Paper Citation Record · LEDGER

Towards Understanding the Shape of Representations in Protein Language Models

As of 14 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2509.24895.

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

pith.paper-citation-record.v1
2509.24895 v2

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T13:54:25.530278Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

32 of 32 outbound references displayed

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unresolved32
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External citation measurements

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Outbound references

Observation 445ffc73-23b5-40cc-bcc9-fd4b692196c1 · outbound

This paper cites write newline.

Towards Understanding the Shape of Representations in Protein Language Models write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:17.929199Z

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Observation 59c74228-0b89-4fd2-b3b8-e48aae1397a2 · outbound

This paper cites A robust tangent pca via shape restoration for shape variability analysis.

Towards Understanding the Shape of Representations in Protein Language Models A robust tangent pca via shape restoration for shape variability analysis

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:18.446307Z

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Observation 8a167dd9-1f8e-435e-bfa9-174b65e0dede · outbound

This paper cites Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning.

Towards Understanding the Shape of Representations in Protein Language Models Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:18.668820Z

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Observation 977b848a-65c2-4034-9975-4f9cd43fa4bf · outbound

This paper cites The gene ontology knowledgebase in 2023.

Towards Understanding the Shape of Representations in Protein Language Models The gene ontology knowledgebase in 2023

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:18.812771Z

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Observation 797bf3d9-3a45-4e8f-a9cd-3f660661f79b · outbound

This paper cites Accurate prediction of protein structures and interactions using a three-track neural network.

Towards Understanding the Shape of Representations in Protein Language Models Accurate prediction of protein structures and interactions using a three-track neural network

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:18.931913Z

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Observation 4b4dac0c-45db-4639-aec6-a8ea937c7092 · outbound

This paper cites Peptide binder design with inverse folding and protein structure prediction.

Towards Understanding the Shape of Representations in Protein Language Models Peptide binder design with inverse folding and protein structure prediction

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:19.070249Z

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Observation 9fc21195-ff89-401a-b5a6-568f2c5577df · outbound

This paper cites Scope: improvements to the structural classification of proteins--extended database to facilitate variant interpretation and machine learning.

Towards Understanding the Shape of Representations in Protein Language Models Scope: improvements to the structural classification of proteins--extended database to facilitate variant interpretation and machine learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:19.206280Z

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Observation 14c22965-47c8-4ab1-ac0b-b747e82df11c · outbound

This paper cites Target sequence-conditioned design of peptide binders using masked language modeling.

Towards Understanding the Shape of Representations in Protein Language Models Target sequence-conditioned design of peptide binders using masked language modeling

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:19.444736Z

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Observation 4492eaa8-6a79-410d-93dc-e4a22b0732ae · outbound

This paper cites Emergence of a High-Dimensional Abstraction Phase in Language Transformers.

Towards Understanding the Shape of Representations in Protein Language Models Emergence of a High-Dimensional Abstraction Phase in Language Transformers

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:19.554129Z

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Observation b6147944-9eb4-479c-9769-ce03f0f84843 · outbound

This paper cites Computational Topology : An Introduction.

Towards Understanding the Shape of Representations in Protein Language Models Computational Topology : An Introduction

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:19.613121Z

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Observation f8e1b650-6fb1-4094-83b7-ab1d4fdb938e · outbound

This paper cites Controllable protein design with language models.

Towards Understanding the Shape of Representations in Protein Language Models Controllable protein design with language models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:19.690776Z

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Observation 65d61faa-b1c1-4a62-868b-bd93fd0be771 · outbound

This paper cites Se (3)-transformers: 3d roto-translation equivariant attention networks.

Towards Understanding the Shape of Representations in Protein Language Models Se (3)-transformers: 3d roto-translation equivariant attention networks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:19.797120Z

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Observation 1c3e570c-80d8-4417-ad55-75d15b8ba7b0 · outbound

This paper cites Sparse autoencoders uncover biologically interpretable features in protein language model representations.

Towards Understanding the Shape of Representations in Protein Language Models Sparse autoencoders uncover biologically interpretable features in protein language model representations

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:19.867247Z

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Observation 479efcf7-a51a-4558-ab46-f88f9212e440 · outbound

This paper cites Simulating 500 million years of evolution with a language model.

Towards Understanding the Shape of Representations in Protein Language Models Simulating 500 million years of evolution with a language model

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:19.924867Z

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Observation ef6b0633-578b-40a7-8fb8-80cbb1fffcce · outbound

This paper cites Graph filtration learning.

Towards Understanding the Shape of Representations in Protein Language Models Graph filtration learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:20.377308Z

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Observation a7c4ac83-dc8a-4540-9740-cea82d52d9d1 · outbound

This paper cites Highly accurate protein structure prediction with alphafold.

Towards Understanding the Shape of Representations in Protein Language Models Highly accurate protein structure prediction with alphafold

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:20.530649Z

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Observation 1e0b3a4e-8597-4616-9842-51e3d12e0e31 · outbound

This paper cites Measuring the Intrinsic Dimension of Objective Landscapes.

