Paper Citation Record · LEDGER

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models

As of 8 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2508.09471.

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

pith.paper-citation-record.v1
2508.09471 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T21:08:18.727993Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

31 of 31 outbound references displayed

verified exact1
verified fuzzy14
unresolved15
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 19adfba2-7c8b-4ba2-ae64-f0d54ff02cad · outbound

This paper cites Bipartite graphs and their applications, volume 131.

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models Bipartite graphs and their applications, volume 131

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:08:19.164533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 87daeb5e-04ac-4e54-871e-f20e5ff89569 · outbound

This paper cites SparseLLM: Towards Global Pruning for Pre-trained Language Models.

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models SparseLLM: Towards Global Pruning for Pre-trained Language Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T21:08:18.637391Z

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Observation af4ef5e7-6b2e-457b-a387-d147c20cb12d · outbound

This paper cites BinaryBERT: Pushing the Limit of BERT Quantization.

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models BinaryBERT: Pushing the Limit of BERT Quantization

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T21:08:18.640797Z

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Observation 048ef4b6-494a-4dd8-87cf-5e0a41b88f40 · outbound

This paper cites The Llama 3 Herd of Models.

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models The Llama 3 Herd of Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T21:08:18.644144Z

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Observation 826288a4-c91c-4df5-bab0-abe240c73f81 · outbound

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

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models Sparsegpt: Massive language models can be accurately pruned in one-shot

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T21:08:18.647398Z

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Observation e4c95dd5-ecb5-4698-b69f-3e9602e1e4b6 · outbound

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

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T21:08:18.650720Z

Source-reported events for the cited work

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Observation 31c3246a-f7f6-4919-b2aa-ede25ceba04a · outbound

This paper cites Learning both weights and connections for efficient neural network.

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models Learning both weights and connections for efficient neural network

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T21:08:18.654331Z

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Unavailable: canonical work link unavailable.

Observation 2702dec4-4075-4d42-b2b2-ba7e1125cb7f · outbound

This paper cites Stork, and Gregory J.

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models Stork, and Gregory J

Reference 8

Resolution: malformed identifier; raw_fallback, observed 2026-08-05T21:08:18.947350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b2adf87d-e49a-4e02-abc1-753d417f8b52 · outbound

This paper cites Revisiting pruning at initialization through the lens of ramanujan graph.

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models Revisiting pruning at initialization through the lens of ramanujan graph

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:08:19.143837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b6ddd13d-8eb6-4804-9498-7d3a64935a84 · outbound

This paper cites Explicit two-sided unique-neighbor expanders.

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models Explicit two-sided unique-neighbor expanders

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:08:19.129513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 41af22fb-43e9-4677-9a6f-83d80f1349d7 · outbound

This paper cites Pruning large language models with semi-structural adaptive sparse training.

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models Pruning large language models with semi-structural adaptive sparse training

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:08:19.117887Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 08ff3ddc-76c0-49e9-a317-fc61f700e9af · outbound

This paper cites Optimal brain damage.

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models Optimal brain damage

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:08:19.107407Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 531a4f2b-c232-4196-a565-87ba427426c0 · outbound

This paper cites SNIP: Single-shot Network Pruning based on Connection Sensitivity.

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models SNIP: Single-shot Network Pruning based on Connection Sensitivity

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T21:08:18.672062Z

Source-reported events for the cited work

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Observation 1bb91b41-8a3b-41af-80b4-c5bef60e2e30 · outbound

This paper cites Awq: Activation-aware weight quantization for on-device llm compression and acceleration.

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models Awq: Activation-aware weight quantization for on-device llm compression and acceleration

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:08:19.095685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 718cc2ba-ad8f-4e1b-b130-d534270db792 · outbound

This paper cites Sparse training via boosting pruning plasticity with neuroregeneration.

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models Sparse training via boosting pruning plasticity with neuroregeneration

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:08:19.085774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d7e5c26d-e5b1-4c6f-9181-973a37817696 · outbound

This paper cites Alphapruning: Using heavy-tailed self regularization theory for improved layer-wise pruning of large language models.

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models Alphapruning: Using heavy-tailed self regularization theory for improved layer-wise pruning of large language models

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:08:19.075462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 58262767-457e-46cd-a998-0dfcd393d6dd · outbound

This paper cites Accelerating sparse deep neural networks, 2021.

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models Accelerating sparse deep neural networks, 2021

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T21:08:18.683906Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3cb35e7a-ab3f-4506-8b40-9efc057b44db · outbound

This paper cites Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science.

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:08:19.059294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5810255b-8b1a-4337-805c-5e46b38ae31e · outbound

This paper cites Nvidia a100 tensor core gpu architecture.

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models Nvidia a100 tensor core gpu architecture

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:08:19.049579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fcdbda20-5e5c-472c-9c1f-da32a45a4401 · outbound

This paper cites Deep expander networks: Efficient deep networks from graph theory.

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models Deep expander networks: Efficient deep networks from graph theory

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:08:19.029478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 29655a91-cb15-40a1-a309-f1029c41e9d5 · outbound

This paper cites Movement pruning: Adaptive sparsity by fine-tuning.

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models Movement pruning: Adaptive sparsity by fine-tuning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T21:08:18.699006Z

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Observation 1c73b968-34df-41e2-91d2-72d2704a8d58 · outbound

This paper cites Spielman.

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models Spielman

Reference 22

Resolution: verified exact; doi, observed 2026-08-05T21:08:18.758121Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 06c802d9-316d-45da-98c0-c9756df82f1e · outbound

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

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models A Simple and Effective Pruning Approach for Large Language Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T21:08:18.704460Z

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Observation 6d513314-bb0b-4e50-8ccb-a0610770cd6f · outbound

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

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-05T21:08:18.707519Z

Source-reported events for the cited work

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Observation 5c017746-2f3a-4eff-8a96-9e499b1f7067 · outbound

This paper cites Picking Winning Tickets Before Training by Preserving Gradient Flow.

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-05T21:08:18.710324Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95ebbb70-c7c2-4fd8-9207-f80b22e3f846 · outbound

This paper cites Pruning before fine-tuning: A retraining-free compression framework for pre-trained language models.

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models Pruning before fine-tuning: A retraining-free compression framework for pre-trained language models

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:08:19.009292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5d1b4f96-52c6-44c2-a25b-8d9577dbb814 · outbound

This paper cites Smoothquant: Accurate and efficient post-training quantization for large language models.

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models Smoothquant: Accurate and efficient post-training quantization for large language models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-05T21:08:18.717524Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37eedbe6-45ce-4eac-81ba-75c2ae95c56e · outbound

This paper cites EfficientLLM: Scalable Pruning-Aware Pretraining for Architecture-Agnostic Edge Language Models.

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models EfficientLLM: Scalable Pruning-Aware Pretraining for Architecture-Agnostic Edge Language Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-05T21:08:18.721011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30829f63-1c4d-4676-b8d4-9bb57c7ed39b · outbound

This paper cites Prune Once for All: Sparse Pre-Trained Language Models.

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models Prune Once for All: Sparse Pre-Trained Language Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-05T21:08:18.724499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e290f3dc-1a56-4fe2-9bb5-c9c144d9adc4 · outbound

This paper cites Plug-and-play: An efficient post-training pruning method for large language models.

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models Plug-and-play: An efficient post-training pruning method for large language models

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:08:18.992840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 07dd23e8-378f-4088-af71-20736c3b6b95 · outbound

This paper cites Whitepaper, Accessed: 2025-05-15.

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models Whitepaper, Accessed: 2025-05-15

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:08:19.039788Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

No inbound Pith citation observations are available.