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Paper Citation Record · LEDGER

DLP: Dynamic Layerwise Pruning in Large Language Models

As of 22 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 6 inbound Pith citation observations for arXiv:2505.23807.

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

pith.paper-citation-record.v1
2505.23807 v3

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:51:18.841892Z

measured 27 of 27 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:38:42.797118Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T22:06:16.853228Z

Reference resolution

21 of 21 outbound references displayed

  • verified exact0
  • verified fuzzy1
  • unresolved17
  • parse uncertain0
  • malformed identifier2
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fcfa16eb-1240-4621-87d8-9da20f31b597 · outbound

This paper cites an unresolved cited work.

DLP: Dynamic Layerwise Pruning in Large Language Models Unresolved cited work

Reference 2

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unresolved
no resolver link, observed 2026-08-07T13:51:16.819301Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:51:16.819301Z digest=sha256:01dd912b210ef79311f35a181d6b2ac530b636189dc684c8b12eb3f75b7445f7

Observation ba49021a-2133-4585-a635-a82126d19c28 · outbound

This paper cites Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y ., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y ., Gonzalez, J.

DLP: Dynamic Layerwise Pruning in Large Language Models Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y ., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y ., Gonzalez, J

Reference 3

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source=pdf_text observed=2026-08-07T13:51:16.897840Z digest=sha256:cb0151965aeadb99d9002e61d0c9cc563cb4a547e7e0bb7448fa2c0b20e9651e

Observation 3005eb77-050b-427e-8a55-0aaa6172085e · outbound

This paper cites The Llama 3 Herd of Models.

DLP: Dynamic Layerwise Pruning in Large Language Models The Llama 3 Herd of Models

Reference 4

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no resolver link, observed 2026-08-07T13:51:17.011462Z

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source=pdf_text observed=2026-08-07T13:51:17.011462Z digest=sha256:47b02a832e872e1996e43b8dec4a4968844b20661bbf8a974c987ebe905652d5

Observation a7978c4a-400e-4969-a0b5-4a61f41d550f · outbound

This paper cites The Llama 3 Herd of Models.

DLP: Dynamic Layerwise Pruning in Large Language Models The Llama 3 Herd of Models

Reference 5

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no resolver link, observed 2026-08-07T13:51:17.130176Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T13:51:17.130176Z digest=sha256:7447925be3157307829d762f7ed85517f0376c5a0a329ef0d0844deea45518ae

Observation f806f274-382b-4650-88c7-eb6b5c29eb11 · outbound

This paper cites Not All Layers of LLMs Are Necessary During Inference.

DLP: Dynamic Layerwise Pruning in Large Language Models Not All Layers of LLMs Are Necessary During Inference

Reference 6

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source=pdf_text observed=2026-08-07T13:51:17.229319Z digest=sha256:4a2c35ae25181c4b59b176f7892a3682c5f0ccb67632be5b73af4abe5a92e752

Observation 90a30ad4-3fac-4dbe-9a1e-110bb598dee7 · outbound

This paper cites Mistral 7B.

DLP: Dynamic Layerwise Pruning in Large Language Models Mistral 7B

Reference 9

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no resolver link, observed 2026-08-07T13:51:17.549184Z

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source=pdf_text observed=2026-08-07T13:51:17.549184Z digest=sha256:92f46dfd71543322b39f6ed1f44debf8401d27cb23369f3943ebc14988358a6f

Observation 79d4dad0-dc46-4680-82f4-a7c3152bd126 · outbound

This paper cites Mistral 7B.

DLP: Dynamic Layerwise Pruning in Large Language Models Mistral 7B

Reference 10

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no resolver link, observed 2026-08-07T13:51:17.643903Z

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source=pdf_text observed=2026-08-07T13:51:17.643903Z digest=sha256:a8a67259205ad12df1375ce6bb4b098c3f58bdfd4e112c5d612991ff0a6c8d36

Observation 4e251b38-fbea-4c62-b83b-dec0611946c0 · outbound

This paper cites Sparse Fine-tuning for Inference Acceleration of Large Language Models.

DLP: Dynamic Layerwise Pruning in Large Language Models Sparse Fine-tuning for Inference Acceleration of Large Language Models

Reference 11

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local_arxiv, observed 2026-08-07T13:51:19.154402Z

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=2026-08-07T13:51:17.731192Z digest=sha256:9fb7b09700e69ec0c45c57b1ae430147f21f614440e30268399d643311f3ff2c

Observation 0594819d-9ba6-4134-94da-e66fbe7ffcbc · outbound

This paper cites MaLei at the PLABA Track of TREC 2024: RoBERTa for Term Replacement -- LLaMA3.1 and GPT-4o for Complete Abstract Adaptation.

