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Paper Citation Record · LEDGER

Adaptive Pruning for Large Language Models with Structural Importance Awareness

As of 11 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2412.15127.

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

pith.paper-citation-record.v1
2412.15127 v1

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:40:41.686204Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

49 of 49 outbound references displayed

  • verified exact3
  • verified fuzzy19
  • unresolved27
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 073edcdf-dc7e-475e-87c0-d039b834d1a9 · outbound

This paper cites LaMDA: Language Models for Dialog Applications.

Adaptive Pruning for Large Language Models with Structural Importance Awareness LaMDA: Language Models for Dialog Applications

Reference 1

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Observation fc28afb6-5268-47de-94c9-71d4c68310dc · outbound

This paper cites A survey on large language models: Applications challenges limitations and practical usage,.

Adaptive Pruning for Large Language Models with Structural Importance Awareness A survey on large language models: Applications challenges limitations and practical usage,

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T11:40:41.458793Z digest=sha256:5ff8db8a4e118b9fc219f943886075dd7f77ee45dfd552d04652c19c3aa32fc1

Observation 441ab76e-2d40-4f1d-837b-4e5220518175 · outbound

This paper cites Emergent Abilities of Large Language Models.

Adaptive Pruning for Large Language Models with Structural Importance Awareness Emergent Abilities of Large Language Models

Reference 3

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source=pdf_text observed=2026-08-11T11:40:41.464376Z digest=sha256:57c439ec50fb2e69a048d85d1dba9473f72f72f909a18d41a768873b87edb820

Observation 85410af1-fd87-4a18-87ac-2b5caedb1fe4 · outbound

This paper cites Toward Democratized Generative AI in Next-Generation Mobile Edge Networks.

Adaptive Pruning for Large Language Models with Structural Importance Awareness Toward Democratized Generative AI in Next-Generation Mobile Edge Networks

Reference 4

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source=pdf_text observed=2026-08-11T11:40:41.469540Z digest=sha256:c2b9627a491e22a838077b87c99683724f742dc247cced7839ae0695f5089931

Observation 2e53841e-28dd-4376-97f4-b7979fe2438b · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

Adaptive Pruning for Large Language Models with Structural Importance Awareness Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 5

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-11T11:40:41.474247Z digest=sha256:3abbc8510b39b56928567c09e356794a56f226bf25662bca2f86c633edfd4ff9

Observation a4e5ffac-7606-4a52-b881-6b0ddd144b97 · outbound

This paper cites The Internet of Things in the Era of Generative AI: Vision and Challenges.

Adaptive Pruning for Large Language Models with Structural Importance Awareness The Internet of Things in the Era of Generative AI: Vision and Challenges

Reference 6

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-11T11:40:41.479197Z digest=sha256:2b803c5e744239b0d07bdc8f11a9466b25a472402f10defbc597bf911b73eda0

Observation 14ea9a7b-1394-471f-a9f5-c15cbf9b7f43 · outbound

This paper cites An Overview of Neural Network Compression.

Adaptive Pruning for Large Language Models with Structural Importance Awareness An Overview of Neural Network Compression

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:40:41.484297Z digest=sha256:3a8abddb1d3dea04e3e4c4b9ee9fd766368a38230e8095dd633b09d7b8288f51

Observation 32605587-ea3b-4149-b7b3-719e86068f08 · outbound

This paper cites Structured Model Pruning of Convolutional Networks on Tensor Processing Units.

Adaptive Pruning for Large Language Models with Structural Importance Awareness Structured Model Pruning of Convolutional Networks on Tensor Processing Units

Reference 8

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local_arxiv, observed 2026-08-11T11:40:42.067202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T11:40:41.489115Z digest=sha256:9a298dfb79eb7f31249808491d38338613d32a5213fcdc90362a47de182abd1d

Observation 13448688-33b4-45c2-937f-ef99db3c1537 · outbound

This paper cites Personalized federated learning by structured and unstructured pruning under data heterogeneity,.

Adaptive Pruning for Large Language Models with Structural Importance Awareness Personalized federated learning by structured and unstructured pruning under data heterogeneity,

Reference 9

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raw_fallback, observed 2026-08-11T11:40:42.384211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T11:40:41.493311Z digest=sha256:6b22dd394f6e37b79239aeee3649c647f6fa000758fef791eedb7f5452477571

Observation 5cc73ea6-3d05-4c43-926e-fa4aebd18140 · outbound

This paper cites Generative AI agents with large language model for satellite networks via a mixture of experts transmission,.

