Paper Citation Record · LEDGER

Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers

As of 13 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2507.23362.

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

pith.paper-citation-record.v1
2507.23362 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T10:55:18.369233Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

52 of 52 outbound references displayed

verified exact0
verified fuzzy1
unresolved51
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 62f27981-59b1-4c18-b617-f3a9f3165287 · outbound

This paper cites Transformer Block Coupling and its Correlation with Generalization in LLMs.

Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Transformer Block Coupling and its Correlation with Generalization in LLMs

Reference 1

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Source-reported events for the cited work

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Observation 2f0ee500-bbf8-493e-98c2-e81fc721723e · outbound

This paper cites Qwen Technical Report.

Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Qwen Technical Report

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T10:55:18.072694Z

Source-reported events for the cited work

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Observation 9a4a1e77-3c4d-42b2-bfd8-447fb0a9264f · outbound

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Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

Reference 3

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Source-reported events for the cited work

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Observation 7d8d6128-7e7b-4499-a4ce-59908613c92f · outbound

This paper cites an unresolved cited work.

Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

Reference 4

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Observation 2c9511e9-cd8f-4a98-a2a2-b3d273406492 · outbound

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

Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Streamlining Redundant Layers to Compress Large Language Models

Reference 5

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Observation f5517b1f-5b07-4274-ba9a-c626a663350c · outbound

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Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

Reference 6

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Source-reported events for the cited work

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Observation 38911b60-6558-4c1a-b884-397ae0eb9ed7 · outbound

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

Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T10:55:18.106740Z

Source-reported events for the cited work

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Observation 2f6e26ba-33e1-4db7-9afc-fde24bcd281c · outbound

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Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

Reference 8

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Source-reported events for the cited work

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Observation b319c30d-11e5-4fec-8141-9c62fa11e5a4 · outbound

This paper cites an unresolved cited work.

Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

Reference 9

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Observation 3604168d-f138-406a-a2f9-3bf081d54965 · outbound

This paper cites an unresolved cited work.

Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

Reference 11

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Observation bcab9834-a370-4cff-9f4f-0d1f01636d0d · outbound

This paper cites The Unreasonable Ineffectiveness of the Deeper Layers.

Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers The Unreasonable Ineffectiveness of the Deeper Layers

Reference 12

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This paper cites an unresolved cited work.

Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

Reference 13

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Observation 2cc3eec8-7b3c-4ea6-8f86-9412d06c521b · outbound

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Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

Reference 14

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Observation e853e097-9f92-4ee5-88cc-86a749e4688d · outbound

This paper cites an unresolved cited work.

Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

Reference 15

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Observation 105dca4c-5ac0-4429-9d20-5e24d14b8e5d · outbound

This paper cites Scaling Laws for Neural Language Models.

Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Scaling Laws for Neural Language Models

Reference 16

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Observation d1379d30-037d-4454-a754-4ed7837a1795 · outbound

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Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

Reference 17

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Observation e315d289-dc92-4f50-b5b1-1e9c769d606a · outbound

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Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

Reference 18

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Observation f698518a-f5ca-4763-8231-d4fba0216d86 · outbound

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Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

Reference 19

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Observation 35a4f014-31da-422f-a6ec-e100b6c927f0 · outbound

This paper cites an unresolved cited work.

Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

Reference 20

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Observation ed9adbe6-f1f4-486d-abbd-208156000e88 · outbound

This paper cites an unresolved cited work.

Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

Reference 21

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Observation fdc7355a-a878-48c0-a559-eb2e5a9c13c0 · outbound

This paper cites Boosting Multimodal Large Language Models with Visual Tokens Withdrawal for Rapid Inference.

Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Boosting Multimodal Large Language Models with Visual Tokens Withdrawal for Rapid Inference

Reference 22

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Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

Reference 23

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Observation 3c5001e5-8df1-4aa2-8753-1abb086fc89c · outbound

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Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

Reference 24

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Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

Reference 25

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Observation e80eba60-7c54-42d2-89e9-8df683e5f489 · outbound

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Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

Reference 26

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Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

Reference 27

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Observation 36ae3d76-ac42-49bb-95f1-76b92fa6fc76 · outbound

This paper cites $\gamma-$MoD: Exploring Mixture-of-Depth Adaptation for Multimodal Large Language Models.

Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers $\gamma-$MoD: Exploring Mixture-of-Depth Adaptation for Multimodal Large Language Models

Reference 28

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Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

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Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

Reference 30

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Observation 193ca694-3e7f-4845-975d-a9dd0fd0b7d5 · outbound

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Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers ShortGPT: Layers in Large Language Models are More Redundant Than You Expect

Reference 31

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Observation 82e8d62e-6f6c-4698-ad14-9bed170ede23 · outbound

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Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

Reference 32

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Observation 724f076b-2b9c-414b-a774-d797cdb7340a · outbound

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Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

Reference 33

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Observation 5340a0f1-ea0b-4bb6-8aa1-314f086a4dc2 · outbound

This paper cites Instruction Tuning with GPT-4.

Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Instruction Tuning with GPT-4

Reference 34

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Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

Reference 35

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Observation dddef11c-d13a-490f-bc6b-f94b78716337 · outbound

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Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

Reference 36

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Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

Reference 37

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Observation ec0219c8-ea7a-437e-b6a6-643da53ca0c8 · outbound

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

Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks

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Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

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Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

Reference 41

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Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

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Observation 7b40f3e3-d913-476d-add1-5307f7fb8f31 · outbound

This paper cites Pruning All-Rounder: Rethinking and Improving Inference Efficiency for Large Vision Language Models.

Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Pruning All-Rounder: Rethinking and Improving Inference Efficiency for Large Vision Language Models

Reference 43

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Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

Reference 44

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Observation 55e0e97f-c34a-40cd-b98f-e360c24789ba · outbound

This paper cites Qwen2.5 Technical Report.

Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Qwen2.5 Technical Report

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Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers LLaMA: Open and Efficient Foundation Language Models

Reference 46

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Reference 47

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Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers ATP-LLaVA: Adaptive Token Pruning for Large Vision Language Models

Reference 48

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Reference 49

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Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Unresolved cited work

Reference 53

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Observation bd5b220a-3920-474c-bb0e-47dae0433a81 · outbound

This paper cites Evaluating Object Hallucination in Large Vision-Language Models.

Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Evaluating Object Hallucination in Large Vision-Language Models

Reference 2023

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This paper cites Towards Interpreting Visual Information Processing in Vision-Language Models.

Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Towards Interpreting Visual Information Processing in Vision-Language Models

Reference 2024

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Observation 0d3426fe-7b78-4653-8226-6ef4527355d6 · outbound

This paper cites In Proceedings of the Computer Vision and Pattern MM ’25, October 27–31, 2025, Dublin, Ireland Ji Ma, Wei Suo, Peng Wang, and Yanning Zhang Recognition Conference.

Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers In Proceedings of the Computer Vision and Pattern MM ’25, October 27–31, 2025, Dublin, Ireland Ji Ma, Wei Suo, Peng Wang, and Yanning Zhang Recognition Conference

Reference 2025

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Pith citing papers

No inbound Pith citation observations are available.