Paper Citation Record · LEDGER

MBQ: Modality-Balanced Quantization for Large Vision-Language Models

As of 21 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 4 inbound Pith citation observations for arXiv:2412.19509.

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

pith.paper-citation-record.v1
2412.19509 v2

Coverage vector

measured 63 of 63 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T00:21:52.377583Z

measured 67 of 67 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T19:12:56.131001Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T16:18:37.366607Z

Reference resolution

63 of 63 outbound references displayed

verified exact0
verified fuzzy24
unresolved39
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2ccd005e-c2e7-4511-bd86-d3ef88c200f8 · outbound

This paper cites Flamingo: a visual language model for few-shot learning.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models Flamingo: a visual language model for few-shot learning

Reference 1

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Source-reported events for the cited work

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Observation e8f2e767-f845-4a23-854d-5852311f48d1 · outbound

This paper cites Vqa: Visual question answering.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models Vqa: Visual question answering

Reference 2

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Source-reported events for the cited work

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Observation af04e1b6-f081-448d-9815-fc87e0416989 · outbound

This paper cites QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs

Reference 3

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Observation 78b32ce7-ef0b-4309-9ad8-d0918eff8080 · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 4

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Source-reported events for the cited work

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Observation 8a62efcc-4947-44b1-9d88-508cdefd994f · outbound

This paper cites PaliGemma: A versatile 3B VLM for transfer.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models PaliGemma: A versatile 3B VLM for transfer

Reference 5

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Observation 18e799cc-3e96-4d80-bad7-3be4a4b751a9 · outbound

This paper cites Madtp: Multi- modal alignment-guided dynamic token pruning for accel- erating vision-language transformer.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models Madtp: Multi- modal alignment-guided dynamic token pruning for accel- erating vision-language transformer

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:21:53.478870Z

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Observation 2f70b025-08aa-45a6-86e5-f71421afb842 · outbound

This paper cites Quip: 2-bit quantization of large language models with guarantees.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models Quip: 2-bit quantization of large language models with guarantees

Reference 7

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Observation 4649ac03-06bf-4d4e-a620-172b4beb1571 · outbound

This paper cites ShareGPT4V: Improving Large Multi-Modal Models with Better Captions.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models ShareGPT4V: Improving Large Multi-Modal Models with Better Captions

Reference 8

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Observation 6ff507a7-7fdc-46ce-9903-dc28985ad3b3 · outbound

This paper cites An image is worth 1/2 tokens after layer 2: Plug-and-play inference acceleration for large vision-language models.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models An image is worth 1/2 tokens after layer 2: Plug-and-play inference acceleration for large vision-language models

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:21:53.450320Z

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Observation ab70f971-27a1-43e3-847c-74cbf10be103 · outbound

This paper cites Microsoft COCO Captions: Data Collection and Evaluation Server.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models Microsoft COCO Captions: Data Collection and Evaluation Server

Reference 10

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Observation 3fa41131-0df4-46ab-997e-05de94b2030e · outbound

This paper cites Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:21:53.435429Z

Source-reported events for the cited work

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Observation 87bd6ebe-a169-4823-a15a-41161d57b92b · outbound

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

MBQ: Modality-Balanced Quantization for Large Vision-Language Models MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices

Reference 12

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Source-reported events for the cited work

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Observation 0527c9fd-9b03-416e-ba9c-4a9ca20110e1 · outbound

This paper cites FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T00:21:52.159156Z

Source-reported events for the cited work

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Observation a81c7215-b073-4b70-a25e-9b81be08aff8 · outbound

This paper cites SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 14

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Source-reported events for the cited work

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Observation 0c08c9cf-1851-4048-a4b3-a95242fd1e98 · outbound

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

MBQ: Modality-Balanced Quantization for Large Vision-Language Models GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 15

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Observation 1f9b97e6-56b7-4c7a-9ed8-26436026e99c · outbound

This paper cites Moa: Mixture of sparse attention for automatic large language model compression.arXiv preprint arXiv:2406.14909, 2024.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models Moa: Mixture of sparse attention for automatic large language model compression.arXiv preprint arXiv:2406.14909, 2024

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T00:21:52.172820Z

Source-reported events for the cited work

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Observation c2f72d89-ac3b-4c8f-8360-2ca3d5b06f43 · outbound

This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T00:21:52.177269Z

Source-reported events for the cited work

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Observation fc66d3c8-698b-4521-a5a1-ea89041de2cb · outbound

This paper cites Vizwiz grand challenge: Answering visual questions from blind people.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models Vizwiz grand challenge: Answering visual questions from blind people

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T00:21:52.182684Z

Source-reported events for the cited work

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Observation 80130e5f-f41b-416d-aeaa-6b63ddd9dc19 · outbound

This paper cites SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension

Reference 19

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Observation 298ad498-c48f-42f5-9415-68f218437624 · outbound

