Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study | Paper Citation Record

Observation cde1480c-a661-4101-87e6-a7b4054270e9 · outbound

This paper cites SliceGPT: Compress Large Language Models by Deleting Rows and Columns.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study SliceGPT: Compress Large Language Models by Deleting Rows and Columns

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:44.042601Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71696497-427a-4cb6-b9f3-d267c42b3438 · outbound

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

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study BinaryBERT: Pushing the Limit of BERT Quantization

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:44.098152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1f1ec04-f4bf-424a-afd5-c1f0e236a85a · outbound

This paper cites an unresolved cited work.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-06T13:22:52.182974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9a3a4765-f16a-48f8-9a9c-b7f8a7db46db · outbound

This paper cites How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:44.247960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89b2e58c-ab52-4e06-b7ff-523d1e445c2c · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:44.288220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5f32ebe-0e91-4a1b-ad90-061213ed3def · outbound

This paper cites an unresolved cited work.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-06T13:22:52.009137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8544ec58-e517-4bfc-97f5-7b8cb41c1d5c · outbound

This paper cites MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:44.438590Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 504bbf55-2121-414f-a9e0-ee8cb76bbafd · outbound

This paper cites an unresolved cited work.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Unresolved cited work

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:44.503133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5878f7d-8e4d-4ae7-8ef7-abbf0b611e3d · outbound

This paper cites int8 (): 8-bit matrix multiplicationfortransformersatscale.AdvancesinNeuralInformationProcessing Systems 35, 30318–30332 (2022).

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study int8 (): 8-bit matrix multiplicationfortransformersatscale.AdvancesinNeuralInformationProcessing Systems 35, 30318–30332 (2022)

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:22:51.820101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation acf926ff-1937-477c-8b0c-130bef12dffc · outbound

This paper cites an unresolved cited work.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-06T13:22:51.695570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4290d994-172a-4b45-b8f7-189b17ba2e12 · outbound

This paper cites Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:44.661615Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3776073e-b025-4559-a80c-f2d61610c801 · outbound

This paper cites Reducing Transformer Depth on Demand with Structured Dropout.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Reducing Transformer Depth on Demand with Structured Dropout

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:44.726762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b314f67b-cb0a-4bf8-856c-e1dc8b7a240f · outbound

This paper cites In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:44.801416Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16654f6e-d277-4a49-b6ac-3de1ccce50d8 · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:22:51.495830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1cc44439-724d-4e5c-843c-84e2f4d2c5cf · outbound

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

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:44.947753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d07fa3d9-2024-433d-9135-a580bfdec58b · outbound

This paper cites International Journal of Computer Vision 129(6), 1789–1819 (Mar Pruning and Recovery Techniques for Compressing MLLMs 15 2021).

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study International Journal of Computer Vision 129(6), 1789–1819 (Mar Pruning and Recovery Techniques for Compressing MLLMs 15 2021)

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:44.982668Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e0e02db-332f-41d8-8a0e-2cc44286cb70 · outbound

This paper cites MiniLLM: On-Policy Distillation of Large Language Models.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study MiniLLM: On-Policy Distillation of Large Language Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:45.128888Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6e16ed1-ea5d-45d0-a58b-b8b29de3acce · outbound

This paper cites Efficient Multimodal Learning from Data-centric Perspective.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Efficient Multimodal Learning from Data-centric Perspective

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:45.165347Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef1e9ca0-ff73-46ea-91bc-b8c8248b5a39 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Distilling the Knowledge in a Neural Network

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:45.229918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d23b2a2-47e7-413f-bf93-54a9a47512fe · outbound

This paper cites The Curious Case of Neural Text Degeneration.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study The Curious Case of Neural Text Degeneration

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:45.286552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a63f0888-6f7e-4bd3-9843-6e4532d24289 · outbound

This paper cites an unresolved cited work.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-06T13:22:51.294215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation aa152e63-2d7a-48d5-94bd-6ee8740a0077 · outbound

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

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study LoRA: Low-Rank Adaptation of Large Language Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:45.474761Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b02ecb8-5706-4f78-9280-1de82ff192ef · outbound

This paper cites In: Proceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study In: Proceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:45.511186Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a41c6bc-6b1f-46ea-abd1-4542610f4d0a · outbound

This paper cites Microsoft Research Blog (2023).

