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Paper Citation Record · LEDGER

Fast Decoding in Sequence Models using Discrete Latent Variables

As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1803.03382.

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

pith.paper-citation-record.v1
1803.03382 v6

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T05:20:45.992697Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-25T18:21:07.087750Z

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 4842a71c-1254-44c7-b81d-ed19134935dc · inbound

Retrieving Sequential Information for Non-Autoregressive Neural Machine Translation cites this paper.

Retrieving Sequential Information for Non-Autoregressive Neural Machine Translation Fast Decoding in Sequence Models using Discrete Latent Variables

Reference 12

Resolution
verified exact
local_arxiv, observed 2026-05-25T18:21:07.090610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-25T18:18:52.897601Z digest=sha256:4c17b79e586d919974e5dc235719783e9fc68a1c16dca99f4a6ba7ac90acd62c

Observation 13b64532-285a-4fb1-9ad8-640817aad0b9 · inbound

Sequence Generation: From Both Sides to the Middle cites this paper.

Sequence Generation: From Both Sides to the Middle Fast Decoding in Sequence Models using Discrete Latent Variables

Reference 6

Resolution
verified exact
local_arxiv, observed 2026-05-25T17:46:05.914300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-05-25T17:45:07.556389Z digest=sha256:fde8c61eaad7d30d226e8c1d1bd90bbb34645dbdfadb655964ff74f8313eb6a7

Observation 201588f8-69d1-49a4-a375-c79214ca7743 · inbound

CTRL: A Conditional Transformer Language Model for Controllable Generation cites this paper.

CTRL: A Conditional Transformer Language Model for Controllable Generation Fast Decoding in Sequence Models using Discrete Latent Variables

Reference 21

Resolution
verified exact
local_arxiv, observed 2026-05-17T06:14:02.588954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-05-17T06:14:02.423030Z digest=sha256:4b8e5b892836ed73e56dffb100653f61e6d5b7f9deba7fbcad7293487a3a30f3

Observation 69b18d8d-600b-4032-a1b6-b758410bd83e · inbound

Optical Context Compression Is Just (Bad) Autoencoding cites this paper.

Optical Context Compression Is Just (Bad) Autoencoding Fast Decoding in Sequence Models using Discrete Latent Variables

Reference 6

Resolution
verified exact
local_arxiv, observed 2026-05-17T02:58:55.161013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-05-17T02:55:06.012687Z digest=sha256:020db8836bf93ae335c655e4bd3f65faab19951eade020c209c66a49ace478a5

Observation e828579d-330d-4e10-b04e-f77c29c36578 · inbound

Vector Quantized Latent Concepts: A Scalable Alternative to Clustering-Based Concept Discovery cites this paper.

Vector Quantized Latent Concepts: A Scalable Alternative to Clustering-Based Concept Discovery Fast Decoding in Sequence Models using Discrete Latent Variables

Reference 692

Resolution
malformed identifier
no resolver link, observed 2026-08-03T05:20:45.992697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T05:20:45.992697Z digest=sha256:2c79ace3d73a281d2eb149be28e35c8adf673c1909758f480b324e870963680f