Paper Citation Record · LEDGER

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models

As of 23 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 1 inbound Pith citation observation for arXiv:2506.11120.

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

pith.paper-citation-record.v1
2506.11120 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:18:00.945501Z

measured 51 of 51 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T03:11:23.755739Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T11:36:55.530603Z

Reference resolution

50 of 50 outbound references displayed

verified exact0
verified fuzzy27
unresolved22
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8812d6d3-7c5e-40da-bfcc-9d79c5dbb4b9 · outbound

This paper cites The Falcon Series of Open Language Models.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models The Falcon Series of Open Language Models

Reference 1

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Source-reported events for the cited work

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Observation e7b9fcf5-bad2-4041-9c7b-a2624ddeca42 · outbound

This paper cites Croci, Marcelo Gennari do Nascimento, Torsten Hoefler, and James Hensman.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Croci, Marcelo Gennari do Nascimento, Torsten Hoefler, and James Hensman

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:01.830614Z

Source-reported events for the cited work

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

Observation 625657ed-3f20-46a8-bea3-6a25380d045a · outbound

This paper cites Qwen technical report, 2023.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Qwen technical report, 2023

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:01.813948Z

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Observation e2f66436-8a7b-454e-a4be-f38f3def90df · outbound

This paper cites Pythia: A suite for analyzing large language models across training and scaling.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Pythia: A suite for analyzing large language models across training and scaling

Reference 4

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Observation 25aaba19-f0c7-4f21-afb6-b08f48dce34e · outbound

This paper cites Piqa: Reasoning about physical common- sense in natural language.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Piqa: Reasoning about physical common- sense in natural language

Reference 5

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Source-reported events for the cited work

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Observation 1659951e-c763-42b4-8953-62918b8564d7 · outbound

This paper cites BoolQ: Exploring the surprising difficulty of natural yes/no questions.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models BoolQ: Exploring the surprising difficulty of natural yes/no questions

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:01.777533Z

Source-reported events for the cited work

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

Observation 18aa9670-509f-44fa-a0cb-9a964110588e · outbound

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

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T05:18:00.653277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3033720-e6be-482f-9e37-2aab8f6bb45d · outbound

This paper cites Beyond Size: How Gradients Shape Pruning Decisions in Large Language Models.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Beyond Size: How Gradients Shape Pruning Decisions in Large Language Models

Reference 8

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Observation a3a9c799-9d89-42b6-bf63-1e444515cd7e · outbound

This paper cites Deepseek-v3 technical report, 2024.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Deepseek-v3 technical report, 2024

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:01.762396Z

Source-reported events for the cited work

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Observation a808ac6d-bae4-4ff6-b991-34dfee1ac399 · outbound

This paper cites Optimal brain compression: A framework for accurate post-training quantization and pruning.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Optimal brain compression: A framework for accurate post-training quantization and pruning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T05:18:00.672644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ce87ca3-bea8-44ba-b5fa-161a1611014a · outbound

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

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Sparsegpt: Massive language models can be accurately pruned in one-shot

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T05:18:00.677706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e292cc65-18ce-42d8-8cd5-bd0b839af4f8 · outbound

This paper cites Gptq: Accurate post-training quantization for generative pre-trained transformers, 2023.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Gptq: Accurate post-training quantization for generative pre-trained transformers, 2023

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T05:18:00.684957Z

Source-reported events for the cited work

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Observation acd6ca35-4a99-4286-8e0b-f0d78b5c9c7d · outbound

This paper cites A framework for few-shot language model evaluation, 12 2023.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models A framework for few-shot language model evaluation, 12 2023

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T05:18:00.692310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8203a861-e8d9-44a2-b227-cb706533c4b0 · outbound

This paper cites The llama 3 herd of models, 2024.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models The llama 3 herd of models, 2024

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:01.706038Z

Source-reported events for the cited work

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Observation 1a70383c-f310-42fe-bf5e-62a421024e46 · outbound

This paper cites Functionary.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Functionary

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:01.689484Z

Source-reported events for the cited work

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

Observation 13c54924-4a5c-49da-ab09-b2bef57572b6 · outbound

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

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Compresso: Structured Pruning with Collaborative Prompting Learns Compact Large Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T05:18:00.717102Z

Source-reported events for the cited work

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Observation cdddbc22-358d-4b2e-8324-6bb8e0923b03 · outbound

This paper cites an unresolved cited work.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Unresolved cited work