DLP: Dynamic Layerwise Pruning in Large Language Models MaLei at the PLABA Track of TREC 2024: RoBERTa for Term Replacement -- LLaMA3.1 and GPT-4o for Complete Abstract Adaptation

Reference 12

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source=pdf_text observed=2026-08-07T13:51:17.864753Z digest=sha256:c11f875ab5ffa646ebecc22c7a8190971a22481dded87eb02108b6e36596ec93

Observation 3f4e342b-8fc9-45a4-af33-6eb3549365d5 · outbound

This paper cites MaLei at the PLABA Track of TREC 2024: RoBERTa for Term Replacement -- LLaMA3.1 and GPT-4o for Complete Abstract Adaptation.

DLP: Dynamic Layerwise Pruning in Large Language Models MaLei at the PLABA Track of TREC 2024: RoBERTa for Term Replacement -- LLaMA3.1 and GPT-4o for Complete Abstract Adaptation

Reference 13

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source=pdf_text observed=2026-08-07T13:51:17.990740Z digest=sha256:6049f1e047c35c5e0897fb071c6b6082752f87090ffeaef156bc34755398ae59

Observation 53fe0df1-233b-4a64-9f0a-36a8353e7885 · outbound

This paper cites GPT-4 Technical Report.

DLP: Dynamic Layerwise Pruning in Large Language Models GPT-4 Technical Report

Reference 14

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no resolver link, observed 2026-08-07T13:51:18.087996Z

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source=pdf_text observed=2026-08-07T13:51:18.087996Z digest=sha256:bfef2e1ccf51791648e45355645984ebfb324fdc8420433975d99e31b10cfbb4

Observation 6a8734c0-d11b-4bff-8986-d0d9cf74c830 · outbound

This paper cites Yin, L., Wu, Y ., Zhang, Z., Hsieh, C., Wang, Y ., Jia, Y ., Li, G., Jaiswal, A.

DLP: Dynamic Layerwise Pruning in Large Language Models Yin, L., Wu, Y ., Zhang, Z., Hsieh, C., Wang, Y ., Jia, Y ., Li, G., Jaiswal, A

Reference 17

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verified fuzzy
raw_fallback, observed 2026-08-07T13:51:20.416964Z

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=2026-08-07T13:51:18.352366Z digest=sha256:ee53a6a5dd76b4d82ac8396681e3045b5f2cb20e55e2620c9ad7001c3cb4bdd2

Observation bc1eccb3-8267-4639-8ad7-c79b6ce815ef · outbound

This paper cites URL https: //doi.org/10.1109/TCYB.2021.3124284.

DLP: Dynamic Layerwise Pruning in Large Language Models URL https: //doi.org/10.1109/TCYB.2021.3124284

Reference 19

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source=pdf_text observed=2026-08-07T13:51:18.581004Z digest=sha256:8be489bf8d1aee7ca156d079eb2b27f90cbaa814639b47061ceeeea1f91383c2

Observation 12ee1cd4-fa3f-4609-a4a3-b49aa1665771 · outbound

This paper cites This is likely because such an approach creates significant sparsity discrepancies between blocks, potentially disrupting inter-layer information flow.

DLP: Dynamic Layerwise Pruning in Large Language Models This is likely because such an approach creates significant sparsity discrepancies between blocks, potentially disrupting inter-layer information flow

Reference 31

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raw_fallback, observed 2026-08-07T13:51:19.810430Z

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=2026-08-07T13:51:18.841892Z digest=sha256:9bc1a99527a5b192c3723c46f290b51cbb0b44271a003a956d4f530252505f81

Observation ca10906a-5d7b-4e1c-a9e8-1f7b5eb56a31 · outbound

This paper cites 13 DLP: Dynamic Layerwise Pruning in Large Language Models A.

DLP: Dynamic Layerwise Pruning in Large Language Models 13 DLP: Dynamic Layerwise Pruning in Large Language Models A

Reference 2018

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raw_fallback, observed 2026-08-07T13:51:20.234206Z

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=2026-08-07T13:51:18.720117Z digest=sha256:3e82c48b51c45ff1ca24087b20d0c94bdd086e1b690110c5134d69979352114c

Observation a73e8638-a105-4b6b-a02e-9d8482454376 · outbound

This paper cites URL https:// doi.org/10.18653/v1/p19-1472.

DLP: Dynamic Layerwise Pruning in Large Language Models URL https:// doi.org/10.18653/v1/p19-1472

Reference 2019

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source=pdf_text observed=2026-08-07T13:51:18.481193Z digest=sha256:a823cae05e8f38b52bd4b3dcd919d0ff1754ff56586b60e5a8c4d05ec783c083

Observation 293f27bb-39de-4eb7-8ee7-3c93f54df463 · outbound

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

DLP: Dynamic Layerwise Pruning in Large Language Models GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 2022

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source=pdf_text observed=2026-08-07T13:51:17.458280Z digest=sha256:c472aa0c675f2a98c6470c1d84a5777d7a6045b81386d54a63f16371192be1a3

Observation 24413ff5-8a76-4c54-94b4-8da0e0de87c4 · outbound

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

DLP: Dynamic Layerwise Pruning in Large Language Models GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 2023

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source=pdf_text observed=2026-08-07T13:51:17.351636Z digest=sha256:caa4dee2e12d3fccdc16ebf098a1fb164c7ea8ddfdc062ee21e2f47d14aaf175

Observation 34fcf0e1-e5de-4062-b125-f622b2083517 · outbound

This paper cites Qwen Technical Report.