This paper cites Llava-next: Stronger llms supercharge multimodal capa- bilities in the wild, 2024.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models Llava-next: Stronger llms supercharge multimodal capa- bilities in the wild, 2024

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:21:53.411165Z

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Observation 00ee3d80-6cce-4d23-a9b5-4a1f09edcb9d · outbound

This paper cites LLaVA-OneVision: Easy Visual Task Transfer.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models LLaVA-OneVision: Easy Visual Task Transfer

Reference 21

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Observation a71ab9fc-5a5e-4ef9-862b-b3fea50e9344 · outbound

This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T00:21:52.200549Z

Source-reported events for the cited work

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Observation 5492805a-4c72-4c41-aa82-6a7a0658dcf1 · outbound

This paper cites Llm-mq: Mixed-precision quantiza- tion for efficient llm deployment.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models Llm-mq: Mixed-precision quantiza- tion for efficient llm deployment

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:21:53.386113Z

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Observation 7e8fcf70-d007-408d-8ef1-4d849fa730ed · outbound

This paper cites Evaluating quantized large language models.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models Evaluating quantized large language models

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:21:53.370020Z

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Observation 0e393eaf-cb02-4872-bcfd-40bf8cff4b9d · outbound

This paper cites EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty

Reference 25

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Source-reported events for the cited work

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Observation 457cc0aa-c337-4925-99da-fc2f404f3bf1 · outbound

This paper cites MoE-LLaVA: Mixture of Experts for Large Vision-Language Models.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models MoE-LLaVA: Mixture of Experts for Large Vision-Language Models

Reference 26

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Source-reported events for the cited work

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Observation eb250ba1-e5e7-4766-9cd1-548bb4100176 · outbound

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

MBQ: Modality-Balanced Quantization for Large Vision-Language Models Awq: Activation-aware weight quantization for on-device llm compression and acceleration

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:21:53.354930Z

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Observation e5e05113-4ff1-48e2-8401-565607cab953 · outbound

This paper cites Vila: On pre-training for vi- sual language models.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models Vila: On pre-training for vi- sual language models

Reference 28

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Observation 1248ba68-d2f8-48eb-9cc1-147a40f1b0af · outbound

This paper cites QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving

Reference 29

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Source-reported events for the cited work

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Observation e31ec82e-f815-4370-bf47-b7610183a5ff · outbound

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

MBQ: Modality-Balanced Quantization for Large Vision-Language Models Boosting Multimodal Large Language Models with Visual Tokens Withdrawal for Rapid Inference

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T00:21:52.233673Z

Source-reported events for the cited work

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Observation c6f2fe14-8d04-457a-83c6-f411447120fc · outbound

This paper cites Visual instruction tuning.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models Visual instruction tuning

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:21:53.328362Z

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Observation f6438b1d-8b42-40fa-bbe1-d64fc72be943 · outbound

This paper cites Ocrbench: On the hidden mystery of ocr in large multimodal models, 2024.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models Ocrbench: On the hidden mystery of ocr in large multimodal models, 2024

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:21:53.313941Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b7552357-7818-412b-ae44-394082910cfa · outbound

This paper cites SpinQuant: LLM quantization with learned rotations.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models SpinQuant: LLM quantization with learned rotations

Reference 33

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Source-reported events for the cited work

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Observation d6e2d7be-32a6-4958-a20e-3651839a077c · outbound

This paper cites Learn to explain: Multimodal reasoning via thought chains for science question answering.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models Learn to explain: Multimodal reasoning via thought chains for science question answering

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:21:53.299425Z

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Observation fc5e0330-9cfa-46ee-9090-0804226388ec · outbound

This paper cites MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training

Reference 35

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Source-reported events for the cited work

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Observation 999bfc33-81c3-4292-91f9-62db3437da67 · outbound

This paper cites Skeleton-of-thought: Prompting llms for efficient parallel generation.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models Skeleton-of-thought: Prompting llms for efficient parallel generation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:21:53.285355Z

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Observation 87dfa7f9-5307-44c3-b52d-374d49bd534e · outbound

This paper cites VL-Mamba: Exploring State Space Models for Multimodal Learning.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models VL-Mamba: Exploring State Space Models for Multimodal Learning

Reference 37

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Observation f595520b-1b07-41a3-a7cf-a5541f737394 · outbound

This paper cites Llava-prumerge: Adaptive token reduction for efficient large multimodal models.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models Llava-prumerge: Adaptive token reduction for efficient large multimodal models

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-11T00:21:52.269865Z

Source-reported events for the cited work

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Observation d7a20f75-d9d6-46fa-9a54-149ee760f6ab · outbound

This paper cites OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-11T00:21:52.274118Z

Source-reported events for the cited work

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Observation ec444c70-24b4-43c2-a44e-a7bb796c0678 · outbound

This paper cites Towards vqa models that can read.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models Towards vqa models that can read

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:21:53.270017Z

Source-reported events for the cited work

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Observation 84f6cc41-98bc-4567-8db8-f5aa81e0f34e · outbound