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Microsoft Research Blog (2023)

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:22:51.106896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7945b797-6fe1-48bc-a8cf-aaa36466f759 · outbound

This paper cites Master’s thesis, University of Washington (2024).

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Master’s thesis, University of Washington (2024)

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:22:50.902477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1830bc6a-bdb2-4108-8f5a-0332b13c47ad · outbound

This paper cites TinyBERT: Distilling BERT for Natural Language Understanding.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study TinyBERT: Distilling BERT for Natural Language Understanding

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:45.689017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97d2059f-e96f-406e-9738-42bc79a9939b · outbound

This paper cites Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:45.746187Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9062c940-8f27-491d-b23c-0817595dff84 · outbound

This paper cites ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:45.812030Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation adc4c753-1de6-4343-a4ba-4d565c7aadda · outbound

This paper cites A Signal Propagation Perspective for Pruning Neural Networks at Initialization.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study A Signal Propagation Perspective for Pruning Neural Networks at Initialization

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:45.868130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b9e497b-40bc-4f95-bab5-1fcd9783b7ea · outbound

This paper cites Pruning Filters for Efficient ConvNets.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Pruning Filters for Efficient ConvNets

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:45.937930Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc4072fa-b7b5-49dd-ad2e-37d41680e44c · outbound

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

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Evaluating Object Hallucination in Large Vision-Language Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:46.010798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 813551f2-7d42-4ace-995a-2b6eefcd90d3 · outbound

This paper cites MixKD: Towards Efficient Distillation of Large-scale Language Models.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study MixKD: Towards Efficient Distillation of Large-scale Language Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:46.069815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7b0fe10-9d47-40ce-a3c2-e0339c882a9c · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:22:50.741243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation aff4ce76-5fdc-43ab-ab30-14bb3b3b6d08 · outbound

This paper cites Visual Instruction Tuning.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Visual Instruction Tuning

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:46.228942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e404fb81-d970-468a-afcb-4cac69586276 · outbound

This paper cites Group Fisher Pruning for Practical Network Compression.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Group Fisher Pruning for Practical Network Compression

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:46.284667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41f59a31-6279-4d4c-a070-0af503067cef · outbound

This paper cites Advances in Neural Information Processing Systems 35, 2507–2521 (2022).

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Advances in Neural Information Processing Systems 35, 2507–2521 (2022)

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:46.381619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ae59497-55cb-4f04-b6fe-ccc62c01cbca · outbound

This paper cites Advances in neural information processing systems36, 21702–21720 (2023).

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Advances in neural information processing systems36, 21702–21720 (2023)

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:46.487160Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49e3e980-9831-4937-9fd4-aa83b45533c7 · outbound

This paper cites Structured Pruning of a BERT-based Question Answering Model.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Structured Pruning of a BERT-based Question Answering Model

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:46.547975Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dca33805-b5df-47e6-8ef6-65a740e9008f · outbound

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

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study ShortGPT: Layers in Large Language Models are More Redundant Than You Expect

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:46.639736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a2b67f8-9578-49d2-ba24-4212364c4287 · outbound

This paper cites an unresolved cited work.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Unresolved cited work

Reference 42

Resolution: unresolved; raw_fallback, observed 2026-08-06T13:22:50.561751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b162db12-7f10-4949-8a80-8ccd808f1a45 · outbound

This paper cites Lookahead: A Far-Sighted Alternative of Magnitude-based Pruning.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Lookahead: A Far-Sighted Alternative of Magnitude-based Pruning

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:46.791956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80e2e3bf-68dc-4bd1-90e9-bdb2235a8f95 · outbound

This paper cites Zero-Shot Distillation for Image Encoders: How to Make Effective Use of Synthetic Data.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Zero-Shot Distillation for Image Encoders: How to Make Effective Use of Synthetic Data

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:46.867030Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7062b5e9-312c-44b8-b478-76755a4a6203 · outbound

This paper cites In: International conference on machine learning.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study In: International conference on machine learning

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:46.889339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd142509-ef06-474c-913c-6061df413d8d · outbound

This paper cites Computer Speech & Language77, 101429 (2023).

