Paper Citation Record · LEDGER

Automatic Pruning Discovery for Large Language Models

As of 19 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2511.15390.

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

pith.paper-citation-record.v1
2511.15390 v2

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T21:28:04.262035Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

42 of 42 outbound references displayed

verified exact0
verified fuzzy0
unresolved42
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malformed identifier0
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External citation measurements

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Outbound references

Observation dddab3e8-00a0-4b18-aaf4-3e8d298a4be3 · outbound

This paper cites https://openai.com/blog/chatgpt, 2022.

Automatic Pruning Discovery for Large Language Models https://openai.com/blog/chatgpt, 2022

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:00.409559Z

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Observation 5cbefcd9-9e48-4c25-821a-19575fa9c3e5 · outbound

This paper cites Lan- guage models are few-shot learners.Advances in neural in- formation processing systems, 33:1877–1901, 2020.

Automatic Pruning Discovery for Large Language Models Lan- guage models are few-shot learners.Advances in neural in- formation processing systems, 33:1877–1901, 2020

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:00.515645Z

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Observation cfd981d5-f5d5-45a9-983c-836061f3f61e · outbound

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

Automatic Pruning Discovery for Large Language Models Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:00.609284Z

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Observation e0c0b710-e493-4847-913e-4871c515a1c7 · outbound

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

Automatic Pruning Discovery for Large Language Models BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:00.802459Z

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Observation 2c0bd90c-bc1e-4331-998e-65089078cbaf · outbound

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

Automatic Pruning Discovery for Large Language Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:00.857895Z

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Observation 14ff089f-3524-48d3-8a44-3c0ccda17610 · outbound

This paper cites The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.

Automatic Pruning Discovery for Large Language Models The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:00.951063Z

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Observation c27a50e1-eb8c-44a2-a206-424446ca148a · outbound

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

Automatic Pruning Discovery for Large Language Models Sparsegpt: Massive lan- guage models can be accurately pruned in one-shot

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:01.072626Z

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Observation ee81dd2b-7787-4fe7-b4e5-308932660e35 · outbound

This paper cites A frame- work for few-shot language model evaluation.Version v0.

Automatic Pruning Discovery for Large Language Models A frame- work for few-shot language model evaluation.Version v0

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:01.222298Z

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Observation 7355f38d-7c31-4b51-b56e-40739d0905d2 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Automatic Pruning Discovery for Large Language Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:01.345096Z

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Observation 72640df6-6b88-43cd-96ba-b5b7714e47b5 · outbound

This paper cites Op- timal brain surgeon and general network pruning.

Automatic Pruning Discovery for Large Language Models Op- timal brain surgeon and general network pruning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:01.499365Z

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Observation b9d77090-37a6-4670-98b5-0df659ac72bb · outbound

This paper cites Amc: Automl for model compression and ac- celeration on mobile devices.

Automatic Pruning Discovery for Large Language Models Amc: Automl for model compression and ac- celeration on mobile devices

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:01.595500Z

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Observation 06635679-650e-4e5f-9e63-f123f02a993f · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Automatic Pruning Discovery for Large Language Models Measuring Massive Multitask Language Understanding

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:01.642116Z

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Observation 2a7c4f89-407c-484d-ad30-316a92989458 · outbound

This paper cites Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures.

Automatic Pruning Discovery for Large Language Models Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:01.703277Z

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Observation a057c38c-24c0-4d69-8bfc-3bec195b046d · outbound

This paper cites GPT-4o System Card.

Automatic Pruning Discovery for Large Language Models GPT-4o System Card

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:01.789399Z

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Observation db15284b-2bdc-4939-8745-60e5949e3efa · outbound

This paper cites Analogcoder: Analog circuit design via training-free code generation.

Automatic Pruning Discovery for Large Language Models Analogcoder: Analog circuit design via training-free code generation

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:01.849117Z

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Observation 47846482-097a-4ac9-977e-75cf46d159e6 · outbound

This paper cites Optimal brain damage.Advances in neural information processing systems, 2, 1989.