Reference 17

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Source-reported events for the cited work

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

Observation 7112f5ed-55fd-439d-b134-248144c36fec · outbound

This paper cites Optimal brain surgeon and general network pruning.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Optimal brain surgeon and general network pruning

Reference 18

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Source-reported events for the cited work

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Observation db8abe49-b88e-4662-b869-e79f8f769d9d · outbound

This paper cites RACE: Large-scale ReAding comprehension dataset from examinations.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models RACE: Large-scale ReAding comprehension dataset from examinations

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:01.647842Z

Source-reported events for the cited work

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Observation 1ab74062-8c34-4389-b535-f339ec49b91f · outbound

This paper cites Optimal brain damage.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Optimal brain damage

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:01.631729Z

Source-reported events for the cited work

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Observation 20deb705-1069-49d0-8185-30a97ac91cfc · outbound

This paper cites TruthfulQA: Measuring how models mimic human falsehoods.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models TruthfulQA: Measuring how models mimic human falsehoods

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:01.615735Z

Source-reported events for the cited work

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

Observation 6e33137f-c3f4-4600-a6ad-f2b8f83dc5af · outbound

This paper cites MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases

Reference 22

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Source-reported events for the cited work

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Observation 12bc2919-2ee0-421a-9fde-c11ed2675af0 · outbound

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

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Llm-pruner: On the structural pruning of large language models

Reference 23

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Source-reported events for the cited work

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Observation a9981811-6034-4088-a6bf-49cddfa8b35a · outbound

This paper cites OpenELM: An efficient language model family with open training and inference framework.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models OpenELM: An efficient language model family with open training and inference framework

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:01.583411Z

Source-reported events for the cited work

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Observation 76dda036-8239-434a-937c-789b849a60a4 · outbound

This paper cites Pointer sentinel mixture models.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Pointer sentinel mixture models

Reference 25

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Source-reported events for the cited work

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Observation d8c25c6e-1fa4-4092-b4cd-4b61e61023e5 · outbound

This paper cites Can a suit of armor conduct electricity? a new dataset for open book question answering.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Can a suit of armor conduct electricity? a new dataset for open book question answering

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:01.557452Z

Source-reported events for the cited work

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

Observation d9673bc7-7414-49cf-9e16-de23478d752e · outbound

This paper cites Importance estimation for neural network pruning.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Importance estimation for neural network pruning

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:01.541531Z

Source-reported events for the cited work

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

Observation 7ba3b960-9222-43a0-8481-ec61c2ce9944 · outbound

This paper cites Pruning Convolutional Neural Networks for Resource Efficient Inference.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Pruning Convolutional Neural Networks for Resource Efficient Inference

Reference 28

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Source-reported events for the cited work

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Observation eb1fd6d4-6c82-45b7-af3d-128aac6b52e5 · outbound

This paper cites Skeletonization: A technique for trimming the fat from a network via relevance assessment.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Skeletonization: A technique for trimming the fat from a network via relevance assessment

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:01.523255Z

Source-reported events for the cited work

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

Observation 08bbace6-84e0-48f8-853e-8cb4da6d71cc · outbound

This paper cites Compact language models via pruning and knowledge distillation.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Compact language models via pruning and knowledge distillation

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T05:18:00.813987Z

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Observation 2ace9761-2f78-45b3-b806-09152f3e7cf2 · outbound

This paper cites CrowS-pairs: A challenge dataset for measuring social biases in masked language models.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models CrowS-pairs: A challenge dataset for measuring social biases in masked language models

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:01.495252Z

Source-reported events for the cited work

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Observation b0dc668a-ebc5-4f60-95fc-22f0bf1e9ff9 · outbound

This paper cites Gpt-4 technical report, 2024.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Gpt-4 technical report, 2024

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:01.474197Z

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Observation f1c0aca5-51f3-4f0e-9e81-b5c562c95525 · outbound

This paper cites Revisiting self-distillation, 2022.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Revisiting self-distillation, 2022

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:01.455223Z

Source-reported events for the cited work

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Observation 0f9e8d22-86fe-4229-bb1d-1976fd5043be · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 34

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Observation b2d7752f-b979-47ae-82c5-a04ed5ad0aa5 · outbound

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

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Winogrande: An adversarial winograd schema challenge at scale

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:01.426223Z

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Observation 1a6d7fed-543b-404f-8482-084ce704ac35 · outbound

