Paper Citation Record · LEDGER

SlimLLM: Accurate Structured Pruning for Large Language Models

As of 8 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 3 inbound Pith citation observations for arXiv:2505.22689.

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

pith.paper-citation-record.v1
2505.22689 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:30:21.169863Z

measured 24 of 24 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T16:41:28.847243Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T07:41:32.920838Z

Reference resolution

21 of 21 outbound references displayed

verified exact0
verified fuzzy0
unresolved21
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4a6a4c23-0e67-4321-96db-cec1a343d080 · outbound

This paper cites GPT-4 Technical Report.

SlimLLM: Accurate Structured Pruning for Large Language Models GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:18.908325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65e72728-6e3a-401f-847f-3f6f275d870c · outbound

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

SlimLLM: Accurate Structured Pruning for Large Language Models GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:19.356948Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0baa9431-9e1d-4dd2-8120-672a2c264bae · outbound

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

SlimLLM: Accurate Structured Pruning for Large Language Models LoRA: Low-Rank Adaptation of Large Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:19.541220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 916a60c0-4740-4e01-b7e6-f560c2ec2f75 · outbound

This paper cites LoRAP: Transformer Sub-Layers Deserve Differentiated Structured Compression for Large Language Models.

SlimLLM: Accurate Structured Pruning for Large Language Models LoRAP: Transformer Sub-Layers Deserve Differentiated Structured Compression for Large Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:19.805816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71fa2261-fb17-479c-a901-faecfa7764a2 · outbound

This paper cites SlimGPT: Layer-wise Structured Pruning for Large Language Models.

SlimLLM: Accurate Structured Pruning for Large Language Models SlimGPT: Layer-wise Structured Pruning for Large Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:19.964750Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 459bd486-32bd-4491-baee-502692a036ca · outbound

This paper cites Pointer Sentinel Mixture Models.

SlimLLM: Accurate Structured Pruning for Large Language Models Pointer Sentinel Mixture Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:20.168376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c7748c0-9aa1-4f27-b552-43f5c056fbe4 · outbound

This paper cites SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks.

SlimLLM: Accurate Structured Pruning for Large Language Models SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:20.434754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d4cf61f-ea2f-47ae-814d-2c9854193f9d · outbound

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

SlimLLM: Accurate Structured Pruning for Large Language Models A Simple and Effective Pruning Approach for Large Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:20.554754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d0d542b-6dd6-480c-a9a6-cd21c682cefc · outbound

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

SlimLLM: Accurate Structured Pruning for Large Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:20.648011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5480ca71-7933-40aa-b733-a8262f9d0ca0 · outbound

This paper cites Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning.

SlimLLM: Accurate Structured Pruning for Large Language Models Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:20.878868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da610b3d-80d9-4024-817f-9d69ad6fc237 · outbound

This paper cites Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity.

SlimLLM: Accurate Structured Pruning for Large Language Models Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:20.966547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e618d6e6-a24e-4cd9-b616-3371fe5d21f1 · outbound

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

SlimLLM: Accurate Structured Pruning for Large Language Models HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:21.091731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc6e8ff4-2f01-4fdd-98d8-d4f976dffa28 · outbound

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

SlimLLM: Accurate Structured Pruning for Large Language Models LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:21.169863Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a9a1ff7-3e39-46f9-9140-d1a48800b146 · outbound

This paper cites ShortGPT: Layers in Large Language Models are More Redundant Than You Expect.

SlimLLM: Accurate Structured Pruning for Large Language Models ShortGPT: Layers in Large Language Models are More Redundant Than You Expect

Reference 1993

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:20.038351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0c4bd2e-963b-4ccb-a0c5-1a47a1ce30ee · outbound

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

SlimLLM: Accurate Structured Pruning for Large Language Models Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:20.274967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b751b8d-e408-4e28-9f69-a72de4268efb · outbound

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

SlimLLM: Accurate Structured Pruning for Large Language Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:19.243071Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7d2da69-6a0a-4fbf-b499-16bc3ad5ee34 · outbound

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

SlimLLM: Accurate Structured Pruning for Large Language Models LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:18.984228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b39f791b-6bb5-4bf9-9a5c-8e4d9f42cb47 · outbound

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

SlimLLM: Accurate Structured Pruning for Large Language Models Shortened LLaMA: Depth Pruning for Large Language Models with Comparison of Retraining Methods

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:19.668268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3b8eeaa-d291-47fa-9214-59a1377fb0fb · outbound

This paper cites Language model compression with weighted low-rank factorization.

SlimLLM: Accurate Structured Pruning for Large Language Models Language model compression with weighted low-rank factorization

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:19.451609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6e43979-3966-4e2b-a5da-b8f2a4b90edd · outbound

This paper cites Streamlining Redundant Layers to Compress Large Language Models.

SlimLLM: Accurate Structured Pruning for Large Language Models Streamlining Redundant Layers to Compress Large Language Models

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:19.050049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a2cae38-8b69-4f35-82cb-4718d400dc43 · outbound

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

SlimLLM: Accurate Structured Pruning for Large Language Models BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:19.159383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 5265b5ae-7be3-48b0-a243-4d7a21268622 · inbound

Different Prompts, Different Ranks: Prompt-aware Dynamic Rank Selection for SVD-based LLM Compression cites this paper.

Different Prompts, Different Ranks: Prompt-aware Dynamic Rank Selection for SVD-based LLM Compression SlimLLM: Accurate Structured Pruning for Large Language Models

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:41:32.966832Z

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 45377df2-6e0f-4677-bfb2-2bbf9b46329d · inbound

It Takes a MAESTRO To Prune Bad Experts cites this paper.

It Takes a MAESTRO To Prune Bad Experts SlimLLM: Accurate Structured Pruning for Large Language Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T07:56:24.530269Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21717134-abf6-457c-a6de-687c2673a554 · inbound

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

SlimVLM: Sensitivity-aware Dynamic Structured Pruning with Adaptive Visual Token Selection for Efficient Vision-Language Models SlimLLM: Accurate Structured Pruning for Large Language Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T16:41:28.847243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.