Rotary Position Embedding.

Transformers trained on proteins can learn to attend to Euclidean distance RoFormer: Enhanced Transformer with Rotary Position Embedding

Reference 30

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Observation 640d44c7-c2ae-4a90-87f5-a6a9160c8d85 · outbound

This paper cites Attention Is All You Need.

Transformers trained on proteins can learn to attend to Euclidean distance Attention Is All You Need

Reference 31

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Observation 78c0bb2d-d397-4cfa-af87-53579859fd73 · outbound

This paper cites Varshney, Caiming Xiong, Richard Socher, and Nazneen Rajani.

Transformers trained on proteins can learn to attend to Euclidean distance Varshney, Caiming Xiong, Richard Socher, and Nazneen Rajani

Reference 32

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Source-reported events for the cited work

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Observation b1313a27-11ca-4b3f-a923-a4b492230ca7 · outbound

This paper cites On Layer Normalization in the Transformer Architecture.

Transformers trained on proteins can learn to attend to Euclidean distance On Layer Normalization in the Transformer Architecture

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:05:55.677795Z

Source-reported events for the cited work

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Observation f9e007c0-24f2-406a-8ad5-5430fdee0e63 · outbound

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Transformers trained on proteins can learn to attend to Euclidean distance write newline

Reference 34

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Pith citing papers

No inbound Pith citation observations are available.