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Paper Citation Record · LEDGER

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs

As of 22 August 2026, this Paper Citation Record lists 70 of 70 outbound references and 2 inbound Pith citation observations for arXiv:2412.14426.

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

pith.paper-citation-record.v1
2412.14426 v2

Coverage vector

measured 70 of 70 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:17:56.885258Z

measured 72 of 72 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:51:04.861888Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T04:58:28.692022Z

Reference resolution

70 of 70 outbound references displayed

  • verified exact2
  • verified fuzzy1
  • unresolved67
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c9289a3b-e935-408b-ba9e-bad28ee5d87a · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.611533Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.611533Z digest=sha256:33661ec674cc222b219f30eea254ac3b9547d39ce14ccc6633b2209a643adffa

Observation 63cd8f86-3ab5-4867-b766-acdd172bc0bf · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 2

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no resolver link, observed 2026-08-11T12:17:56.616062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.616062Z digest=sha256:2cee94de5cb9dbb85545c82838029c8bf2b5806487543d5a6c2197e6486f0435

Observation 303d73e3-5754-46bf-8752-eeef6c53dbb2 · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 3

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unresolved
no resolver link, observed 2026-08-11T12:17:56.619728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.619728Z digest=sha256:516a18ceb752c970a53a219c39c710eda3ab714507e3e5d1a1f3afb7d2782828

Observation 8adcb9cd-4612-4848-a386-ee899882e757 · outbound

This paper cites Layer Normalization.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Layer Normalization

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.623947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.623947Z digest=sha256:52531758a3d2eb8f35eca013410bd14f5e7d6a36008a6279f2ba4dc328f6a33f

Observation a5aed046-c239-484c-a7bc-34f800f0a2b7 · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 5

Resolution
unresolved
raw_fallback, observed 2026-08-11T12:17:57.679308Z

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=2026-08-11T12:17:56.628188Z digest=sha256:0e2efbc9379365aecccc95fb39966a603975e33b566f6a29b6cb33895e20fad1

Observation b8833fdd-0169-40ec-954c-00baab62ef24 · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 6

Resolution
unresolved
raw_fallback, observed 2026-08-11T12:17:57.667682Z

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=2026-08-11T12:17:56.631928Z digest=sha256:e293602e710719011fd7dd62feb5f0560a2dd37fffcd08b21d6fc61d593bc69f

Observation a2ae5180-97d8-4fe7-a1b8-f961657358f8 · outbound

This paper cites A Recent Survey of Heterogeneous Transfer Learning.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs A Recent Survey of Heterogeneous Transfer Learning

Reference 7

Resolution
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no resolver link, observed 2026-08-11T12:17:56.635918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.635918Z digest=sha256:bf1dc955742d0eade9060ba9cbc8152d0a4eff93401c4018592850fc2e31182b

Observation 9af885bf-a9b0-4227-a291-f0d216e3742a · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 8

Resolution
unresolved
raw_fallback, observed 2026-08-11T12:17:57.656175Z

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=2026-08-11T12:17:56.639859Z digest=sha256:d614a20593444c422daa069eecaaf1ae9709b08b1e882c7ef575b6e90445e64e

Observation a2fb8e0e-3045-4f6e-8d21-ac34c84b94e3 · outbound

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

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 9

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no resolver link, observed 2026-08-11T12:17:56.644139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.644139Z digest=sha256:94af8d9ec1893357825953e0c470854c956431121fcb2ec5985c50fec321f354

Observation 549926a7-4b52-495f-95a8-5848d8ad7df1 · outbound

This paper cites Hudson, Ehsan Adeli, and Russ Altman.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Hudson, Ehsan Adeli, and Russ Altman

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T12:17:57.645622Z

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=2026-08-11T12:17:56.648381Z digest=sha256:dbf672785dcda332fbb12f5d1742ca1ea2eb93d83db549c595cb63f18e94bb3a

Observation a0eac82e-871e-4623-a15f-b5a8e0c36b6c · outbound

This paper cites Med42 -- Evaluating Fine-Tuning Strategies for Medical LLMs: Full-Parameter vs. Parameter-Efficient Approaches.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Med42 -- Evaluating Fine-Tuning Strategies for Medical LLMs: Full-Parameter vs. Parameter-Efficient Approaches