This paper cites FlatQuant: Flatness Matters for LLM Quantization.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models FlatQuant: Flatness Matters for LLM Quantization

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-11T00:21:52.282753Z

Source-reported events for the cited work

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Observation 63b7a57d-0ecd-4844-8583-3ab40b3369d3 · outbound

This paper cites Attention is all you need.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models Attention is all you need

Reference 42

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Source-reported events for the cited work

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Observation 0999704a-26e5-418d-9632-184d2537b9a9 · outbound

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

MBQ: Modality-Balanced Quantization for Large Vision-Language Models Smoothquant: Accurate and effi- cient post-training quantization for large language models

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:21:53.245362Z

Source-reported events for the cited work

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Observation bb5e6cbd-ce04-42d2-8e04-52df6428f9a5 · outbound

This paper cites Efficient Streaming Language Models with Attention Sinks.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models Efficient Streaming Language Models with Attention Sinks

Reference 44

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Source-reported events for the cited work

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Observation ca19e749-1942-4d8e-87fb-4d3f901b9cce · outbound

This paper cites LLaVA-UHD: an LMM Perceiving Any Aspect Ratio and High-Resolution Images.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models LLaVA-UHD: an LMM Perceiving Any Aspect Ratio and High-Resolution Images

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-11T00:21:52.300528Z

Source-reported events for the cited work

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Observation e13bac50-265a-49da-8a76-8133c812febf · outbound

This paper cites ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-11T00:21:52.305041Z

Source-reported events for the cited work

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Observation f1f7cbd6-4511-47d1-a7eb-990d43074858 · outbound

This paper cites Mmmu: A massive multi-discipline mul- timodal understanding and reasoning benchmark for expert agi.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models Mmmu: A massive multi-discipline mul- timodal understanding and reasoning benchmark for expert agi

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:21:53.230644Z

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Observation 92f982ef-741a-470e-9b61-2fe9c039edb3 · outbound

This paper cites Sigmoid loss for language image pre-training,.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models Sigmoid loss for language image pre-training,

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-11T00:21:52.312700Z

Source-reported events for the cited work

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Observation b95bd41c-7765-48b4-953f-85419d44d3b8 · outbound

This paper cites Lmms- eval: Reality check on the evaluation of large multimodal models, 2024.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models Lmms- eval: Reality check on the evaluation of large multimodal models, 2024

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:21:53.205977Z

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Observation 3af66ba1-ac2d-419c-b818-cf3e37abc2ac · outbound

This paper cites Debi- asing multimodal large language models.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models Debi- asing multimodal large language models

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:21:53.192263Z

Source-reported events for the cited work

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Observation 13e58d4b-495e-40be-ab0f-72f490fca7d7 · outbound

This paper cites H2o: Heavy-hitter ora- cle for efficient generative inference of large language mod- els.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models H2o: Heavy-hitter ora- cle for efficient generative inference of large language mod- els

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:21:53.178261Z

Source-reported events for the cited work

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Observation 7b56f215-0911-45c3-8102-17d9289bdd3a · outbound

This paper cites Cobra: Extending Mamba to Multi-Modal Large Language Model for Efficient Inference.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models Cobra: Extending Mamba to Multi-Modal Large Language Model for Efficient Inference

Reference 52

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Reference 53

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MBQ: Modality-Balanced Quantization for Large Vision-Language Models A Survey on Efficient Inference for Large Language Models

Reference 54

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MBQ: Modality-Balanced Quantization for Large Vision-Language Models Llava-phi: Efficient multi-modal as- sistant with small language model, 2024

Reference 55

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MBQ: Modality-Balanced Quantization for Large Vision-Language Models The Inference Process of VLMs The inference process of VLMs is shown in Fig

Reference 56

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MBQ: Modality-Balanced Quantization for Large Vision-Language Models LLM Quantization Post-Training Quantization (PTQ) techniques are widely used in LLMs to accelerate the inference process

Reference 57

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Reference 58

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Reference 59

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Reference 60

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Reference 61

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Reference 62

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Reference 63

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Pith citing papers

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MQuant: Unleashing the Inference Potential of Multimodal Large Language Models via Full Static Quantization cites this paper.

MQuant: Unleashing the Inference Potential of Multimodal Large Language Models via Full Static Quantization MBQ: Modality-Balanced Quantization for Large Vision-Language Models

Reference 38

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DREAM-S: Speculative Decoding with Searchable Drafting and Target-Aware Refinement for Multimodal Generation cites this paper.

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Reference 51

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LASER: Loss-Aware Singular-value Decomposition and Rank Allocation for Efficient Low-Precision Vision-Language Models cites this paper.

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Reference 25

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SAB-LVLM: Significance-Aware Binarization for Large Vision-Language Models cites this paper.

SAB-LVLM: Significance-Aware Binarization for Large Vision-Language Models MBQ: Modality-Balanced Quantization for Large Vision-Language Models

Reference 6

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