DLP: Dynamic Layerwise Pruning in Large Language Models Qwen Technical Report

Reference 2024

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source=pdf_text observed=2026-08-07T13:51:16.713194Z digest=sha256:52c45897b0b55c287251b9818e34f6db968c2d1e093bad635ba02ebb146a1bab

Observation e0173b36-efd0-4599-b5c1-baf8a5b3f955 · outbound

This paper cites v34i05.6399.

DLP: Dynamic Layerwise Pruning in Large Language Models v34i05.6399

Reference 6399

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source=pdf_text observed=2026-08-07T13:51:18.284394Z digest=sha256:30c640fc66e5562677a8a77a82ce35d813b74bc8b9e03b0efbee2a30a7108250

Observation a9d62be1-044f-4e92-b117-0b2be4d86817 · outbound

This paper cites doi: 10.1609/AAAI.V34I05.

DLP: Dynamic Layerwise Pruning in Large Language Models doi: 10.1609/AAAI.V34I05

Reference 8740

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source=pdf_text observed=2026-08-07T13:51:18.210956Z digest=sha256:d15054be62c0f936b28a0ef0fecff6f5b4c2ccc73f07f754fdb31a0abc8f1e08

Pith citing papers

Observation 6365b885-bf8f-4178-acba-b61f195f88e5 · inbound

SkipOPU: An FPGA-based Overlay Processor for Large Language Models with Dynamically Allocated Computation cites this paper.

SkipOPU: An FPGA-based Overlay Processor for Large Language Models with Dynamically Allocated Computation DLP: Dynamic Layerwise Pruning in Large Language Models

Reference 7

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source=pdf_text observed=2026-08-02T18:16:45.159255Z digest=sha256:c9cc64af2b4dde52ce2b50a3c6c8dc426508783bcd52550146b7b9e2cea2ef93

Observation 70f8d530-d2fc-4d83-910c-d9972f021ea3 · inbound

SimDiff: Depth Pruning via Similarity and Difference cites this paper.

SimDiff: Depth Pruning via Similarity and Difference DLP: Dynamic Layerwise Pruning in Large Language Models

Reference 11

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verified exact
arxiv_id, observed 2026-05-11T13:16:22.196994Z

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=2026-05-10T02:00:55.378631Z digest=sha256:5b994f9f139c766e6e600c4b07341950add091f0de3c44c33577d109af383647

Observation b7fee7ae-aaba-41f0-8e30-13c94137820a · inbound

CRePE: Convolution-aware Relative Importance in Post-training Pruning with Efficient Search cites this paper.

CRePE: Convolution-aware Relative Importance in Post-training Pruning with Efficient Search DLP: Dynamic Layerwise Pruning in Large Language Models

Reference 6

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arxiv_id, observed 2026-07-01T22:06:16.854657Z

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=2026-06-28T15:43:35.063909Z digest=sha256:05ae9b1e4b93b28d0f1a324ddc82c4a3b2c4c32cf036e15774539cddd47bdd11

Observation d4b2da36-89eb-4063-ac0f-ab997de69602 · inbound

Are the High-weight Neurons the Important Ones in Image Classification Neural Networks? cites this paper.

Are the High-weight Neurons the Important Ones in Image Classification Neural Networks? DLP: Dynamic Layerwise Pruning in Large Language Models

Reference 36

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source=pdf_text observed=2026-08-01T02:17:12.213712Z digest=sha256:569a73f3e1e97444600d733f3d01df1065879bc8b14b04283b48d3d2117a0605

Observation c3eb4117-dfb7-4d2c-add0-b5367a52c0d6 · inbound

SlimVLM: Sensitivity-aware Dynamic Structured Pruning with Adaptive Visual Token Selection for Efficient Vision-Language Models cites this paper.

SlimVLM: Sensitivity-aware Dynamic Structured Pruning with Adaptive Visual Token Selection for Efficient Vision-Language Models DLP: Dynamic Layerwise Pruning in Large Language Models

Reference 21

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source=pdf_text observed=2026-08-05T16:41:28.841625Z digest=sha256:cc999cf4df8c266d9dc582662a884b338b59cdd9c36964c8ff4e723bb9156789

Observation 95e14cae-67b8-43c2-b501-c11da33b3dbe · inbound

Understanding Calibration and Truncation Error Propagation in Training-Free Low-Rank Compression for LLMs cites this paper.

Understanding Calibration and Truncation Error Propagation in Training-Free Low-Rank Compression for LLMs DLP: Dynamic Layerwise Pruning in Large Language Models

Reference 49

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T04:38:42.797118Z digest=sha256:37a0682d384f64e5979c8656de0da49c64409b35e875b46f421805c6c0f6ec22