This paper cites Social IQa: Commonsense reasoning about social interactions.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Social IQa: Commonsense reasoning about social interactions

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:01.408252Z

Source-reported events for the cited work

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

Observation e03ab5a8-29f2-4f77-9b17-149add4d7c1d · outbound

This paper cites Amalgamating knowledge towards comprehensive classification.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Amalgamating knowledge towards comprehensive classification

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:01.386791Z

Source-reported events for the cited work

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

Observation bcf4b02e-a27e-4fe3-bbf3-f208d0d07f30 · outbound

This paper cites Progressive network grafting for few-shot knowledge distillation.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Progressive network grafting for few-shot knowledge distillation

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:01.368161Z

Source-reported events for the cited work

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Observation 81077838-92b6-4cd4-bdea-5fceb44c6939 · outbound

This paper cites A simple and effective pruning approach for large language models.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models A simple and effective pruning approach for large language models

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:01.348763Z

Source-reported events for the cited work

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Observation c71ed3b0-259e-4ab7-9df1-52cbaf754290 · outbound

This paper cites Learning Compact Vision Tokens for Efficient Large Multimodal Models.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Learning Compact Vision Tokens for Efficient Large Multimodal Models

Reference 40

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Observation 7aa2ad72-1f46-46ef-a88e-45072a432edd · outbound

This paper cites MobiLlama: Towards Accurate and Lightweight Fully Transparent GPT.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models MobiLlama: Towards Accurate and Lightweight Fully Transparent GPT

Reference 41

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Source-reported events for the cited work

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Observation 78e6679a-bcb9-4277-bb50-f42df4b913bc · outbound

This paper cites Llama: Open and efficient foundation language models, 2023.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Llama: Open and efficient foundation language models, 2023

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:01.330822Z

Source-reported events for the cited work

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

Observation f1264099-5f0f-4388-9fc6-83c208d9a2c3 · outbound

This paper cites LaMini-LM: A diverse herd of distilled models from large- scale instructions.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models LaMini-LM: A diverse herd of distilled models from large- scale instructions

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:01.311997Z

Source-reported events for the cited work

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

Observation 2915fee0-4dd4-4b65-a4df-9352a3f62240 · outbound

This paper cites Sheared LLaMA: Accelerating language model pre-training via structured pruning.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Sheared LLaMA: Accelerating language model pre-training via structured pruning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:01.294103Z

Source-reported events for the cited work

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

Observation 98954a94-d164-4709-b0a1-40f41b39c9ff · outbound

This paper cites Outlier weighed layerwise sparsity (owl) a missing secret sauce for pruning llms to high sparsity.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Outlier weighed layerwise sparsity (owl) a missing secret sauce for pruning llms to high sparsity

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:01.272457Z

Source-reported events for the cited work

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

Observation cea5167b-bad2-4919-98df-f348459342ec · outbound

This paper cites Be your own teacher: Improve the performance of convolutional neural networks via self distillation.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Be your own teacher: Improve the performance of convolutional neural networks via self distillation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:01.254986Z

Source-reported events for the cited work

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Observation 126ed860-7599-41b3-81ad-f350f9cc52a3 · outbound

This paper cites LoRAPrune: Structured pruning meets low-rank parameter-efficient fine-tuning.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models LoRAPrune: Structured pruning meets low-rank parameter-efficient fine-tuning

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:18:01.229170Z

Source-reported events for the cited work

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Observation 113da4f3-6025-4798-986d-88977a3da9f5 · outbound

This paper cites Tinyllama: An open-source small language model, 2024.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Tinyllama: An open-source small language model, 2024

Reference 48

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Source-reported events for the cited work

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Observation b06d7cf2-3449-43c7-80dc-d9f2e60b725e · outbound

This paper cites Opt: Open pre-trained transformer language models, 2022.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Opt: Open pre-trained transformer language models, 2022

Reference 49

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T05:18:01.186968Z

Source-reported events for the cited work

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Observation 597da13f-ac1f-4420-88ae-2531553071de · outbound

This paper cites an unresolved cited work.

SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models Unresolved cited work

Reference 2024

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Pith citing papers

Observation a9f89fff-61e2-4123-91c0-10afe46d1e92 · inbound

Less is MoE: Trimming Experts in Domain-Specialist Language Models cites this paper.

Less is MoE: Trimming Experts in Domain-Specialist Language Models SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-07-02T11:36:55.532074Z

Source-reported events for the cited work

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