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Paper Citation Record · LEDGER

Learning immune receptor representations with protein language models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2402.03823.

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

pith.paper-citation-record.v1
2402.03823 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:54:44.086319Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T09:12:39.605853Z

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 ed245efd-4a90-427e-a06e-2b6a64f0770b · inbound

Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only data cites this paper.

Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only data Learning immune receptor representations with protein language models

Reference 81

Resolution
unresolved
no resolver link, observed 2026-08-06T21:54:44.086319Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:54:44.086319Z digest=sha256:83a632a61dd0a5072b80cf76692df61f69db124fb4e6d15b19b32bc870134252

Observation ed17200f-c9a7-4a97-a937-1dfa7fb73081 · inbound

SwiftRepertoire: Few-Shot Immune-Signature Synthesis via Dynamic Kernel Codes cites this paper.

SwiftRepertoire: Few-Shot Immune-Signature Synthesis via Dynamic Kernel Codes Learning immune receptor representations with protein language models

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-16T09:12:39.608447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-16T09:11:18.538499Z digest=sha256:ac6b7115ef3003ef1f6b99c586a02766f917c7575e2b36a925b1befff8a531f6

Observation 5a357cba-2803-41e6-ae6f-aebde38a06db · inbound

DynImmune-BERT: Dynamic Immune Repertoire Modeling with Neural ODE Driven Continuous Transformers cites this paper.

DynImmune-BERT: Dynamic Immune Repertoire Modeling with Neural ODE Driven Continuous Transformers Learning immune receptor representations with protein language models

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-01T18:39:16.407103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T18:39:16.407103Z digest=sha256:3803d5d452a1ef545fb5837db9e98e08f5ed42a9c503b5baf8fa5fe69564535f

Observation 27db0681-4e3d-4b83-8122-ec6654a07b7c · inbound

DynImmune-BERT: Dynamic Immune Repertoire Modeling with Neural ODE Driven Continuous Transformers cites this paper.

DynImmune-BERT: Dynamic Immune Repertoire Modeling with Neural ODE Driven Continuous Transformers Learning immune receptor representations with protein language models

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-04T04:12:44.901480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T04:12:44.901480Z digest=sha256:9ca76985b771bfdff47f475b1d90e019cff2bc4294b85b8bd7d15a9a157c9da5