Automatic Pruning Discovery for Large Language Models Optimal brain damage.Advances in neural information processing systems, 2, 1989

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:01.930456Z

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Observation 004edd36-d9f3-4dbb-acd3-328158933951 · outbound

This paper cites Layer-adaptive sparsity for the magnitude-based pruning.

Automatic Pruning Discovery for Large Language Models Layer-adaptive sparsity for the magnitude-based pruning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:01.992469Z

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Observation 101934b8-78c7-4e7d-baad-69bb644b9544 · outbound

This paper cites Discovering sparsity allocation for layer-wise pruning of large language models.

Automatic Pruning Discovery for Large Language Models Discovering sparsity allocation for layer-wise pruning of large language models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:02.060270Z

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Observation f1a3c082-f37b-4902-af9e-2be3744bb2d3 · outbound

This paper cites Adaptive layer sparsity for large language models via activation corre- lation assessment.

Automatic Pruning Discovery for Large Language Models Adaptive layer sparsity for large language models via activation corre- lation assessment

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:02.106032Z

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Observation dccac0d1-a4af-439f-a413-0c6062a19952 · outbound

This paper cites Llm-pruner: On the structural pruning of large language models.Ad- vances in neural information processing systems, 36:21702– 21720, 2023.

Automatic Pruning Discovery for Large Language Models Llm-pruner: On the structural pruning of large language models.Ad- vances in neural information processing systems, 36:21702– 21720, 2023

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:02.194407Z

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Observation 1399f40e-6c47-4af3-a0ec-3f56f95bf44e · outbound

This paper cites Pointer Sentinel Mixture Models.

Automatic Pruning Discovery for Large Language Models Pointer Sentinel Mixture Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:02.254827Z

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Observation 0c2ff070-cd95-4d27-97ed-beeba7842dd6 · outbound

This paper cites Llama 3 8b instruct.

Automatic Pruning Discovery for Large Language Models Llama 3 8b instruct

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:02.344255Z

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Observation 4400b67a-55a6-4846-865c-8c44c16dae95 · outbound

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

Automatic Pruning Discovery for Large Language Models Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:02.435188Z

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Observation 6c7eca43-6f27-44b8-8905-2a41822fbeef · outbound

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

Automatic Pruning Discovery for Large Language Models Pruning Convolutional Neural Networks for Resource Efficient Inference

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:02.495390Z

Source-reported events for the cited work

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Observation 0d0da7ca-285a-4850-9008-447a831e0983 · outbound

This paper cites Importance estimation for neural net- work pruning.

Automatic Pruning Discovery for Large Language Models Importance estimation for neural net- work pruning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:02.610953Z

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Observation a05eaaca-36ee-4a81-b545-fa039d4ae3d9 · outbound

This paper cites SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning.

Automatic Pruning Discovery for Large Language Models SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:02.704555Z

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Observation bc76630b-f35c-4d38-a2a8-c08fbd54a41e · outbound

This paper cites Gpt-o3 system card.OpenAI System Card, 2025.

Automatic Pruning Discovery for Large Language Models Gpt-o3 system card.OpenAI System Card, 2025

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:02.795282Z

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Observation 6fb34b74-66c0-41b1-92d2-ce3e1f009f70 · outbound

This paper cites Winogrande: An adversarial winograd schema challenge at scale.Communications of the ACM, 64 (9):99–106, 2021.

Automatic Pruning Discovery for Large Language Models Winogrande: An adversarial winograd schema challenge at scale.Communications of the ACM, 64 (9):99–106, 2021

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:02.881748Z

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Observation 13c5f4b5-5dab-42b8-a249-7a61497d7271 · outbound

This paper cites The skewness of science.Journal of the Amer- ican society for information science, 43(9):628–638, 1992.