Reference 11

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no resolver link, observed 2026-08-11T12:17:56.651892Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.651892Z digest=sha256:0b4452ea01c67e176126699b26977ae2c22db776dc1511be19dc60e553f83b2f

Observation 1a95f531-cea9-4155-a95d-4abf0ff81c17 · outbound

This paper cites The Llama 3 Herd of Models.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs The Llama 3 Herd of Models

Reference 12

Resolution
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no resolver link, observed 2026-08-11T12:17:56.655871Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.655871Z digest=sha256:fe5b96e0e9197809f327aa81104d163f2a34f8ac7a66590680ee925ae1fa1f3d

Observation 8aa98df6-2906-4d90-aa23-8fd900d0a087 · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.659701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.659701Z digest=sha256:cf33c2c31977078075f7872cf95d5a9b559f64871ffff90561d8e402d1adcbb6

Observation cf6e3f4d-a2c4-484e-b4dd-b74911565472 · outbound

This paper cites DISP-LLM: Dimension-Independent Structural Pruning for Large Language Models.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs DISP-LLM: Dimension-Independent Structural Pruning for Large Language Models

Reference 14

Resolution
verified exact
local_arxiv, observed 2026-08-11T12:17:57.301545Z

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=2026-08-11T12:17:56.663320Z digest=sha256:344e4568306d321ef67028c3a2406e963439bbd01f09ee392f04d6db1290d070

Observation a98869ee-5890-4ffb-8efa-8a4c842cadab · outbound

This paper cites Compresso: Structured Pruning with Collaborative Prompting Learns Compact Large Language Models.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Compresso: Structured Pruning with Collaborative Prompting Learns Compact Large Language Models

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.668390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.668390Z digest=sha256:8669e227886ce3bfa39e6f2458021ea0ed0733a87be5f3cdf3d4a56c47682675

Observation 91a47d0d-71be-461e-886c-459e0791450b · outbound

This paper cites Towards a Unified View of Parameter-Efficient Transfer Learning.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Towards a Unified View of Parameter-Efficient Transfer Learning

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.672478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.672478Z digest=sha256:55ef2315fedba2fa1be1418b7193310b1725ec02d36835ef84f69fc592ce8c16

Observation 6ab4995c-def5-47aa-a956-185212d6e4b4 · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.676338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.676338Z digest=sha256:51aac2849e6e076b15b7255786711f9cef46c7d87902988bce77eeb2a2caae1d

Observation b37ec56e-6a91-4050-bb92-2c4f98328b54 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs LoRA: Low-Rank Adaptation of Large Language Models

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.683114Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.683114Z digest=sha256:fb4b2a532631006c22f7fce965091e092812955e0d7299eb27c1d32b661842ea

Observation 683b9187-37ba-4485-9b2e-1c8203e9237a · outbound

This paper cites Categorical Reparameterization with Gumbel-Softmax.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Categorical Reparameterization with Gumbel-Softmax

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.687043Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.687043Z digest=sha256:ddca5df55a557181cde442ac665b7f0461be5854a71b7f3c0806eaba143b444a

Observation c118b74d-40f4-45a1-bf3f-18eafd6e1160 · outbound

This paper cites Fine-tuning and Utilization Methods of Domain-specific LLMs.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Fine-tuning and Utilization Methods of Domain-specific LLMs

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.690667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.690667Z digest=sha256:7ecf1603e1a0ae567598a9a10d13b86b65d63782fe092b766539b60a21f16242

Observation 49db6295-5c76-4c38-8295-a295f36e804e · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 21

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no resolver link, observed 2026-08-11T12:17:56.694257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.694257Z digest=sha256:86ab35b32dc443667113437186ef205022919b158d8a8e35a875a08d06ecd558

Observation 93c48780-4ff4-4c3a-8182-93d08f90c17b · outbound

This paper cites BillSum: A Corpus for Automatic Summarization of US Legislation.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs BillSum: A Corpus for Automatic Summarization of US Legislation

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.697713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.697713Z digest=sha256:7576e372a2fff56ec8dcbdc3df95548876cbdc698330cb7d55cc21868614deca

Observation b7e4a9d6-c77f-4422-8853-593d3482ec46 · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 23

Resolution
unresolved
raw_fallback, observed 2026-08-11T12:17:57.614152Z

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=2026-08-11T12:17:56.701608Z digest=sha256:6682b5650c7699063cb72d5939846d56c59ab4d26c47c9ddeacdcc4838362f22

