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Paper Citation Record · LEDGER

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits

As of 22 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 1 inbound Pith citation observation for arXiv:2505.10202.

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

pith.paper-citation-record.v1
2505.10202 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:18:24.529671Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:26:51.707959Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-16T12:16:17.039197Z

Reference resolution

30 of 30 outbound references displayed

  • verified exact0
  • verified fuzzy1
  • unresolved29
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 42cc6d94-4609-4d44-af61-e0f1d3a07d91 · outbound

This paper cites online" 'onlinestring :=.

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits online" 'onlinestring :=

Reference 1

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unresolved
no resolver link, observed 2026-08-15T21:18:24.410534Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:18:24.410534Z digest=sha256:ce7088405388cca19b6bb3fe7e198e1af90113ca72050d53b4e6bb370b4b3579

Observation 4eeca735-24d4-420f-aa88-e8839e4f4712 · outbound

This paper cites write newline.

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits write newline

Reference 2

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unresolved
no resolver link, observed 2026-08-15T21:18:24.415315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:18:24.415315Z digest=sha256:5bbbf43142e0896dc3142a53a3d8050cd4a21a3ad75c4a3b9727c55622298765

Observation 5affab7c-02f8-4359-9bdc-4d0389aafd3c · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 3

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no resolver link, observed 2026-08-15T21:18:24.419564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:18:24.419564Z digest=sha256:27c49b90ca9136e89f340e57f0af640685bccedd2e681032f61e79c75374b6b0

Observation e6562826-835e-4420-b3f7-535d633cb34b · outbound

This paper cites Language Models are Few-Shot Learners.

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits Language Models are Few-Shot Learners

Reference 4

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no resolver link, observed 2026-08-15T21:18:24.423831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:18:24.423831Z digest=sha256:f5f0da7c46a81728179c5b7df9257cbcc92ed5f469f1323518ce369ebfa55c5d

Observation b9d12b2d-6095-442b-8cf7-8dba9dd7b590 · outbound

This paper cites an unresolved cited work.

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits Unresolved cited work

Reference 5

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raw_fallback, observed 2026-08-15T21:18:24.955317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-15T21:18:24.428069Z digest=sha256:0997543f4d0d1f39c2e4e2d9a59cb60956b686759479810c7e73703f7ef66279

Observation f3898a5a-ab0e-4e79-b04a-9d1e78c971ce · outbound

This paper cites The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 6

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no resolver link, observed 2026-08-15T21:18:24.431797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:18:24.431797Z digest=sha256:bcc2a9563006ca40a719065194e347245e2e4b5a9d5b8d175121fe85954619e1

Observation c4e643f3-a78c-4f89-a42b-3e4f38c817e1 · outbound

This paper cites an unresolved cited work.

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits Unresolved cited work

Reference 7

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raw_fallback, observed 2026-08-15T21:18:24.942743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-15T21:18:24.436209Z digest=sha256:ab7a0ceed55d81c94ae06d9a2e21225fd15ff2f328ae3d0f63da5496d6f3684b

Observation fd25c665-8c3c-4559-900f-b8fde3c5d6b5 · outbound

This paper cites Learning both Weights and Connections for Efficient Neural Networks.

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits Learning both Weights and Connections for Efficient Neural Networks

Reference 8

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no resolver link, observed 2026-08-15T21:18:24.440824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:18:24.440824Z digest=sha256:4c5e0fc5947bdb9d25aee88307d508194ed19ddb4221a6e9f03eb124d22372c8

Observation 57d39289-d7b9-4761-bb0b-ea05bc7cba95 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits Distilling the Knowledge in a Neural Network

Reference 9

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no resolver link, observed 2026-08-15T21:18:24.444996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:18:24.444996Z digest=sha256:da667591a0b99d35209b58ddbaceda5c6695ac974ad8ef5194bea5799c9a2cb6

Observation 03b2c2cd-262b-4044-88d7-579d2c4c31c7 · outbound

This paper cites Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling.

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling

Reference 10

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unresolved
no resolver link, observed 2026-08-15T21:18:24.448944Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:18:24.448944Z digest=sha256:1876f8d83ea06357f3674e36a700e0b33d0c910adcfe835ae52381f9fa58c634

Observation b2432bd2-474c-41da-98cd-e3de97385433 · outbound

This paper cites Categorical Reparameterization with Gumbel-Softmax.