Adaptive Pruning for Large Language Models with Structural Importance Awareness Generative AI agents with large language model for satellite networks via a mixture of experts transmission,

Reference 10

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raw_fallback, observed 2026-08-11T11:40:42.372928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T11:40:41.497273Z digest=sha256:941a64b150eddc27176e85e6b7dcc0f8c502163690de3451bbb96fa5b699896e

Observation 7636205e-bf7f-47c6-b8b1-01925ea1c075 · outbound

This paper cites Optimal brain damage,.

Adaptive Pruning for Large Language Models with Structural Importance Awareness Optimal brain damage,

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T11:40:41.501522Z digest=sha256:3f03dacfd65c004b08e9b32aca578f865d5f53e7ac382324ca06590db4b13398

Observation 68a5ce05-739e-4b1b-a5ff-79d542b52cef · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

Adaptive Pruning for Large Language Models with Structural Importance Awareness Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:40:41.505716Z digest=sha256:1e61e14c4b6835334c89fe2e1b79c908243c45ee12bc071727980d69b0a8cc24

Observation 65279d03-b12d-495d-a2f5-a655726d0313 · outbound

This paper cites The design and implementation of xiaoice an empathetic social chatbot,.

Adaptive Pruning for Large Language Models with Structural Importance Awareness The design and implementation of xiaoice an empathetic social chatbot,

Reference 13

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raw_fallback, observed 2026-08-11T11:40:42.349611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T11:40:41.509944Z digest=sha256:721c9ddb3df56f1681e00dcf1f66b344d85088d36e0b97ff634299cafdb657fe

Observation 057a39e1-12d9-408d-8c2d-6101c497cbeb · outbound

This paper cites Interactive AI with retrieval-augmented generation for next generation networking,.

Adaptive Pruning for Large Language Models with Structural Importance Awareness Interactive AI with retrieval-augmented generation for next generation networking,

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T11:40:41.514752Z digest=sha256:8cc1761f8d7e2b3f81264663bad18f0e24aed11d3b4e11d1662bd6f116c488f9

Observation 4f3bd275-67b9-443a-8fae-6d9b95763df9 · outbound

This paper cites Dynamic Sparse Graph for Efficient Deep Learning.

Adaptive Pruning for Large Language Models with Structural Importance Awareness Dynamic Sparse Graph for Efficient Deep Learning

Reference 15

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local_arxiv, observed 2026-08-11T11:40:42.025553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T11:40:41.518983Z digest=sha256:54e2872de735c10d4069c4bade91793ea959b576795ec710e9c9e3da5bdd3af8

Observation 557fb637-0df5-4f22-8163-ee05a4c2271c · outbound

This paper cites ZipLM: Inference-aware struc- tured pruning of language models,.

Adaptive Pruning for Large Language Models with Structural Importance Awareness ZipLM: Inference-aware struc- tured pruning of language models,

Reference 16

Resolution
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raw_fallback, observed 2026-08-11T11:40:42.321580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T11:40:41.523441Z digest=sha256:3aa39c7a8df3f671a6d81c1fde62e4f930a17c478e1c68ac01b0cd2a8a33d54f

Observation ba7fd398-c25e-47f8-a622-a99d64d335bb · outbound

This paper cites Fluctuation-based adaptive structured pruning for large language models,.

Adaptive Pruning for Large Language Models with Structural Importance Awareness Fluctuation-based adaptive structured pruning for large language models,

Reference 17

Resolution
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raw_fallback, observed 2026-08-11T11:40:42.306752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T11:40:41.527748Z digest=sha256:fac61d80a1b2ef7701e92d4ac451e42c018d4c8d55be85dc1fa9564598cbe7bf

Observation 51998fd5-72e1-4bfe-9335-32dfd11a0b93 · outbound

This paper cites Llm-pruner: On the structural pruning of large language models,.

Adaptive Pruning for Large Language Models with Structural Importance Awareness Llm-pruner: On the structural pruning of large language models,

Reference 18

Resolution
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raw_fallback, observed 2026-08-11T11:40:42.294340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T11:40:41.531979Z digest=sha256:54af07c71e2eed30bedcc11d042dc5aad38fcee42a329266a80fac3a78d60ff8

Observation ba84c68a-cd27-4a91-97fc-d1bf73f2be5b · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Adaptive Pruning for Large Language Models with Structural Importance Awareness LLaMA: Open and Efficient Foundation Language Models

Reference 19

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:40:41.536270Z digest=sha256:2c96a2daf16b3bee8b816e377eef4aab90ed13033c9ddf129301e9e846d16b4b

Observation 0322c16c-379e-439f-8abf-0a8e32f42e42 · outbound

This paper cites Vicuna: An open-source chatbot impressing GPT-4 with 90%* ChatGPT quality.