Observation c881e8ae-cab6-4eac-bed4-5226ecc54c8d · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 24

Resolution
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no resolver link, observed 2026-08-11T12:17:56.704995Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.704995Z digest=sha256:ecc6999b52833ca6cc8eebba83a521fe2233a88844af3729250dfc808cc8c6d9

Observation 27d1d1e6-44e0-4e63-827c-c8028b711794 · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.708764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.708764Z digest=sha256:281dae886d9495c86b6adf02a07db9fcae32e9ab0630fb9b433cf2fd0bd0e200

Observation 280e1223-3438-459c-ada2-a5486a6916d4 · outbound

This paper cites MoDeGPT: Modular Decomposition for Large Language Model Compression.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs MoDeGPT: Modular Decomposition for Large Language Model Compression

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.713172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.713172Z digest=sha256:8b877b4961ada266d0374d0ceb3f045948888d0d799671ab934905dd1606bc57

Observation da0e8439-8ab8-443b-815e-dbe792ecced0 · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.716980Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.716980Z digest=sha256:f1fbd126226c1f39eb4470d64123c34d1541f9f598f9c74078834a6489804b96

Observation 93f7da0d-e75d-4dc6-94d6-209082789501 · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 28

Resolution
unresolved
raw_fallback, observed 2026-08-11T12:17:57.595791Z

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=2026-08-11T12:17:56.720546Z digest=sha256:9ff62b40d8389feda40f0cf3711ba8aef490eb078adc3be818fdffae064df7bc

Observation 4cc9bc4b-72a8-4f25-aaa2-9d2145491a4f · outbound

This paper cites Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.724468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.724468Z digest=sha256:55c31a448685c81bbcb9c5f7eaaead492667a15e31755cec85d02b25e72b493e

Observation c2c8fd07-c239-4cbb-a92d-8498832db9be · outbound

This paper cites DoRA: Weight-Decomposed Low-Rank Adaptation.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs DoRA: Weight-Decomposed Low-Rank Adaptation

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.728340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.728340Z digest=sha256:a9ea49ea1d869f65fc57ff673d3670ab19bc326189f1bc0be2367b08816b6a66

Observation d40a94bb-efce-433d-8b19-ed7f2215c323 · outbound

This paper cites P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.732578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.732578Z digest=sha256:9f92400e9078fb1722307c34c50233ac404c9a8ba9112fc1afe85565bf3500ab

Observation ec1fc60c-ff1c-44d7-a95a-aca181f4ea26 · outbound

This paper cites Full Parameter Fine-tuning for Large Language Models with Limited Resources.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Full Parameter Fine-tuning for Large Language Models with Limited Resources

Reference 32

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unresolved
no resolver link, observed 2026-08-11T12:17:56.736630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.736630Z digest=sha256:146f0ed769aa8708fbb9c321af336f45b824de13e970a659d0bd2cf068efdff4

Observation b7f973e8-24b5-413e-a151-b34d66e7bf39 · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.740383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.740383Z digest=sha256:449734fd36e0719113256726e1fedd07451b3d80106b5c2fb9adb38b27a7d0b3

Observation 87f712e0-d56e-4446-9c21-bb60fcb349a8 · outbound

This paper cites MultiLegalPile: A 689GB Multilingual Legal Corpus.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs MultiLegalPile: A 689GB Multilingual Legal Corpus

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.744222Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.744222Z digest=sha256:56bae2e8f6a6e867468dc48a417e5711f67fae4f363de824ae1d5bfcb07f8c96

Observation 38d5e26f-2792-455d-bed0-3fc30e773878 · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.748772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.748772Z digest=sha256:e6d110eea4e1eabfe5185a2ea71dd8e5d2c3ae40eeb016aa9269ad5d93c92d6c

Observation 470119a2-c773-4483-8f6f-b1ee3165b1d6 · outbound

This paper cites Lessons from Natural Language Inference in the Clinical Domain.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Lessons from Natural Language Inference in the Clinical Domain

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.752344Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.752344Z digest=sha256:b140bb231fc40f3375dbd6758d7d0ac7cae1bd8eb55abe0b46764f7777caa0f3

Observation 6d01ff88-e681-47bf-afa1-1c5929237b8c · outbound

This paper cites Instruction Tuning With Loss Over Instructions.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Instruction Tuning With Loss Over Instructions