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits Categorical Reparameterization with Gumbel-Softmax

Reference 11

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no resolver link, observed 2026-08-15T21:18:24.453654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:18:24.453654Z digest=sha256:ab3a70654a8f0339474152087c1012c5942c4b5ba7a693a54a487558d59ccbcc

Observation 82dc74e3-94ca-4ab0-b8dd-4c0868a54a75 · outbound

This paper cites On Using Very Large Target Vocabulary for Neural Machine Translation.

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits On Using Very Large Target Vocabulary for Neural Machine Translation

Reference 12

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unresolved
no resolver link, observed 2026-08-15T21:18:24.457829Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:18:24.457829Z digest=sha256:c0f1ff0a544250aaea6d81e0d22cfabcdb3827b79cdec87371e165591a84e028

Observation b2ffcb63-6199-4ed6-b3eb-95f7d5245635 · outbound

This paper cites an unresolved cited work.

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits Unresolved cited work

Reference 13

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unresolved
no resolver link, observed 2026-08-15T21:18:24.461834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:18:24.461834Z digest=sha256:89d388b21deb460a6e03d72faf94a448757c4143f1261ef24e98e76314edb406

Observation 60dc7f77-d346-4827-9560-4340c40a6e82 · outbound

This paper cites an unresolved cited work.

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits Unresolved cited work

Reference 14

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unresolved
no resolver link, observed 2026-08-15T21:18:24.465579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:18:24.465579Z digest=sha256:bcba308e8e06a60d48ee389fc9a563bd7b1545ccffbd60a5b67a8936b8ed0038

Observation 68686b63-e9a9-4af6-93e3-535a579aab05 · outbound

This paper cites an unresolved cited work.

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits Unresolved cited work

Reference 15

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unresolved
raw_fallback, observed 2026-08-15T21:18:24.932374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-15T21:18:24.469322Z digest=sha256:df01511cdca34d3b0bd345607d9bf0c4c8a4983938e8e2963898689ed84da9b4

Observation b62d7131-15f7-4890-9233-fb87a7807033 · outbound

This paper cites an unresolved cited work.

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits Unresolved cited work

Reference 16

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unresolved
raw_fallback, observed 2026-08-15T21:18:24.920849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-15T21:18:24.473515Z digest=sha256:2cfafdf539c858ec3b3799fb3352503eabc13f076da5a1e58a18519c2279a6d6

Observation ce6043f8-3ae8-4bbd-8495-5b3be7817ab5 · outbound

This paper cites ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-15T21:18:24.477241Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:18:24.477241Z digest=sha256:2f32071d28ced91edcdddbc4cfe50594090110238745f599787e8fc916e6a55a

Observation 3fdaf91a-7873-4863-8bd6-1a85aa345b39 · outbound

This paper cites Decoupled Weight Decay Regularization.

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits Decoupled Weight Decay Regularization

Reference 18

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unresolved
no resolver link, observed 2026-08-15T21:18:24.481301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:18:24.481301Z digest=sha256:4a07be24bc944aa506c6ac0ab32d45ad2f69ab7bfdac8e14f72b49ca1b0d2f51

Observation e1acafc8-4602-485c-8b85-d75248acf651 · outbound

This paper cites Marcus, Mary Ann Marcinkiewicz, and Beatrice Santorini.

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits Marcus, Mary Ann Marcinkiewicz, and Beatrice Santorini

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:18:24.909476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-15T21:18:24.486003Z digest=sha256:810f44be3d7c5c332a419666f897de243fa4e9d50a94ef0efc224188135bca57

Observation c71c33e3-32b2-4ba7-8552-6f3ce7570207 · outbound

This paper cites an unresolved cited work.

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits Unresolved cited work

Reference 20

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unresolved
raw_fallback, observed 2026-08-15T21:18:24.897468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-15T21:18:24.489731Z digest=sha256:e30a8bebb766dfe9d1efbb08aea2655936e80a53ecc9707c1c1dd29d314a6594

Observation 206e5a89-8bec-49b8-9322-739afc69e249 · outbound

This paper cites an unresolved cited work.