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Computer Speech & Language77, 101429 (2023)

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:46.953068Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d7b8da9-6408-42dd-9ddb-2559ec0adbc0 · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:47.025758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15bff839-5809-4602-a976-2975f142d3a1 · outbound

This paper cites Movement Pruning: Adaptive Sparsity by Fine-Tuning.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Movement Pruning: Adaptive Sparsity by Fine-Tuning

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:47.097104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 427cc078-087e-4d6b-a1d0-186875127e40 · outbound

This paper cites Patient Knowledge Distillation for BERT Model Compression.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Patient Knowledge Distillation for BERT Model Compression

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:47.217308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8888c43e-686e-49a3-bf51-2325d029d1f9 · outbound

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

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study LLaMA: Open and Efficient Foundation Language Models

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:47.333939Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3f4a32f-4f2d-4e7a-afd8-1b776e1b54f7 · outbound

This paper cites Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:47.423140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 899eaffc-bd30-49da-9ac8-13f86b13e5e4 · outbound

This paper cites MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:47.515023Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d6e61e8-845d-43aa-9d8f-5c0e98c4af06 · outbound

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

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:47.582094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3447fd5-6547-4b55-a2c3-f0b007303cf5 · outbound

This paper cites A Survey on Knowledge Distillation of Large Language Models.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study A Survey on Knowledge Distillation of Large Language Models

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:47.656391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b44a2ab7-f7f3-46ea-bb8c-342c9ae1d263 · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:22:50.309347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 934d023b-00d6-45e5-a64e-f1d9d38e76e3 · outbound

This paper cites ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:47.848505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d57b322-8fa6-4449-aee6-d7525aa7edd1 · outbound

This paper cites A Survey on Multimodal Large Language Models.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study A Survey on Multimodal Large Language Models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:47.959885Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4fcd6331-8720-4d8d-9447-6aee4685d197 · outbound

This paper cites Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:48.083495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39b8d7ee-30b3-49b8-b505-406f390d2741 · outbound

This paper cites In: Proceedings of CVPR (2024).

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study In: Proceedings of CVPR (2024)

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:22:50.120328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 72696593-63a9-4078-9549-9df1553f1be8 · outbound

This paper cites In: 2019 Fifth Workshop on Energy Efficient Machine Learn- ing and Cognitive Computing - NeurIPS Edition (EMC2-NIPS).

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study In: 2019 Fifth Workshop on Energy Efficient Machine Learn- ing and Cognitive Computing - NeurIPS Edition (EMC2-NIPS)

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:48.316422Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c8c4468-5aff-456f-869d-c32b64457647 · outbound

This paper cites In: Proceedings of the IEEE/CVF International Conference on Computer Vision.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study In: Proceedings of the IEEE/CVF International Conference on Computer Vision

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:22:49.965938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 80e71a35-495e-4fb4-979e-48df0d34a593 · outbound

This paper cites Mipha: A Comprehensive Overhaul of Multimodal Assistant with Small Language Models.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Mipha: A Comprehensive Overhaul of Multimodal Assistant with Small Language Models

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:48.525847Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cefbfc6f-6959-4fda-a4a6-fbf15b377446 · outbound

This paper cites In: Proceedings of the 1st International Workshop on Efficient Multimedia Computing under Limited.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study In: Proceedings of the 1st International Workshop on Efficient Multimedia Computing under Limited

Reference 63

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T13:22:49.766910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers