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Paper Citation Record · LEDGER

Can Post-Training Quantization Benefit from an Additional QLoRA Integration?

As of 10 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2502.10202.

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

pith.paper-citation-record.v1
2502.10202 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T19:03:04.952823Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

31 of 31 outbound references displayed

  • verified exact0
  • verified fuzzy1
  • unresolved30
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b8fc4d71-1995-4814-9768-167912fde731 · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 1

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Unavailable: canonical work link unavailable.

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Observation b001be23-2ee9-46fb-ac30-a1c246e9861c · outbound

This paper cites Efficient Intent Detection with Dual Sentence Encoders.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? Efficient Intent Detection with Dual Sentence Encoders

Reference 2

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source=arxiv_source observed=2026-08-07T19:03:04.790602Z digest=sha256:027afa0f8a8e0fa224bceab31bb5fc7db373940c933f51268343cc150e16a2ca

Observation 478cec03-2437-43ca-a72f-99f6f06010fa · outbound

This paper cites an unresolved cited work.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? Unresolved cited work

Reference 3

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source=arxiv_source observed=2026-08-07T19:03:04.795949Z digest=sha256:b5dbcd329349923cb9236d0e6cfdd35b43250b1782674c0b68a8cf66bef38923

Observation 2a86c0fc-a129-4403-a53c-32f80bf719d7 · outbound

This paper cites an unresolved cited work.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? Unresolved cited work

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-07T19:03:04.803756Z digest=sha256:70648a0e6816bd4dc20de1604c4fede218f8287b6c425bc2c559a5c714fddef7

Observation 4c4e017a-8a34-4ec9-aaac-6496d5935516 · outbound

This paper cites an unresolved cited work.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? Unresolved cited work

Reference 5

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source=arxiv_source observed=2026-08-07T19:03:04.809073Z digest=sha256:43e50954785e426cdfd3863c4748504ddfa486980e4afcbb14eb607d4c1b7ba8

Observation 9ba65dab-8ce3-41ed-b955-eb89ea4691df · outbound

This paper cites an unresolved cited work.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? Unresolved cited work

Reference 6

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 08eacbdc-a5f1-45d5-ba91-a8ad3cb60419 · outbound

This paper cites an unresolved cited work.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? Unresolved cited work

Reference 7

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source=arxiv_source observed=2026-08-07T19:03:04.819711Z digest=sha256:4f3adff76117dc1d24e5b318e829cd18fa36cb95724ae97218d592072459a6eb

Observation c62a6ab0-8f4f-4642-a7cb-6c6ca9356536 · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 8

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source=arxiv_source observed=2026-08-07T19:03:04.824895Z digest=sha256:c78a51b4472c9c008b43c9563236771165514fca24086324bfd8b5a1e1a1c1b8

Observation ba15b062-6f1f-4f08-ae8b-f99cc97a44b3 · outbound

This paper cites an unresolved cited work.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? Unresolved cited work

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation be0c3fb5-8441-4c69-9787-a111ff43e423 · outbound

This paper cites A Survey of Quantization Methods for Efficient Neural Network Inference.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? A Survey of Quantization Methods for Efficient Neural Network Inference

Reference 10

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Observation 849cea46-4e56-4e4d-9d99-31babb59e85a · outbound

This paper cites Parameter-Efficient Transfer Learning for NLP.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? Parameter-Efficient Transfer Learning for NLP

Reference 11

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source=arxiv_source observed=2026-08-07T19:03:04.842154Z digest=sha256:2976a6cc50481a4eae8c6eafdac4de9bc2af37b44bc875bf44b05175a5dfd1fa

Observation 280796a5-b564-47fc-b041-9dcdc695c225 · outbound

This paper cites an unresolved cited work.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? Unresolved cited work

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation efdac8ed-8a5d-453c-8c98-e8919c548821 · outbound

This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen - Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen - Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 13

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verified fuzzy
raw_fallback, observed 2026-08-07T19:03:05.454151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-07T19:03:04.851839Z digest=sha256:ed295f50201bfb404edd5acde405251c112e5996c30d16d72272826a73e4fd3d

Observation 9692f89c-8f4a-4f9f-8842-5465d9cf0c23 · outbound

This paper cites an unresolved cited work.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? Unresolved cited work

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-07T19:03:04.855994Z digest=sha256:3829f854ad1afa10a0673de6e99a38b98fbdd5886afadb0aafef62db03799ed5

Observation f259af1e-2281-4c7f-a1ac-dca7fdfa2024 · outbound

This paper cites Mistral 7B.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? Mistral 7B

Reference 15

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T19:03:04.860679Z digest=sha256:810cfd39e5813898ae74565786ccf2acd64332897edb86785477e206a75b9475