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits Unresolved cited work

Reference 21

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unresolved
raw_fallback, observed 2026-08-15T21:18:24.886287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-15T21:18:24.493252Z digest=sha256:959dc14c20b88d5750d6f363555430a733556ffafc94a95bd3308796b400a00b

Observation ade839bf-7dc1-4e10-a3de-b69a70a8379b · outbound

This paper cites an unresolved cited work.

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits Unresolved cited work

Reference 22

Resolution
unresolved
raw_fallback, observed 2026-08-15T21:18:24.874564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-15T21:18:24.497089Z digest=sha256:c194db4b4d5964317dade5e4672cc33b04762e0c376eb287622523f89f4f4973

Observation 46fd6e34-9419-4b17-babe-e9de3a422573 · outbound

This paper cites an unresolved cited work.

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits Unresolved cited work

Reference 23

Resolution
unresolved
raw_fallback, observed 2026-08-15T21:18:24.862645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-15T21:18:24.501028Z digest=sha256:5e8c0405b1f5904f8088483d236877db672fec26adc91c249b0f1da95dea1a05

Observation 21f0a68b-5512-4002-a069-a285804686f2 · outbound

This paper cites GPT-4 Technical Report.

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits GPT-4 Technical Report

Reference 24

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no resolver link, observed 2026-08-15T21:18:24.505437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:18:24.505437Z digest=sha256:26881a4a13cd876f1317fe6bc78887c09dd83eeb56e6922c878141f669512b48

Observation 31439218-63a7-420d-878b-96dc8ae91ca7 · outbound

This paper cites Using the Output Embedding to Improve Language Models.

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits Using the Output Embedding to Improve Language Models

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-15T21:18:24.509783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:18:24.509783Z digest=sha256:c319cf94e3533c4670fa0ac843888f8efe95425c0026acd1ffec85d1277bac52

Observation 1e902cf1-78ba-458b-bd1f-4390ab8179d8 · outbound

This paper cites an unresolved cited work.

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits Unresolved cited work

Reference 26

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unresolved
no resolver link, observed 2026-08-15T21:18:24.514648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:18:24.514648Z digest=sha256:5013c1f373e73bfd0d24683b2861ffbc3789ffe019591803581bdb7301725dd6

Observation 36d1912d-5019-41e7-9cd4-5853cb9d1492 · outbound

This paper cites an unresolved cited work.

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits Unresolved cited work

Reference 27

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unresolved
no resolver link, observed 2026-08-15T21:18:24.518967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:18:24.518967Z digest=sha256:58c1e4a0c58ed3d6957ff4c568c8048c066020d6bbe622182a0718b6d67643c6

Observation 90fe15cc-ea6c-4dab-af39-bd0805568e17 · outbound

This paper cites Sainath, Brian Kingsbury, Bhuvana Ramabhadran, Petr Fousek, Petr Novak, and Abdel - rahman Mohamed.

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits Sainath, Brian Kingsbury, Bhuvana Ramabhadran, Petr Fousek, Petr Novak, and Abdel - rahman Mohamed

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-15T21:18:24.522661Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:18:24.522661Z digest=sha256:73e74f91f43e0e5acda662d573dec663d222fe69c299b45e3a20fdaf448698ba

Observation b2d0dcd4-94a0-4d09-af79-1cfad88b2e17 · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 29

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unresolved
no resolver link, observed 2026-08-15T21:18:24.526083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:18:24.526083Z digest=sha256:18a040276eb9e0a3c33b0b3000760b4705ff8b3d3ab56e4390dd6d0aab57f548

Observation c73622d5-412a-4697-9a0e-302c54b3f22d · outbound

This paper cites Neural Discrete Representation Learning.

VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits Neural Discrete Representation Learning

Reference 30

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unresolved
no resolver link, observed 2026-08-15T21:18:24.529671Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:18:24.529671Z digest=sha256:c65e1fbf22ea79925903538959b50f0ee8b43cd8247172dbda18678d5e038462

Pith citing papers

Observation 985d0610-4d2d-4a68-92e6-acb10f7dc5cf · inbound

SoftWater: Class-Aware Rate Allocation for Softmax Quantization cites this paper.

SoftWater: Class-Aware Rate Allocation for Softmax Quantization VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits

Reference 52

Resolution
metadata mismatch
local_arxiv, observed 2026-08-16T00:26:52.160396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-08-16T00:26:51.707959Z digest=sha256:5356fc0316b2a7484c2f9864e8073c91254cd83388c052917e245065a8b17205