Adaptive Pruning for Large Language Models with Structural Importance Awareness Vicuna: An open-source chatbot impressing GPT-4 with 90%* ChatGPT quality

Reference 20

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raw_fallback, observed 2026-08-11T11:40:42.281684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T11:40:41.541285Z digest=sha256:754ef4d23766edb1de172c669be8f826af1726bb26fbc76b4b6b65f8cacb6945

Observation 70d5377f-2ac2-48cb-9dc4-7bc0009de8c8 · outbound

This paper cites GLM: General Language Model Pretraining with Autoregressive Blank Infilling.

Adaptive Pruning for Large Language Models with Structural Importance Awareness GLM: General Language Model Pretraining with Autoregressive Blank Infilling

Reference 21

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:40:41.546393Z digest=sha256:8c9b8ee18d3305f85695e19869fd4ffe98c044ed71704e80fd06a13d1f967cdb

Observation 5029b7f6-781b-464d-bba8-13c6e11480cc · outbound

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

Adaptive Pruning for Large Language Models with Structural Importance Awareness A Simple and Effective Pruning Approach for Large Language Models

Reference 22

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:40:41.551151Z digest=sha256:e2e648f180b81c817071b68461be3c693378161973d114bfbd292dd032d1709c

Observation fd86edbd-afe3-4e80-9515-3aa0d252ae93 · outbound

This paper cites LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery.

Adaptive Pruning for Large Language Models with Structural Importance Awareness LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery

Reference 23

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source=pdf_text observed=2026-08-11T11:40:41.556567Z digest=sha256:a7813e3299a5a2ab221b8e27da9288330ced4c0a2764ed3086f57339b0b70286

Observation 2e640b37-f791-430c-a7b6-7e6cfb912106 · outbound

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

Adaptive Pruning for Large Language Models with Structural Importance Awareness Sparsegpt: Massive language models can be accurately pruned in one-shot,

Reference 24

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raw_fallback, observed 2026-08-11T11:40:42.268925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T11:40:41.562304Z digest=sha256:c825bef33ed03841418b6723203f94eef136f9a79bdd45dac9fcf5c1c0c64a1d

Observation 95f21e58-03fb-4d9a-a45b-125bb377c610 · outbound

This paper cites Pruning Large Language Models via Accuracy Predictor.

Adaptive Pruning for Large Language Models with Structural Importance Awareness Pruning Large Language Models via Accuracy Predictor

Reference 25

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verified exact
local_arxiv, observed 2026-08-11T11:40:41.959346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T11:40:41.566683Z digest=sha256:0e4320061073588469e3cdb94c03c2579b7fe33d6c5e3b3e2ce6bda34e1c79bf

Observation 9188a738-4415-480a-8953-5da0b3cc5094 · outbound

This paper cites Shortened LLaMA: Depth Pruning for Large Language Models with Comparison of Retraining Methods.

Adaptive Pruning for Large Language Models with Structural Importance Awareness Shortened LLaMA: Depth Pruning for Large Language Models with Comparison of Retraining Methods

Reference 26

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:40:41.572185Z digest=sha256:1c544a5a8ee4849dfc5fd36ed479b8124f6b07a30ca011250b5ace0c752734f1

Observation b459aaff-7bd0-4e27-8a53-9cb80e905329 · outbound

This paper cites MINI-LLM: Memory-Efficient Structured Pruning for Large Language Models.

Adaptive Pruning for Large Language Models with Structural Importance Awareness MINI-LLM: Memory-Efficient Structured Pruning for Large Language Models

Reference 27

Resolution
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no resolver link, observed 2026-08-11T11:40:41.576792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:40:41.576792Z digest=sha256:d86387662592603393ce318761fbc712dc0138d4c918e5ac3d63c60f67b7ada8

Observation ef2647dc-de31-4975-92cf-0b3532afae0a · outbound

This paper cites EDGE-LLM: Enabling Efficient Large Language Model Adaptation on Edge Devices via Layerwise Unified Compression and Adaptive Layer Tuning and Voting.

Adaptive Pruning for Large Language Models with Structural Importance Awareness EDGE-LLM: Enabling Efficient Large Language Model Adaptation on Edge Devices via Layerwise Unified Compression and Adaptive Layer Tuning and Voting

Reference 28

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no resolver link, observed 2026-08-11T11:40:41.581736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:40:41.581736Z digest=sha256:bfe90ee25c2dd39adeca9e7cc154930980c786bd8327b5ea96e2d50c6661549f

Observation 70c6fafe-75d3-49f8-add8-3a0de9d099d9 · outbound

This paper cites AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models.

