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Paper Citation Record · LEDGER

Non-Autoregressive Machine Translation with Latent Alignments

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2004.07437.

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

pith.paper-citation-record.v1
2004.07437 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:05:44.947836Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T03:30:22.292481Z

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 879bf282-2052-4b2f-a533-ba2815a9edf9 · inbound

Continuous diffusion for categorical data cites this paper.

Continuous diffusion for categorical data Non-Autoregressive Machine Translation with Latent Alignments

Reference 74

Resolution
verified exact
arxiv_id, observed 2026-05-18T03:30:22.296384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-05-18T03:30:22.025578Z digest=sha256:16c38bc39400c12c4d5b2a5c7f95250ee4e576149ad73dc426afb7cfc6986f58

Observation 23145aa5-4c4b-43d5-bdf5-04b237aa2598 · inbound

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing cites this paper.

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing Non-Autoregressive Machine Translation with Latent Alignments

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-15T20:05:44.947836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T20:05:44.947836Z digest=sha256:65d7ab4a17d5e5369e36a057e1b416e474c228fdb2208931eb32a37f4c25d659