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Paper Citation Record · LEDGER

Neural Machine Translation with Byte-Level Subwords

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:1909.03341.

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

pith.paper-citation-record.v1
1909.03341 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:36:45.595362Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

9
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation d40d4450-762f-48bf-a5ae-09d419db39eb · inbound

XGrammar: Flexible and Efficient Structured Generation Engine for Large Language Models cites this paper.

XGrammar: Flexible and Efficient Structured Generation Engine for Large Language Models Neural Machine Translation with Byte-Level Subwords

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-12T14:36:45.595362Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T14:36:45.595362Z digest=sha256:c24436fd1f2ab93f9225af427cf9fc04018960db4f0d3e2f4ca5bd4d224deb13

Observation 88866d76-be29-488e-93ba-bf00aa16fa2a · inbound

Byte BPE Tokenization as an Inverse string Homomorphism cites this paper.

Byte BPE Tokenization as an Inverse string Homomorphism Neural Machine Translation with Byte-Level Subwords

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-11T22:50:28.678596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T22:50:28.678596Z digest=sha256:c03b7498452f0e80bd65c5a05b601fc1b57ecd7716985a3535ccd3e9de1ba36d

Observation f2150d84-1ce6-4307-9ad2-ae70d62fcbfe · inbound

SnipGen: A Mining Repository Framework for Evaluating LLMs for Code cites this paper.

SnipGen: A Mining Repository Framework for Evaluating LLMs for Code Neural Machine Translation with Byte-Level Subwords

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-08T14:01:12.422294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T14:01:12.422294Z digest=sha256:7d2ed125659ae416a2b8981b3ac63ed0418171e268d63b2a24d05e5e4bb72f84

Observation 59443cc1-b8b8-4b44-9ba0-7fcdf2bb5777 · inbound

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering cites this paper.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Neural Machine Translation with Byte-Level Subwords

Reference 293

Resolution
unresolved
no resolver link, observed 2026-08-07T15:30:09.176820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:30:09.176820Z digest=sha256:99badc3097c118da97d63f2f3f5a04595829ef9c01e14e2676be35520458318c

Observation 44417b48-4317-44c5-bdfd-8b771e69254b · inbound

Bit-level BPE: Below the byte boundary cites this paper.

Bit-level BPE: Below the byte boundary Neural Machine Translation with Byte-Level Subwords

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-07T05:40:47.814325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:40:47.814325Z digest=sha256:2f424629ebba54b93f7d5e279020c14d0c2c39c5c25900b5d77b43d51a6e816f

Observation 4341cc1e-cde5-4471-a04b-606ce150088b · inbound

Is There a Case for Conversation Optimized Tokenizers in Large Language Models? cites this paper.

Is There a Case for Conversation Optimized Tokenizers in Large Language Models? Neural Machine Translation with Byte-Level Subwords

Reference 2019

Resolution
unresolved
no resolver link, observed 2026-08-06T23:19:15.667006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:19:15.667006Z digest=sha256:b29166739a7758e1216c15c6e680844c1dcf3ffbb2e8d4dfa23f7f1123741c5b

Observation bc39910d-1331-47b1-ab4b-53482a79e350 · inbound

CG-MLLM: Captioning and Generating 3D content via Multi-modal Large Language Models cites this paper.

CG-MLLM: Captioning and Generating 3D content via Multi-modal Large Language Models Neural Machine Translation with Byte-Level Subwords

Reference 52

Resolution
verified exact
arxiv_id, observed 2026-05-21T14:50:14.713022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-21T14:48:21.787919Z digest=sha256:2231146b091b6561008aa4c53393d5d0c9618eb6be1e50b506222c27c963cfe5