Adaptive Pruning for Large Language Models with Structural Importance Awareness AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models

Reference 29

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no resolver link, observed 2026-08-11T11:40:41.586478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:40:41.586478Z digest=sha256:ab938e3078946e937e7a564037b17b4879d3f2b7c348734013e5072ee8fd7c65

Observation 79cf3993-c5f9-42d9-97d4-bb5bd7a00855 · outbound

This paper cites Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey.

Adaptive Pruning for Large Language Models with Structural Importance Awareness Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 30

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no resolver link, observed 2026-08-11T11:40:41.593375Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:40:41.593375Z digest=sha256:69f4c70ce65a64830189dc31720fe3e6fa2c85a118ef1ce273ecfe6e6a5b3eb6

Observation 95633254-7792-4b09-bcff-3ce9b5407997 · outbound

This paper cites Parameter-efficient transfer learning for NLP,.

Adaptive Pruning for Large Language Models with Structural Importance Awareness Parameter-efficient transfer learning for NLP,

Reference 31

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verified fuzzy
raw_fallback, observed 2026-08-11T11:40:42.256256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T11:40:41.599065Z digest=sha256:fea71efa03bc4ff734b8876921b5bf7d3237e081638e64613225a9b3084c4365

Observation 531f9cbf-895a-4675-aa7c-5db333386c2a · outbound

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

Adaptive Pruning for Large Language Models with Structural Importance Awareness LoRA: Low-Rank Adaptation of Large Language Models

Reference 32

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no resolver link, observed 2026-08-11T11:40:41.603938Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:40:41.603938Z digest=sha256:d5ed1e44391499e0f6ba3c1973a4bdd1cb95febe6797eedaefefeddc126f9652

Observation 1c92b357-3bb3-432a-8dd5-b846009c0fdd · outbound

This paper cites Qlora: Efficient finetuning of quantized llms.

Adaptive Pruning for Large Language Models with Structural Importance Awareness Qlora: Efficient finetuning of quantized llms

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:40:42.241965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T11:40:41.608652Z digest=sha256:2b973b7d47024e75d94b2c192cd4176b8bafcce5754a706a8419b27b441ebbe2

Observation f8bf1718-5c81-42fd-9c73-30a66ac0168b · outbound

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

Adaptive Pruning for Large Language Models with Structural Importance Awareness A fast post-training pruning framework for transformers,

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:40:42.227619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T11:40:41.613084Z digest=sha256:87b5f6a52a6556fc2d647b50793297b12757b43027843a2309f12bd75e0cd52a

Observation a5debc4e-cfba-418f-9e4f-b71da7c164ab · outbound

This paper cites UPop: Unified and progressive pruning for compressing vision-language transformers,.

Adaptive Pruning for Large Language Models with Structural Importance Awareness UPop: Unified and progressive pruning for compressing vision-language transformers,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:40:42.213326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T11:40:41.618157Z digest=sha256:ad73b9f4357841b1ac2263254d54fc542e9e4998c91c674d539153d259699e57

Observation 50d3ed81-b638-4028-bb1f-0633cab759f4 · outbound

This paper cites Polynomial-type Lya- punov–Krasovskii functional and Jacobi–Bessel inequality: Further re- sults on stability analysis of time-delay systems,.

Adaptive Pruning for Large Language Models with Structural Importance Awareness Polynomial-type Lya- punov–Krasovskii functional and Jacobi–Bessel inequality: Further re- sults on stability analysis of time-delay systems,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:40:42.198453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T11:40:41.622522Z digest=sha256:45bb1032c61cd35e9d404061c68276737958e54b9db98b26426c66607e6c1a12

Observation e0eaf831-e285-4543-ba80-c97e9418c190 · outbound

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

Adaptive Pruning for Large Language Models with Structural Importance Awareness Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-11T11:40:41.626794Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:40:41.626794Z digest=sha256:72e53d0fea1a61a182edf4bf1408bfc3554022fdfc31494c357c382d59c390e5

Observation cf19a336-786d-4ff3-83ec-1f7202f2d6fd · outbound

This paper cites An empirical study of LLaMA3 quantization: from LLMs to MLLMs.