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.756532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.756532Z digest=sha256:1b03fa3949ef38fbec49d26f9735e9868bf82847a3bb145216d8c1a25ab57522

Observation 427419b7-83bd-4d53-b60c-3292869875d5 · outbound

This paper cites Towards Greener LLMs: Bringing Energy-Efficiency to the Forefront of LLM Inference.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Towards Greener LLMs: Bringing Energy-Efficiency to the Forefront of LLM Inference

Reference 38

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unresolved
no resolver link, observed 2026-08-11T12:17:56.760236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.760236Z digest=sha256:9c4cd66c163b5c2ea718615c368207de1371de4acb3841d26428e424c3c00f2e

Observation 68ed08ca-6355-4c80-b319-fb4a6ee67289 · outbound

This paper cites A Simple and Effective Pruning Approach for Large Language Models.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs A Simple and Effective Pruning Approach for Large Language Models

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.764087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.764087Z digest=sha256:b25db841b50858d57a733067cfd55cd2ca2824bcd3c62a52f9eff1a4815c1985

Observation 5c731372-b90e-4a39-abb0-645ba6acc1a6 · outbound

This paper cites Automating Research Synthesis with Domain-Specific Large Language Model Fine-Tuning.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Automating Research Synthesis with Domain-Specific Large Language Model Fine-Tuning

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.767904Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.767904Z digest=sha256:8bb72abb7ce81284eaa679fa8a27fde1c1015081da3f7f7709692b446eab987b

Observation 594f630d-fcc3-4e7d-a6d7-524449b19ec4 · outbound

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

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.771743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.771743Z digest=sha256:0129dda84f40b34a1c6eebe7325ced86f55e408231f48a62e3ed8efcd0ad1d1d

Observation f4f178ba-9d3c-4cd1-83bd-e4acd5448f0e · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 42

Resolution
unresolved
raw_fallback, observed 2026-08-11T12:17:57.570300Z

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=2026-08-11T12:17:56.775807Z digest=sha256:8cdc8895949cee454ff6f9ace0a928fb475631a85ea4d403d673de93db028c13

Observation f0dde0f9-d1da-4039-9442-fad864f0a71b · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.779369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.779369Z digest=sha256:9f6862f7eb9675ac9c9855778564baeb0289af5c3f58d76d4d4ce67fffbe00f2

Observation f367fe39-b176-4c2b-91f3-8b6285a68126 · outbound

This paper cites Efficient Large Language Models: A Survey.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Efficient Large Language Models: A Survey

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.782840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.782840Z digest=sha256:94489241c340d9b1beb010deb65809739a2543e639f2c6a03f6cfa8995f1c882

Observation 9551cab0-254d-45c4-af2b-2bac1d2e7678 · outbound

This paper cites InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.786438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.786438Z digest=sha256:92a3780594671e950147ab1e16730728c90800f171b01d3765efacc7dde2dc81

Observation 843a548b-0452-45c1-8534-b7771393c577 · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 46

Resolution
unresolved
raw_fallback, observed 2026-08-11T12:17:57.552744Z

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=2026-08-11T12:17:56.791016Z digest=sha256:5d613fdf15f1776dc48d787fce70deb449a6f9d85c261a4a5c7c464e968125c1

Observation 7e5c9b38-b446-4b10-8099-640f51d7f2af · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 47

Resolution
unresolved
raw_fallback, observed 2026-08-11T12:17:57.542394Z

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=2026-08-11T12:17:56.794530Z digest=sha256:b5ad46a885da7c8295b1adac4616fc54348c2f63bcde29acff674a79ad2b361a

Observation 845cfa8f-c8de-456a-897c-1b35532c3dea · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 48

Resolution
unresolved
raw_fallback, observed 2026-08-11T12:17:57.531706Z

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=2026-08-11T12:17:56.797876Z digest=sha256:1dd52fea92c049de6a421aa1c83d5239e52d33a718f2d03b1dc07a26f64449bc

Observation 83e9cf2c-42e4-4833-aea6-9f22f4fbeae1 · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 49

Resolution
unresolved
raw_fallback, observed 2026-08-11T12:17:57.520559Z

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=2026-08-11T12:17:56.801242Z digest=sha256:b1db739a95d0d9310e097a454776f52f9bf6341ab0dbd394c7d0532ef94ddfe1

