Paper Citation Record · LEDGER

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models

As of 23 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2505.21835.

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

pith.paper-citation-record.v1
2505.21835 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:27:11.054872Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

44 of 44 outbound references displayed

verified exact2
verified fuzzy33
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2136e17d-f7b2-4e88-91dc-a8aee51b6b5e · outbound

This paper cites Sparsellm: Towards global pruning of pre-trained lan- guage models.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models Sparsellm: Towards global pruning of pre-trained lan- guage models

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:17.301812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 50240113-56b8-40ce-a9b4-415f891df18a · outbound

This paper cites Post train- ing 4-bit quantization of convolutional networks for rapid- deployment.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models Post train- ing 4-bit quantization of convolutional networks for rapid- deployment

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:17.182578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e872eb5b-87b9-450c-b3dc-a17cea93ea1a · outbound

This paper cites Zeroq: A novel zero shot quantization framework.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models Zeroq: A novel zero shot quantization framework

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:17.003824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 798f4191-50b1-42c0-8432-0e3ff70663ff · outbound

This paper cites Scatterbrain: Unifying sparse and low- rank attention.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models Scatterbrain: Unifying sparse and low- rank attention

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:16.683251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6b6c9809-c391-4a57-93d8-38764195f81b · outbound

This paper cites Comprehensive survey of model compression and speed up for vision transformers.Journal of Information, Technology and Policy, pages 1–12, 2024.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models Comprehensive survey of model compression and speed up for vision transformers.Journal of Information, Technology and Policy, pages 1–12, 2024

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:16.490975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7da13ec5-e43d-4ddf-b3d1-d83ad8ff2376 · outbound

This paper cites Super- LoRA: Parameter-efficient unified adaptation for large vi- sion models.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models Super- LoRA: Parameter-efficient unified adaptation for large vi- sion models

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:16.327213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3ad0ca8d-894c-48d0-8c40-e52cb3ccf083 · outbound

This paper cites Slaying the hydra: Parameter-efficient hyper networks with low-displacement rank adaptation.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models Slaying the hydra: Parameter-efficient hyper networks with low-displacement rank adaptation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:16.044216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3340848f-345e-4bd1-8d2b-aa2702bdf93c · outbound

This paper cites A survey on deep neural network pruning: Taxonomy, compar- ison, analysis, and recommendations.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models A survey on deep neural network pruning: Taxonomy, compar- ison, analysis, and recommendations

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:15.864540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 83a98a25-848a-4532-9c21-8bd371858cf0 · outbound

This paper cites Rethinking attention with performers.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models Rethinking attention with performers

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:15.726792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9e4473d6-68fb-45f4-8056-19b68514901b · outbound

This paper cites an unresolved cited work.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:27:15.541106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d7d7e1f4-f05a-4c83-87de-ba582b997024 · outbound

This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models An image is worth 16x16 words: Trans- formers for image recognition at scale

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T13:27:08.733767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12627876-5727-414e-954f-79423a6bfe0a · outbound

This paper cites KronA: Parameter efficient tuning with Kronecker adapter.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models KronA: Parameter efficient tuning with Kronecker adapter

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:15.335885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0c6f5d1a-9081-48d5-831d-6a109dad8a09 · outbound

This paper cites Sparsegpt: massive language models can be accurately pruned in one-shot.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models Sparsegpt: massive language models can be accurately pruned in one-shot

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:15.196928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a438a6a2-4a9c-4447-8d20-c814ef2d242d · outbound

This paper cites The Impact of Initialization on LoRA Finetuning Dynamics.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models The Impact of Initialization on LoRA Finetuning Dynamics

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T13:27:08.973188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e4fab44-f78f-40f0-b9f7-2a951a0ad48c · outbound

This paper cites Language model compression with weighted low-rank factorization.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models Language model compression with weighted low-rank factorization

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:15.031749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 28e35ff2-89d7-43e3-a9a2-367b03e532d8 · outbound

This paper cites Lora: Low- rank adaptation of large language models.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models Lora: Low- rank adaptation of large language models

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:14.903328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6a5a103e-deb0-4096-850b-2e4080bb7985 · outbound

This paper cites Com- pressing speaker extraction model with ultra-low precision quantization and knowledge distillation.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models Com- pressing speaker extraction model with ultra-low precision quantization and knowledge distillation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:14.756114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bd50a7e3-423e-44da-a64a-bcd1ed6c7cca · outbound

This paper cites PC-LoRA: Low-Rank Adaptation for Progressive Model Compression with Knowledge Distillation.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models PC-LoRA: Low-Rank Adaptation for Progressive Model Compression with Knowledge Distillation

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T13:27:09.214105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47e69711-42d8-424f-9072-2ab7f25081fc · outbound

This paper cites Gpt-zip: Deep compres- sion of finetuned large language models.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models Gpt-zip: Deep compres- sion of finetuned large language models

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:14.610831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 728c7c8c-c5ff-4d14-81e7-b597eb1ac161 · outbound

This paper cites A neural network com- pression method based on knowledge-distillation and param- eter quantization for the bearing fault diagnosis.Applied Soft Computing, 127:109331, 2022.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models A neural network com- pression method based on knowledge-distillation and param- eter quantization for the bearing fault diagnosis.Applied Soft Computing, 127:109331, 2022

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:14.413962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c89cf26d-c698-466f-ae2b-b93659449954 · outbound

This paper cites Learning multiple layers of features from tiny images.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models Learning multiple layers of features from tiny images