Towards Understanding the Shape of Representations in Protein Language Models Measuring the Intrinsic Dimension of Objective Landscapes

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:20.776295Z

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Observation 862a221d-9af6-469a-9a3f-92067a0ac81a · outbound

This paper cites Fatcat 2.0: towards a better understanding of the structural diversity of proteins.

Towards Understanding the Shape of Representations in Protein Language Models Fatcat 2.0: towards a better understanding of the structural diversity of proteins

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:21.008490Z

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Observation e537bff6-3af1-4723-b41d-7058fb630ad9 · outbound

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

Towards Understanding the Shape of Representations in Protein Language Models Evolutionary-scale prediction of atomic-level protein structure with a language model

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:21.311936Z

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Observation 853a747c-322e-4efe-bab7-d20483ec8cb3 · outbound

This paper cites Protein structure alignment using elastic shape analysis.

Towards Understanding the Shape of Representations in Protein Language Models Protein structure alignment using elastic shape analysis

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:21.804747Z

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Observation c9b69bf7-beb3-4b84-9a40-fabc5bc1d069 · outbound

This paper cites Variational autoencoder for design of synthetic viral vector serotypes.

Towards Understanding the Shape of Representations in Protein Language Models Variational autoencoder for design of synthetic viral vector serotypes

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:22.110498Z

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Observation 98c56080-a376-42ba-aa98-f9dc31ac8ad8 · outbound

This paper cites Large language models generate functional protein sequences across diverse families.

Towards Understanding the Shape of Representations in Protein Language Models Large language models generate functional protein sequences across diverse families

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:22.669847Z

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Observation 8f289cbc-b34d-4177-ac07-b3a2b5053652 · outbound

This paper cites Exploring the Impact of a Transformer's Latent Space Geometry on Downstream Task Performance.

Towards Understanding the Shape of Representations in Protein Language Models Exploring the Impact of a Transformer's Latent Space Geometry on Downstream Task Performance

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:23.184801Z

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Observation 28c5cf6d-0cc5-489d-bb1a-08950aae8903 · outbound

This paper cites Geomstats: A python package for riemannian geometry in machine learning.

Towards Understanding the Shape of Representations in Protein Language Models Geomstats: A python package for riemannian geometry in machine learning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:23.314746Z

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Observation c84b1131-6857-490b-abf8-5c2ccf325990 · outbound

This paper cites Filtration curves for graph representation.

Towards Understanding the Shape of Representations in Protein Language Models Filtration curves for graph representation

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:23.444958Z

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Observation 1c7e0b96-f678-4941-aa42-de4b8b3e8f1b · outbound

This paper cites Less is more: Local intrinsic dimensions of contextual language models.

Towards Understanding the Shape of Representations in Protein Language Models Less is more: Local intrinsic dimensions of contextual language models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:23.620934Z

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Observation afd09e44-85c6-4a52-8248-9eb98b9b9f38 · outbound

This paper cites Interplm: Discovering interpretable features in protein language models via sparse autoencoders.

Towards Understanding the Shape of Representations in Protein Language Models Interplm: Discovering interpretable features in protein language models via sparse autoencoders

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:23.837618Z

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Observation a084771a-bae1-4a0e-9cb0-8fedf7df83ab · outbound

This paper cites Shape analysis of elastic curves in euclidean spaces.

Towards Understanding the Shape of Representations in Protein Language Models Shape analysis of elastic curves in euclidean spaces

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:24.307195Z

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Observation 98cc310c-c4fe-4ad7-916e-707d4b52050c · outbound

This paper cites The geometry of hidden representations of large transformer models.

Towards Understanding the Shape of Representations in Protein Language Models The geometry of hidden representations of large transformer models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:24.694844Z

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Observation 86ed2443-72a4-4027-8e28-af21795a4c51 · outbound

This paper cites Artificial intelligence using a latent diffusion model enables the generation of diverse and potent antimicrobial peptides.

Towards Understanding the Shape of Representations in Protein Language Models Artificial intelligence using a latent diffusion model enables the generation of diverse and potent antimicrobial peptides

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:24.894007Z

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Observation e6f274e0-6d06-40b6-a8cf-6af793a057b1 · outbound

This paper cites Scoring function for automated assessment of protein structure template quality.

Towards Understanding the Shape of Representations in Protein Language Models Scoring function for automated assessment of protein structure template quality

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:25.160057Z

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Observation be4c1aaf-5874-48c0-b4b4-c265cbbb87f5 · outbound

This paper cites Protein language models learn evolutionary statistics of interacting sequence motifs.

Towards Understanding the Shape of Representations in Protein Language Models Protein language models learn evolutionary statistics of interacting sequence motifs

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:25.530278Z

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Pith citing papers

No inbound Pith citation observations are available.