Observation ee030299-06df-498a-a0fa-cb1f8474efc4 · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 50

Resolution
unresolved
raw_fallback, observed 2026-08-11T12:17:57.509748Z

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=2026-08-11T12:17:56.804796Z digest=sha256:2be96b172607d7c84fbc546b602c1603c77b42a969aa67717ab1ff049a68ae00

Observation 38c11f8f-c024-4824-8556-9906d9c6ba7e · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 51

Resolution
unresolved
raw_fallback, observed 2026-08-11T12:17:57.497789Z

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=2026-08-11T12:17:56.808279Z digest=sha256:0a9b3d46042a139bda12a1fa2581dbc662b87f2770a1177e8590e7db0c1cb494

Observation 007519ed-e948-41df-bc45-59be154e448e · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 52

Resolution
unresolved
raw_fallback, observed 2026-08-11T12:17:57.486122Z

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=2026-08-11T12:17:56.811943Z digest=sha256:f6176e6d374adc2526d24500c06d031121fc74d10b1121d00731b4c8b35c2772

Observation 84812da3-0fde-45c8-8409-794494107598 · outbound

This paper cites Decentralized Unsupervised Learning of Visual Representations.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Decentralized Unsupervised Learning of Visual Representations

Reference 53

Resolution
verified exact
local_arxiv, observed 2026-08-11T12:17:56.970817Z

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=2026-08-11T12:17:56.816040Z digest=sha256:ca15bc13e9b17016d8b622a13284d496c51634d5605489874ff28096213fdb89

Observation 972d6c36-d194-4285-a575-8e9bae01379d · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 54

Resolution
unresolved
raw_fallback, observed 2026-08-11T12:17:57.474054Z

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=2026-08-11T12:17:56.820161Z digest=sha256:7ea2b826e12f71255360fe434b76202543f457c50c1e80571b28be708caacafe

Observation 6cb63d5b-86ee-40f3-9531-6f69f03e8588 · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 55

Resolution
unresolved
raw_fallback, observed 2026-08-11T12:17:57.463094Z

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=2026-08-11T12:17:56.824311Z digest=sha256:5451f9184a8f85889dceaf78accd0f4f84ea7d6f91e4e185e60066b74c3316ef

Observation b0fb088b-33c9-42a0-895d-7e90424f25e3 · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 56

Resolution
unresolved
raw_fallback, observed 2026-08-11T12:17:57.451714Z

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=2026-08-11T12:17:56.828040Z digest=sha256:594049dfb2d6182b1a01ff1999791b88f130ffa9313a9adca4db6ec0841f9611

Observation 55b8f7e2-a70d-499d-81fd-e9660cb0f51e · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 57

Resolution
unresolved
raw_fallback, observed 2026-08-11T12:17:57.440765Z

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=2026-08-11T12:17:56.831708Z digest=sha256:f897617e28066b5d21a93483b0590e54c1e380c727feafa9b4f7e1bd390bf3c4

Observation a6f2a0d2-813e-4e3e-9db9-84642c68707c · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 58

Resolution
unresolved
raw_fallback, observed 2026-08-11T12:17:57.427710Z

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=2026-08-11T12:17:56.836714Z digest=sha256:0762021aa27bac905ec17c4d7c616c06f332509b5ebfaca1789e7b68ae4a71ac

Observation ec1a0337-694e-41df-b196-081755840f5d · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.840266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.840266Z digest=sha256:3d7c4d94612fe575b635dd8e3d9e4ad13a11b2096261cd70ccaa6153c2e445f6

Observation 3a1918fc-8e77-4442-bd06-5dd353be74a5 · outbound

This paper cites Me LLaMA: Foundation Large Language Models for Medical Applications.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Me LLaMA: Foundation Large Language Models for Medical Applications

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.843858Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.843858Z digest=sha256:23632a113fc27d083d04558241294a859ba65189f67785a562fedb4c1e97a25e

Observation e28f2532-787e-471c-95f7-31881f8f9748 · outbound

This paper cites When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.848531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.848531Z digest=sha256:c17e906a1206a0b90a99172f6f025cb12baa0a5f29ec0934d816e241a429c5e6

Observation 8101fbc1-681a-4d21-a857-884d922595ce · outbound

This paper cites LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.852294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.852294Z digest=sha256:af198727451c5e74f430740d1706e5f36a5db8d60912ff2442a8ea20b8174783

