Paper Citation Record · LEDGER

A Variational Perspective on Generative Protein Fitness Optimization

As of 17 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2501.19200.

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

pith.paper-citation-record.v1
2501.19200 v2

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T21:02:47.505888Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

44 of 44 outbound references displayed

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verified fuzzy14
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation adbaa34e-2632-48b3-b7ab-199ca5ae33ec · outbound

This paper cites write newline.

A Variational Perspective on Generative Protein Fitness Optimization write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-09T21:02:47.310856Z

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Observation aca9b8f2-108a-4124-aa45-6e5e60e34639 · outbound

This paper cites D-Flow: Differentiating through Flows for Controlled Generation.

A Variational Perspective on Generative Protein Fitness Optimization D-Flow: Differentiating through Flows for Controlled Generation

Reference 2

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Observation 90b8c3a2-1df5-432c-bc0d-18942f4e95dc · outbound

This paper cites Conditioning by adaptive sampling for robust design.

A Variational Perspective on Generative Protein Fitness Optimization Conditioning by adaptive sampling for robust design

Reference 3

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This paper cites H., Bashir, A., Sinai, S., Jain, N.

A Variational Perspective on Generative Protein Fitness Optimization H., Bashir, A., Sinai, S., Jain, N

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:02:48.093859Z

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A Variational Perspective on Generative Protein Fitness Optimization Unresolved cited work

Reference 5

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Observation 4adfe810-5da6-440e-a3c4-08f7b353f042 · outbound

This paper cites T., Klasky, M.

A Variational Perspective on Generative Protein Fitness Optimization T., Klasky, M

Reference 6

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Observation 61bdcae5-7939-4737-816f-2af3f24d8397 · outbound

This paper cites E., Wittmann, B.

A Variational Perspective on Generative Protein Fitness Optimization E., Wittmann, B

Reference 7

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Observation 0f6b3a56-dc3b-43e4-b936-64db66d5995c · outbound

This paper cites and Nichol, A.

A Variational Perspective on Generative Protein Fitness Optimization and Nichol, A

Reference 8

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A Variational Perspective on Generative Protein Fitness Optimization Unresolved cited work

Reference 9

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Observation 5906f17c-c876-4ec0-836d-537fd10c9c71 · outbound

This paper cites Scaling Rectified Flow Transformers for High-Resolution Image Synthesis.

A Variational Perspective on Generative Protein Fitness Optimization Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

Reference 10

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Observation 3c403a9c-d23a-4423-a276-18a45c6a58d8 · outbound

This paper cites Protein Discovery with Discrete Walk-Jump Sampling.

A Variational Perspective on Generative Protein Fitness Optimization Protein Discovery with Discrete Walk-Jump Sampling

Reference 11

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Observation f25971b2-98a9-49ca-b50b-2b349a886a28 · outbound

This paper cites Oops i took a gradient: Scalable sampling for discrete distributions.

A Variational Perspective on Generative Protein Fitness Optimization Oops i took a gradient: Scalable sampling for discrete distributions

Reference 12

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Observation d9ee609a-9617-4cad-8abf-e2455e1a4e01 · outbound

This paper cites G., Hotzel, I., Lafrance-Vanasse, J., Rajpal, A., Cho, K., and Wilson, A.

A Variational Perspective on Generative Protein Fitness Optimization G., Hotzel, I., Lafrance-Vanasse, J., Rajpal, A., Cho, K., and Wilson, A

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:02:48.014009Z

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Observation df3d86b7-6e45-472c-a7d6-2f2de7b1b453 · outbound

This paper cites D., Blacklow, S.

A Variational Perspective on Generative Protein Fitness Optimization D., Blacklow, S

Reference 14

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Observation 00e54667-27d5-4e21-8028-af12c399c851 · outbound

This paper cites P., Glorot, X., Botvinick, M.

A Variational Perspective on Generative Protein Fitness Optimization P., Glorot, X., Botvinick, M

Reference 15

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Observation 2a14a33a-422c-4480-96bd-a85051450a83 · outbound

This paper cites Denoising diffusion probabilistic models.

A Variational Perspective on Generative Protein Fitness Optimization Denoising diffusion probabilistic models

Reference 16

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A Variational Perspective on Generative Protein Fitness Optimization Unresolved cited work

Reference 17

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Observation ca61b0e0-b138-4115-b8f7-4d4e8cbd23ff · outbound

This paper cites F., Ekbote, C.

A Variational Perspective on Generative Protein Fitness Optimization F., Ekbote, C

Reference 18

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Observation 863cad44-b59a-4809-9cbc-00f318e81382 · outbound

This paper cites R., Fu, X., Viknander, S., Goldin, C., Monaco, S., Zelezniak, A., and Yang, K.

A Variational Perspective on Generative Protein Fitness Optimization R., Fu, X., Viknander, S., Goldin, C., Monaco, S., Zelezniak, A., and Yang, K

Reference 19

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Observation aae26500-3bc0-4039-a490-5c2ec8dca6a1 · outbound

This paper cites E., Fannjiang, C., Wittmann, B.

A Variational Perspective on Generative Protein Fitness Optimization E., Fannjiang, C., Wittmann, B

Reference 20

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Observation 3acceb77-2c83-4f40-ae0d-8d91423ce536 · outbound

This paper cites Auto-Encoding Variational Bayes.