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T13:27:09.460156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7bd84b27-84f0-49d9-9821-3523e01404da · outbound

This paper cites Reward design with language models.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models Reward design with language models

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:14.224375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 459a16c8-a939-480d-bfe5-c16df65d2569 · outbound

This paper cites A fast post- training pruning framework for transformers.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models A fast post- training pruning framework for transformers

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:14.027390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation dba9a3b0-07b2-42de-be6b-b5d84cf5d726 · outbound

This paper cites On the Crucial Role of Initialization for Matrix Factorization.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models On the Crucial Role of Initialization for Matrix Factorization

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:27:11.467656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3b3f448b-9084-4144-9a0b-181594f09b0f · outbound

This paper cites Yolo-based face mask detection on low-end devices using pruning and quantization.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models Yolo-based face mask detection on low-end devices using pruning and quantization

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:13.854642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 28c3630b-11a2-4bd7-b888-7977dfd0de5a · outbound

This paper cites Awq: Activation-aware weight quantization for on-device llm compression and acceleration.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models Awq: Activation-aware weight quantization for on-device llm compression and acceleration

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:13.606005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5b687bd2-61c0-41b6-ab63-e240b78a9d5c · outbound

This paper cites Loda: Low-dimensional adaptation of large language models.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models Loda: Low-dimensional adaptation of large language models

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:13.489565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 962da8db-f74b-4445-8fca-d8c5d8d50619 · outbound

This paper cites Rethinking the value of network pruning.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models Rethinking the value of network pruning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:13.316518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation beab3b1b-cf2c-4330-bc5e-88af6f82d6f4 · outbound

This paper cites Com- puter vision model compression techniques for embedded systems: A survey.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models Com- puter vision model compression techniques for embedded systems: A survey

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:13.188385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8963f463-2cd5-4565-bb02-b4f148379ddd · outbound

This paper cites ShortGPT: Layers in Large Language Models are More Redundant Than You Expect.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models ShortGPT: Layers in Large Language Models are More Redundant Than You Expect

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T13:27:10.007718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b80768bf-516a-4c59-9567-ac132bf8778d · outbound

This paper cites PiSSA: Principal singular values and singular vectors adaptation of large language models.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models PiSSA: Principal singular values and singular vectors adaptation of large language models

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:13.025869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation dd3fa0d7-1ecc-4981-8bc7-531560141953 · outbound

This paper cites Data-free quantization through weight equal- ization and bias correction.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models Data-free quantization through weight equal- ization and bias correction

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:12.859768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1abad312-9419-45e0-aee5-e844a7675ae4 · outbound

This paper cites Structured unrestricted-rank matrices for parameter efficient finetuning.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models Structured unrestricted-rank matrices for parameter efficient finetuning

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:12.754961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4afef119-3aca-4c14-92a3-0c76be11ce82 · outbound

This paper cites Sanity-checking prun- ing methods: Random tickets can win the jackpot.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models Sanity-checking prun- ing methods: Random tickets can win the jackpot

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:12.571987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation df4c8700-5461-4287-9bbf-68d47f7c8b5a · outbound

This paper cites Fine-Pruning: Joint Fine-Tuning and Compression of a Convolutional Network with Bayesian Optimization.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models Fine-Pruning: Joint Fine-Tuning and Compression of a Convolutional Network with Bayesian Optimization

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:27:11.252846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 85810dff-2c11-443d-8c8c-505104d94ec4 · outbound

This paper cites Gan slimming: All-in-one gan compres- sion by a unified optimization framework.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models Gan slimming: All-in-one gan compres- sion by a unified optimization framework

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:12.380482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6ce94ae1-8776-4a69-a417-dbc03dc0484d · outbound

This paper cites Pufferfish: Communication-efficient models at no extra cost.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models Pufferfish: Communication-efficient models at no extra cost

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:12.277986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 46b6a569-7155-4d36-9e62-c123388e4c4d · outbound

This paper cites SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T13:27:10.525787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation acb47ce2-d9d5-4612-ab10-b9452038e4f6 · outbound

This paper cites Nystr¨omformer: A nystr¨om-based algorithm for approximat- ing self-attention.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models Nystr¨omformer: A nystr¨om-based algorithm for approximat- ing self-attention

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:12.169278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation da3faa53-5715-459d-aa10-a821087a47ba · outbound

This paper cites CorDA: Context-oriented decomposition adaptation of large language models for task-aware parameter-efficient fine- tuning.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models CorDA: Context-oriented decomposition adaptation of large language models for task-aware parameter-efficient fine- tuning

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:11.985340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b0abe06a-c59a-41d4-bb0b-551bcc15481c · outbound

This paper cites Joint-detnas: Upgrade your detector with nas, pruning and dynamic distillation.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models Joint-detnas: Upgrade your detector with nas, pruning and dynamic distillation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:11.814204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 31b33a42-4c11-4ff7-932a-855b9c58ca98 · outbound

This paper cites Navigating text-to- image customization: From LyCORIS fine-tuning to model evaluation.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models Navigating text-to- image customization: From LyCORIS fine-tuning to model evaluation

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:27:11.669401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bc21011f-198d-4ce5-9c00-4842c0696f96 · outbound

This paper cites RPTQ: Reorder-based Post-training Quantization for Large Language Models.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models RPTQ: Reorder-based Post-training Quantization for Large Language Models

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T13:27:10.956921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b83ea1fb-458f-4318-9c9a-8156c12db32f · outbound

This paper cites ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models.

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T13:27:11.054872Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.