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Paper Citation Record · LEDGER

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

As of 12 August 2026, this Paper Citation Record lists 70 of 70 outbound references and 1 inbound Pith citation observation 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 71 of 71 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:58:26.292793Z

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

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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:f4233037f929f88e94040af001953aa53303d16358f148332f360663e0a3885e

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.616062Z digest=sha256:23e154907f07c347a46036278fe157f9fd173808dc3dd51dfbc0a5bf83de604f

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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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=sha256:6c324619ac6252685d5c2894de76dd40d5894f1430271b575ee906bbc85d6f37

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

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

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.623947Z digest=sha256:48665e48e3a110ba30166d88dfa1e9fa7850c9db327b1c80ae6c1587ccc4ba38

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 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T12:17:56.628188Z digest=sha256:cebade38847d63f93ef8cf7b11314a3478ca95d2d444c804c29855a73ac5322a

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 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T12:17:56.631928Z digest=sha256:94ab1e9a009e8c3bc5be30c7266ea3caf57d9be55ac1c426be622f0f211d2a6a

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

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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:e4dba5b6821738cd68b2c58964f60283e397340725572ab21cfd727f624b3047

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
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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 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T12:17:56.639859Z digest=sha256:230f0332b099e6c8a96969721460e7cec0f8cb9fffde74c89d725b660a5bd7ba

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:b20c4dc132a6a2e997265ca81b9e8025138bfb4e993b1cff86ab13df23a93c07

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 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T12:17:56.648381Z digest=sha256:5cc6b2bb391bcbc05180aa2b136f9f6fbc20dd3dffa668887103b49c6a2571fe

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:17:56.651892Z digest=sha256:903d77a85b7ebfccd6b950474336de1ab868e002fbbe4064950835278f6f7073

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

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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:d0ae3e7436e5729d2e1b65060ef74f07054014b037a1732e87a93a6b4283a7ec

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

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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:fb1d56770464f5766ae63c6be12f998496edae4faefb073d3bba599ac3bdef9c

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 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T12:17:56.663320Z digest=sha256:7fc934ad453fa6d9c5a53835c937eb7670f365956cc213576239c8668d369fad

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
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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=sha256:160cff06d5d309b8727117d36527d7da9061b6f86fc7c3dd51ac4f137fcb0c3e

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

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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=sha256:95834e100caa6089af07877af4bb3a4f82f7d92f8c7c16079f106d7df54845ee

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

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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:f6e088cd7f6031fdbc8f1f0ee2c1543f5904279119df842aca8d82a2336e7722

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
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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:bdbb2a297e578ce12eab42f55d02c8a07b4ea6c28f453539643cc17944328070

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
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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=sha256:6fb76b3abe6edebbf6c22baaf04912e442ebb7e4497a960a8c72438c35225935

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

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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=sha256:080d488789402e9eb1938fe690225fd5dd0c30a68fac141276bfd0ea9feae5f0

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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unresolved
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=sha256:0ea2c74dc2be62d67cd85aac1c0ae81ed5c6cebe97c3a74d32f1bacfb30b922a

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=sha256:7cebfc685119b979ff45973b9f19d903e0ae8098b236d1a18be8649a45c0bdaa

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 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T12:17:56.701608Z digest=sha256:906e1f09b14cc4ab0b45c1dbae8c733ebbcf730a2feb8d4465515d96e571b1f4

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:a9a52294837bbf4767d65cacea0f281b908b192def4df287297485e44916974a

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=sha256:3bd7459526ebb2f02b67ab7a5cc32edda0a1f6ca0c3d3c52fdcd026613cc3288

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

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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=sha256:6391d1274c91be8d3043b55f8191aa5b103dfafec916bee46511960c32507084

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

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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:e7c6f5342c084a383be50abd478e49e600c83c99594c425ca586530972a6ffd4

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 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T12:17:56.720546Z digest=sha256:fe0b98625ce7f24a1d9513136d622853b6308d5b04a31e9a4fcd13d888677180

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=sha256:34a56ac635eca41bbc13d200aa7074215a1b5f401f6c5b4a394705fad3cc2b0d

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:db1ca9f037f405bcc18d55788460ff2b7020d19fef7113152f1da866c492ff44

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:aa2191e36e471034ca3e16a8ce4602ceb3388582ea16855323361a8fdc72ec65

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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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=sha256:369ba0571633881e3e0b403d24d370431099ac025901cf0d71bbf04f534eb866

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
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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:e2f021de90a5256d08c86a08a0ef584322614624b26f4a0346a28e35722bf75f

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=sha256:7eafc5e19de34d08acfe8ee01b1b73d538cde4b0569b04f49305172108cbd717

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

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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=sha256:1ebd5aabc9c448de6ff941c2ab16b5b27a22fae7b5dd66810f3388bfab312516

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

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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=sha256:4faf04f3627254082de530fc42a9f0c1065268c635efff9419b1c2ad2ccf09ab