Automatic Pruning Discovery for Large Language Models The skewness of science.Journal of the Amer- ican society for information science, 43(9):628–638, 1992

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:02.939732Z

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Observation 70e81337-a8da-4098-b68e-47323ef26175 · outbound

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

Automatic Pruning Discovery for Large Language Models A simple and effective pruning approach for large language models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:02.996725Z

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Observation 6864e878-da06-415f-bddd-1e05c224efc1 · outbound

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

Automatic Pruning Discovery for Large Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:03.049131Z

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Observation a8640133-ca39-46c9-b4aa-aa21ea05a362 · outbound

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

Automatic Pruning Discovery for Large Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:03.089961Z

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Observation bc64d33b-9a6b-449a-8d3e-0df0a487c5e9 · outbound

This paper cites GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.

Automatic Pruning Discovery for Large Language Models GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:03.185346Z

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Observation 13d50f4b-b377-4f43-80d1-db1bddc04c00 · outbound

This paper cites MoE-Pruner: Pruning Mixture-of-Experts Large Language Model using the Hints from Its Router.

Automatic Pruning Discovery for Large Language Models MoE-Pruner: Pruning Mixture-of-Experts Large Language Model using the Hints from Its Router

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:03.295344Z

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Observation 1f57266b-c0de-466d-8102-59d7af509f90 · outbound

This paper cites BESA: Pruning Large Language Models with Blockwise Parameter-Efficient Sparsity Allocation.

Automatic Pruning Discovery for Large Language Models BESA: Pruning Large Language Models with Blockwise Parameter-Efficient Sparsity Allocation

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:03.454661Z

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Observation 6b82ac85-38ab-4b07-b12f-33167ccfdff1 · outbound

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

Automatic Pruning Discovery for Large Language Models Outlier weighed layerwise sparsity (owl): A missing secret sauce for pruning llms to high sparsity

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:03.613541Z

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Observation 68b1c6f2-10d2-4595-9aab-b9da131d8c12 · outbound

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

Automatic Pruning Discovery for Large Language Models Outlier weighed layerwise sparsity (owl): A missing secret sauce for pruning llms to high sparsity

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:03.726394Z

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Observation ac98de61-16ce-4364-abd9-ad49a429ec20 · outbound

This paper cites Auto graph encoder-decoder for neural network pruning.

Automatic Pruning Discovery for Large Language Models Auto graph encoder-decoder for neural network pruning

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:03.873297Z

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Observation 2d0f4eec-0546-415d-9c29-74b1852a7bde · outbound

This paper cites Carrying out cnn channel pruning in a white box.IEEE Transactions on Neural Networks and Learning Systems, 34(10):7946– 7955, 2022.

Automatic Pruning Discovery for Large Language Models Carrying out cnn channel pruning in a white box.IEEE Transactions on Neural Networks and Learning Systems, 34(10):7946– 7955, 2022

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:03.957835Z

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Observation a334ca33-65be-459d-b87e-a7beac8190d3 · outbound

This paper cites A review on edge large language models: Design, execution, and applications.ACM Comput- ing Surveys, 57(8):1–35, 2025.

Automatic Pruning Discovery for Large Language Models A review on edge large language models: Design, execution, and applications.ACM Comput- ing Surveys, 57(8):1–35, 2025

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:04.058930Z

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Observation a0eea9dc-0fa4-4067-a6a2-04dbbe542e55 · outbound

This paper cites an unresolved cited work.

Automatic Pruning Discovery for Large Language Models Unresolved cited work

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:04.181373Z

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Observation 78f7560a-e25a-464d-8d2e-3ac93c3bd879 · outbound

This paper cites Perp: Rethinking the prune-retrain paradigm in the era of llms.arXiv preprint arXiv:2312.15230, 2023.

Automatic Pruning Discovery for Large Language Models Perp: Rethinking the prune-retrain paradigm in the era of llms.arXiv preprint arXiv:2312.15230, 2023

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-03T21:28:04.262035Z

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Pith citing papers

No inbound Pith citation observations are available.