A Variational Perspective on Generative Protein Fitness Optimization Auto-Encoding Variational Bayes

Reference 21

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This paper cites S., Barzilay, R., and Fiete, I.

A Variational Perspective on Generative Protein Fitness Optimization S., Barzilay, R., and Fiete, I

Reference 22

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Observation 0af4ea4e-1d9e-46b3-a744-8403257f9130 · outbound

This paper cites Robust Optimization in Protein Fitness Landscapes Using Reinforcement Learning in Latent Space.

A Variational Perspective on Generative Protein Fitness Optimization Robust Optimization in Protein Fitness Landscapes Using Reinforcement Learning in Latent Space

Reference 23

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Observation 884aee2f-679d-4795-88fc-dde9525e6e34 · outbound

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

A Variational Perspective on Generative Protein Fitness Optimization Evolutionary-scale prediction of atomic-level protein structure with a language model

Reference 24

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A Variational Perspective on Generative Protein Fitness Optimization Unresolved cited work

Reference 25

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Observation 18818d6b-c699-4941-aed4-03b64f584695 · outbound

This paper cites Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow.

A Variational Perspective on Generative Protein Fitness Optimization Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 26

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This paper cites N., Marks, D., and Gal, Y.

A Variational Perspective on Generative Protein Fitness Optimization N., Marks, D., and Gal, Y

Reference 27

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Observation 6297abdd-9168-45c0-88cc-a3ed9753d09d · outbound

This paper cites Proteingym: Large-scale benchmarks for protein fitness prediction and design.

A Variational Perspective on Generative Protein Fitness Optimization Proteingym: Large-scale benchmarks for protein fitness prediction and design

Reference 28

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A Variational Perspective on Generative Protein Fitness Optimization Unresolved cited work

Reference 29

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Observation bd7a1446-6d51-441d-8f72-e7cc2347da9e · outbound

This paper cites Proximal exploration for model-guided protein sequence design.

A Variational Perspective on Generative Protein Fitness Optimization Proximal exploration for model-guided protein sequence design

Reference 30

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Observation 7c8bb0eb-9e5e-4897-9716-8202280bfd89 · outbound

This paper cites Taming VAEs.

A Variational Perspective on Generative Protein Fitness Optimization Taming VAEs

Reference 31

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A Variational Perspective on Generative Protein Fitness Optimization Unresolved cited work

Reference 32

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Observation f9dbf80b-e518-4e55-ad47-f1edd76e139a · outbound

This paper cites S., Bolotin, D.

A Variational Perspective on Generative Protein Fitness Optimization S., Bolotin, D

Reference 33

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Observation bb7f4f21-d2c9-439f-bb1d-078ea8528b6d · outbound

This paper cites A primer on model-guided exploration of fitness landscapes for biological sequence design.

A Variational Perspective on Generative Protein Fitness Optimization A primer on model-guided exploration of fitness landscapes for biological sequence design

Reference 34

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Observation 1abddc36-be35-4b37-9b98-6a4090d0e501 · outbound

This paper cites AdaLead: A simple and robust adaptive greedy search algorithm for sequence design.

A Variational Perspective on Generative Protein Fitness Optimization AdaLead: A simple and robust adaptive greedy search algorithm for sequence design

Reference 35

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This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

A Variational Perspective on Generative Protein Fitness Optimization Deep unsupervised learning using nonequilibrium thermodynamics

Reference 36

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This paper cites P., Kumar, A., Ermon, S., and Poole, B.

A Variational Perspective on Generative Protein Fitness Optimization P., Kumar, A., Ermon, S., and Poole, B

Reference 37

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Observation 38b4ce69-466e-411e-aefe-b6b70c38466e · outbound

This paper cites Closed-Form Test Functions for Biophysical Sequence Optimization Algorithms.

A Variational Perspective on Generative Protein Fitness Optimization Closed-Form Test Functions for Biophysical Sequence Optimization Algorithms

Reference 38

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This paper cites Conservative objective models for effective offline model-based optimization.

A Variational Perspective on Generative Protein Fitness Optimization Conservative objective models for effective offline model-based optimization

Reference 39

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Observation 7c74b390-03f6-47cb-8f02-0fd82f60d168 · outbound

This paper cites V., Ngo, N.

A Variational Perspective on Generative Protein Fitness Optimization V., Ngo, N

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:02:47.765621Z

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Observation 996a6524-f0a1-4d45-a905-574391f8f8c4 · outbound

This paper cites and Weissman, D.

A Variational Perspective on Generative Protein Fitness Optimization and Weissman, D

Reference 41

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Observation e5f9ed93-8c83-4216-8540-ce4fcb736e43 · outbound

This paper cites The reparameterization trick for acquisition functions.

A Variational Perspective on Generative Protein Fitness Optimization The reparameterization trick for acquisition functions

Reference 42

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Observation 63c77cf7-4360-4a12-9276-ac560b21f7d1 · outbound

This paper cites G., Bowden, J.

A Variational Perspective on Generative Protein Fitness Optimization G., Bowden, J

Reference 43

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Observation eba00146-83b1-4552-bfcf-5462eb577bc8 · outbound

This paper cites Guided Flows for Generative Modeling and Decision Making.

A Variational Perspective on Generative Protein Fitness Optimization Guided Flows for Generative Modeling and Decision Making

Reference 44

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Pith citing papers

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