Adaptive Pruning for Large Language Models with Structural Importance Awareness An empirical study of LLaMA3 quantization: from LLMs to MLLMs

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-11T11:40:41.631116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:40:41.631116Z digest=sha256:502304d0ac53223139b8fa05a87391c4e227b0bf38274defe486c62b2ae43f67

Observation 6f4d5a8d-c23e-4369-843d-624e53216ca8 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Adaptive Pruning for Large Language Models with Structural Importance Awareness Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-11T11:40:41.635431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:40:41.635431Z digest=sha256:c6638b3bb3f3171c99bd16bdfb76145d2fe81cb43e437a524986efc7787875d9

Observation 4ca98292-ee6b-4efb-b9cd-7a8a15bbf069 · outbound

This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

Adaptive Pruning for Large Language Models with Structural Importance Awareness BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-11T11:40:41.640905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:40:41.640905Z digest=sha256:e48068aa822553da9d07878e594d15db4abc1cdbfd9d09625c50ff8d8933ed1b

Observation 6296675f-c466-4244-b6b1-500e43d12c84 · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

Adaptive Pruning for Large Language Models with Structural Importance Awareness HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-11T11:40:41.646012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:40:41.646012Z digest=sha256:40213019eb10770b37a357e5b02be7e9e8da7ee106668ede5a67a7b32d38bb31

Observation 53a38563-41af-40de-99a5-862fa485a99f · outbound

This paper cites Piqa: Reasoning about physical commonsense in natural language,.

Adaptive Pruning for Large Language Models with Structural Importance Awareness Piqa: Reasoning about physical commonsense in natural language,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:40:42.184979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T11:40:41.651670Z digest=sha256:896e22a0800073040281d74bfa4d301a33e82c1bb156b1ba7804b87f8646765b

Observation 9f274ee5-694a-4e66-ab61-501d6df4867a · outbound

This paper cites Winogrande: An adversarial winograd schema challenge at scale,.

Adaptive Pruning for Large Language Models with Structural Importance Awareness Winogrande: An adversarial winograd schema challenge at scale,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:40:42.170890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T11:40:41.656677Z digest=sha256:e05e36e6218569add8dc580619951058e48f68065c74b986c78079c18d546adc

Observation 75438ffa-64d2-4ce7-9a52-ae66b0fb40c3 · outbound

This paper cites Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering.

Adaptive Pruning for Large Language Models with Structural Importance Awareness Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-11T11:40:41.660706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:40:41.660706Z digest=sha256:eb27189bcb0d84fe93767185d481e2f5d4acd371f6327055639b72dc135715aa

Observation 3b8769b0-e9ac-4723-b3aa-5f4e1d2bc212 · outbound

This paper cites Building a large annotated corpus of English: The penn treebank,.

Adaptive Pruning for Large Language Models with Structural Importance Awareness Building a large annotated corpus of English: The penn treebank,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:40:42.156814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T11:40:41.666244Z digest=sha256:1da8b75d3e535141fb3e0dc4c83c5bc4d18b7fc864e584ac95be9cca294427e9

Observation 7a25c146-ac19-466a-b943-83a9ea2d0839 · outbound

This paper cites Pointer Sentinel Mixture Models.

Adaptive Pruning for Large Language Models with Structural Importance Awareness Pointer Sentinel Mixture Models

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-11T11:40:41.671967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:40:41.671967Z digest=sha256:6d37714635cb9bccc27f494d9f537baa6485c39db8768bc8aae323e68543b1de

Observation fb3df22c-ffb6-41ab-907b-54d2ed26202c · outbound

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

Adaptive Pruning for Large Language Models with Structural Importance Awareness LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-11T11:40:41.677113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:40:41.677113Z digest=sha256:6a98f43e5a294b476faec36e05694691a40654917c5d483fdc29129db662a676

Observation 125a3234-417a-44e4-9eb1-f28ab8cab719 · outbound

This paper cites Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books.

Adaptive Pruning for Large Language Models with Structural Importance Awareness Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-11T11:40:41.682230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:40:41.682230Z digest=sha256:2286aa7221761554914639ac13b3b674bc7e53945f86778286b0859afcc8fce8

Observation 457bb50a-2c30-4a43-990d-1efd9bf03ba3 · outbound

This paper cites WizardLM: Empowering large pre-trained language models to follow complex instructions.

Adaptive Pruning for Large Language Models with Structural Importance Awareness WizardLM: Empowering large pre-trained language models to follow complex instructions

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-11T11:40:41.686204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:40:41.686204Z digest=sha256:94d1384c82dd6b1f9143330b09ff589af3dd30227bfa867232be16c0d0c69c1e

Pith citing papers

No inbound Pith citation observations are available.