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

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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=sha256:469475457c309a794cd1e6142211aae652a6ea0ce7289748ed17a9740e4e98d6

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:bbb81388edac957e2f155ae0faa5872b7a38b9c93430fba5b0367b042a486c4d

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=sha256:2e340e5c3a5b8dd5eb14cc449c3b6e02ac856ca6f3004ec19d6f24a4f775ba55

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=sha256:974463f16f4006ab4d308ef78964dc2a2f67d140740b44ae605a5fe7da3edca9

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=sha256:3cb5f5257537a918fff149bf339097388f2e611e46de7361c73fb01c53965afb

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 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T12:17:56.775807Z digest=sha256:b831375e9b97a9a16de69c41b70f617fc6310dd56a20adef28e9de624a73a28f

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=sha256:6b79a1b4c75a1dd6906af1d83063d84fd0bce1ba864b4d113cbe115c8860eb2a

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:c80199e0899938ae1a40ae30dd5b984f34d810ba9179135bcd4e5fb433fc0a20

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:b4803ce16be0fc08f3110dac66e69b2053e989ddada1ea9dd2eef9670039146e

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 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T12:17:56.791016Z digest=sha256:2d3ad0785720a0e2159867ae8b0b3d494c8e2cfafddeef43f77e9cbf005d8472

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 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T12:17:56.794530Z digest=sha256:3df58d7b55dba77c30bffb852fcfc40b25cc6aac017355cb591f1ae0653d4dff

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 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T12:17:56.797876Z digest=sha256:6bfc47d7eec89fff73f4f623747bedc9d2217b621ce54323cf1a572e056239c9

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 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T12:17:56.801242Z digest=sha256:3d76733f72bb0156121933724cd07397e765361b096f7d5547e2c1bb918b1d41

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 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T12:17:56.804796Z digest=sha256:b537bf20225a734038001999b2a4fe623907b05bb1bde531769114e9d9070f7c

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 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T12:17:56.808279Z digest=sha256:c290bfb7882b2e7a7529d6433539ef3c7fd3883b97496ebc3131e00be0d5162a

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 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T12:17:56.811943Z digest=sha256:28bf33e37d0d66c6f82fa1e0114b4ef500228feb3c8352a374f98210f2cfdddf

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 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T12:17:56.816040Z digest=sha256:beb79d74f186e62f73902406349af76058f86e784c27c1e19f6887c3df0d6189

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 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T12:17:56.820161Z digest=sha256:4cc303e7ffa4f248bd571c093e8cb7598bb2faa930afc90f352137283e057ab8

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 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T12:17:56.824311Z digest=sha256:f11936cfe30cfa878cfb4c30b956e82dbb16ecb3e9b0a9238f217aa4d1e29511

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 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T12:17:56.828040Z digest=sha256:aaf06cc914909f37be08ce61e3f9c6c9ce10dc6e504435976360faef1c949034

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 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T12:17:56.831708Z digest=sha256:5c35874c36c65aa14259041bcc47cd2948dd1cda718a043c97479fcdfe5f6c1c

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 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T12:17:56.836714Z digest=sha256:bd92dbc678bae5c5a94e9bbf3112c0bcece9faed849dc605a71831139356752c

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:a0908d1bb8edf2c68ad09cbfd16f8942d1f344eb82a8d633237f9c235504af15

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:a0f8b1ea9e6942e9a3e00513aba6005b6f83412ea09737fda6c93fa585504527

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=sha256:498a4add1e641c4dbef19103b23c45a4aaef61c5195611c2bafcea68011ab82f

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=sha256:155031ed244b033a2eeb639d476ee0e91fa7e878529ce5ce4eca17e0801403cb

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 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T12:17:56.856955Z digest=sha256:8ea50723eccbd5084460b875c1bdfceae59d89bc29f7f265a65c59a015e1f1f0

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 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T12:17:56.860220Z digest=sha256:5a5f02f81284d93620312516c508e2cba8d4f4f058dec055d5e13e23cb303e4c

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 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T12:17:56.865294Z digest=sha256:205ef1f1047a85ccee738d738d70eba5f7aeea402b46904c6b03f0f02df94603

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:d438e21f649776794f3e3ae5cda755445380a2ff4b48d090d096e16bd37d5af2

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 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-11T12:17:56.872956Z digest=sha256:b138a11a0d76478c57eced69b2b828d8171ccde0dee5ca641dbd49b24a305461

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=sha256:558da59458907af04383171e871c8880ade15cc0ae54e13a74652de619c3e8d6

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=sha256:386ca0d9cc90fda73db5b3c43c8ac5ec6d9a664d1b18e486302d05b9b2544797

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=sha256:0b4f6821ed8769eed0d803c8ddcd16a8fd40f26a4b2f7a2f85ee0007ff49acb2

Pith citing papers

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-07T04:58:26.292793Z digest=sha256:aacb9ecfdb2e5a4d1f7117e2a849d265d64d830afc9fca09d5ca81bc890902b1