Observation 1ba30fcc-a5e0-469a-a5c1-42f00d72f477 · outbound

This paper cites Exploring the Trade-Offs: Quantization Methods, Task Difficulty, and Model Size in Large Language Models From Edge to Giant.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? Exploring the Trade-Offs: Quantization Methods, Task Difficulty, and Model Size in Large Language Models From Edge to Giant

Reference 16

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unresolved
no resolver link, observed 2026-08-07T19:03:04.865990Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T19:03:04.865990Z digest=sha256:07e34b4e269549223d4fef7cde77032f2b39f48d787bfc046f244fa2e959415d

Observation 3dfdb429-e723-4e75-a4bb-51d0e5dac7f0 · outbound

This paper cites BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction

Reference 17

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source=arxiv_source observed=2026-08-07T19:03:04.871376Z digest=sha256:2f388400c3ce827b427fd5096f645af73e7c0bd906603f4f05880b92f9fbaec6

Observation 52a1e0e5-d7bc-4f83-937b-316a46c593ad · outbound

This paper cites an unresolved cited work.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? Unresolved cited work

Reference 18

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raw_fallback, observed 2026-08-07T19:03:05.410906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-07T19:03:04.876851Z digest=sha256:5d3a43c8cb5db30f902df6123edb231d29ceb7b1647f0dbcab2b3d4994997a44

Observation 59634723-225c-45b9-a7d4-632ad0ec4466 · outbound

This paper cites an unresolved cited work.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? Unresolved cited work

Reference 19

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-07T19:03:04.882502Z digest=sha256:6e4c689a6dd704ab8f4c242e45b02336db5f283153033468ca38b0f254c10b87

Observation 5df8c8fb-2ca8-4e20-9fed-1609a0470700 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 20

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Observation 84f0cab1-ee13-4463-a783-62b6ce8a871b · outbound

This paper cites an unresolved cited work.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? Unresolved cited work

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 38e9f7c6-a4ca-4f9b-80a6-3cbc5368a6c2 · outbound

This paper cites A White Paper on Neural Network Quantization.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? A White Paper on Neural Network Quantization

Reference 22

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Unavailable: canonical work link unavailable.

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Observation e0e7553a-9311-4693-9a29-229204ca3ffb · outbound

This paper cites an unresolved cited work.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? Unresolved cited work

Reference 23

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source=arxiv_source observed=2026-08-07T19:03:04.904192Z digest=sha256:a4886e7fc307803ce7030b0766c67ae7d7891b1513723e5435be49e7a53e38d7

Observation cce01f86-fdb0-49bc-94a7-56f2809cb184 · outbound

This paper cites Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Reference 24

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Observation 0f56160b-4c24-4c59-86e2-2736592306c1 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 25

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Unavailable: canonical work link unavailable.

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Observation 2c5bcfe7-7558-4f49-aa0d-c3c5519629f4 · outbound

This paper cites Smith, Daniel Khashabi, and Hannaneh Hajishirzi.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? Smith, Daniel Khashabi, and Hannaneh Hajishirzi

Reference 26

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Unavailable: canonical work link unavailable.

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Observation 6194d3b9-a5c9-4c1f-bcb9-16d84940719b · outbound

This paper cites an unresolved cited work.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? Unresolved cited work

Reference 27

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no resolver link, observed 2026-08-07T19:03:04.927820Z

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Unavailable: canonical work link unavailable.

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Observation 7d0d3486-75ed-4b5c-87a8-ff7e59931247 · outbound

This paper cites Qwen2 Technical Report.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? Qwen2 Technical Report

Reference 28

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no resolver link, observed 2026-08-07T19:03:04.935180Z

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Unavailable: canonical work link unavailable.

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Observation 09373e4b-cbaa-43b4-a5da-96b915ba79fe · outbound

This paper cites an unresolved cited work.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? Unresolved cited work

Reference 29

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Unavailable: canonical work link unavailable.

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Observation 6739deae-6ac4-48f6-ae1b-fa46dcaf5382 · outbound

This paper cites online" 'onlinestring :=.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? online" 'onlinestring :=

Reference 30

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no resolver link, observed 2026-08-07T19:03:04.947234Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T19:03:04.947234Z digest=sha256:82d9693bbb2d4698c033ea69c5cf3e94a21991b92a193277c34fa7e395bcfbba

Observation 5c42d01f-63ca-4c55-98d1-01e8a4abc59e · outbound

This paper cites write newline.

Can Post-Training Quantization Benefit from an Additional QLoRA Integration? write newline

Reference 31

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Pith citing papers

No inbound Pith citation observations are available.