Paper Citation Record · LEDGER

RITA: a Study on Scaling Up Generative Protein Sequence Models

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2205.05789.

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

pith.paper-citation-record.v1
2205.05789 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:03:20.010102Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T03:04:14.468656Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 12bce19d-bbdf-4b27-9792-c44b6e59aa3c · inbound

Computational Protein Science in the Era of Large Language Models (LLMs) cites this paper.

Computational Protein Science in the Era of Large Language Models (LLMs) RITA: a Study on Scaling Up Generative Protein Sequence Models

Reference 104

Resolution: unresolved; no resolver link, observed 2026-08-10T19:19:27.942077Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0626e956-7ee8-448c-9970-274d184071c9 · inbound

A Comprehensive Review of Protein Language Models cites this paper.

A Comprehensive Review of Protein Language Models RITA: a Study on Scaling Up Generative Protein Sequence Models

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-08T18:43:34.226380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3e33fa6-9367-45c2-84f6-e3d14e7d3a57 · inbound

PDFBench: A Benchmark for De novo Protein Design from Function cites this paper.

PDFBench: A Benchmark for De novo Protein Design from Function RITA: a Study on Scaling Up Generative Protein Sequence Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T14:26:42.746112Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c2981e9-6adf-472c-b832-c595c46dd1b1 · inbound

Leveraging Natural Language Processing to Unravel the Mystery of Life: A Review of NLP Approaches in Genomics, Transcriptomics, and Proteomics cites this paper.

Leveraging Natural Language Processing to Unravel the Mystery of Life: A Review of NLP Approaches in Genomics, Transcriptomics, and Proteomics RITA: a Study on Scaling Up Generative Protein Sequence Models

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-07T11:31:19.279026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 116d1e8b-5670-4d3e-96b5-cef674791020 · inbound

Scaling and Data Saturation in Protein Language Models cites this paper.

Scaling and Data Saturation in Protein Language Models RITA: a Study on Scaling Up Generative Protein Sequence Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T12:00:27.926946Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d19832c5-5590-4bb8-bdde-e17b655d7791 · inbound

Directed Evolution of Proteins via Bayesian Optimization in Embedding Space cites this paper.

Directed Evolution of Proteins via Bayesian Optimization in Embedding Space RITA: a Study on Scaling Up Generative Protein Sequence Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-05T05:45:30.524078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 283d477e-c69c-4004-94c3-a1f4dcd4e6b7 · inbound

ProteinOPD: Towards Effective and Efficient Preference Alignment for Protein Design cites this paper.

ProteinOPD: Towards Effective and Efficient Preference Alignment for Protein Design RITA: a Study on Scaling Up Generative Protein Sequence Models

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:36:20.095858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ddb30031-7698-415e-a77c-2b02a01d53ed · inbound

Two-Stage Fine-Tuning for Protein Sequence Generation with Targeted Amino-Acid Composition cites this paper.

Two-Stage Fine-Tuning for Protein Sequence Generation with Targeted Amino-Acid Composition RITA: a Study on Scaling Up Generative Protein Sequence Models

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-29T17:23:45.487640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 13d7d16a-8a52-4aeb-887e-acbf2ea573c4 · inbound

Modeling Protein Evolution with Generative Models: from Extant Sequence Data to Evolutionary Dynamics cites this paper.

Modeling Protein Evolution with Generative Models: from Extant Sequence Data to Evolutionary Dynamics RITA: a Study on Scaling Up Generative Protein Sequence Models

Reference 77

Resolution: verified exact; arxiv_id, observed 2026-06-30T03:04:14.471806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 14022e96-ef42-4364-9822-04b7a949a9cb · inbound

Genotypic Triggers: Exposing Pharmacogenomic Blind Spots via Host-Specific Backdoors in Generative Antimicrobial Peptide Models cites this paper.

Genotypic Triggers: Exposing Pharmacogenomic Blind Spots via Host-Specific Backdoors in Generative Antimicrobial Peptide Models RITA: a Study on Scaling Up Generative Protein Sequence Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T21:03:20.010102Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.