Observation 7d0b60d6-6348-46fe-a089-b6b17270ee24 · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 63

Resolution
unresolved
raw_fallback, observed 2026-08-11T12:17:57.408687Z

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=2026-08-11T12:17:56.856955Z digest=sha256:c7b6064cca89c2deb07035b31011d561f39ce8e6672026e090fdde0372fd112f

Observation c2a84c4f-14a3-489b-be15-6041bba1b0ea · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 64

Resolution
unresolved
raw_fallback, observed 2026-08-11T12:17:57.397521Z

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=2026-08-11T12:17:56.860220Z digest=sha256:7670a4546fb8f178307eef91e0e6125594a22235beb429281efb8fe7193d4489

Observation ec2cbba5-e095-4a16-8547-3db47f66d549 · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 65

Resolution
unresolved
raw_fallback, observed 2026-08-11T12:17:57.386051Z

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=2026-08-11T12:17:56.865294Z digest=sha256:693d5896f4f959ada35558305bbc822c0feb9da338bd1c74abd8b1636b4cc50e

Observation 31959962-6a31-4071-8b1a-b6c1bf7f08ae · outbound

This paper cites Fine-tuning Large Language Models for Domain-specific Machine Translation.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Fine-tuning Large Language Models for Domain-specific Machine Translation

Reference 66

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.869044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.869044Z digest=sha256:8b85331e848b673addc05c3ab8c4646e6de40bad13ea6e9460f7f744045eda4b

Observation bda1745a-4b41-4103-a7f4-c1e8355b43fc · outbound

This paper cites an unresolved cited work.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs Unresolved cited work

Reference 67

Resolution
unresolved
raw_fallback, observed 2026-08-11T12:17:57.375382Z

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=2026-08-11T12:17:56.872956Z digest=sha256:4f5f7a30e4cd2650a76037908825da7ecd97d7b8aafe66323b0137debed3aeac

Observation 69d8a12b-4e14-4481-acb8-1336695b4934 · outbound

This paper cites TrafficSafetyGPT: Tuning a Pre-trained Large Language Model to a Domain-Specific Expert in Transportation Safety.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs TrafficSafetyGPT: Tuning a Pre-trained Large Language Model to a Domain-Specific Expert in Transportation Safety

Reference 68

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.876510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.876510Z digest=sha256:f55d9b51df41229809a8bc86c21c0a31d483ab967cdc49e609a2fa807b4401ae

Observation b69eb500-fb4a-4b11-ac2f-e5208a58674e · outbound

This paper cites online" 'onlinestring :=.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs online" 'onlinestring :=

Reference 69

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.880801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.880801Z digest=sha256:53ae43d003cf7c1a32eb5f2a6fc52337e99c3573ddac6745767d5bfba4bdf039

Observation d2c9f6e7-4143-47d5-868d-4645b877299b · outbound

This paper cites write newline.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs write newline

Reference 70

Resolution
unresolved
no resolver link, observed 2026-08-11T12:17:56.885258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.885258Z digest=sha256:3ea132e2bc0ca555a0682edff139972e406e213726f69123bb8fff808334559a

Pith citing papers

Observation 73043e89-57d3-4b14-acf1-8c2cfbc9e2b4 · inbound

The Rise of Small Language Models in Healthcare: A Comprehensive Survey cites this paper.

The Rise of Small Language Models in Healthcare: A Comprehensive Survey All-in-One Tuning and Structural Pruning for Domain-Specific LLMs

Reference 121

Resolution
unresolved
no resolver link, observed 2026-08-16T10:51:04.861888Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:51:04.861888Z digest=sha256:fe7243d180a106c3735a0c75d50f358f97a3a5a2f480b5fca8b8ce836b45360b

Observation a68d6938-c538-4530-88c0-4d95d9b0da3a · inbound

Safe Screening Rules for Group SLOPE cites this paper.

Safe Screening Rules for Group SLOPE All-in-One Tuning and Structural Pruning for Domain-Specific LLMs

Reference 26

Resolution
metadata mismatch
local_arxiv, observed 2026-08-07T04:58:28.769663Z

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=pdf_text observed=2026-08-07T04:58:26.292793Z digest=sha256:b7833448b77c5d9489083d50c489f911c41f83b45a167cf7f7415753bf3a28d9