Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation | Paper Citation Record

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This paper cites Claude v3.0, 2024.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Claude v3.0, 2024

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This paper cites ShapeNet: An Information-Rich 3D Model Repository.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation ShapeNet: An Information-Rich 3D Model Repository

Reference 2

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This paper cites Spatialvlm: Endow- ing vision-language models with spatial reasoning capabili- ties.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Spatialvlm: Endow- ing vision-language models with spatial reasoning capabili- ties

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This paper cites ALLaVA: Harnessing GPT4V-Synthesized Data for Lite Vision-Language Models.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation ALLaVA: Harnessing GPT4V-Synthesized Data for Lite Vision-Language Models

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

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks

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This paper cites SpatialRGPT: Grounded Spatial Reasoning in Vision Language Models.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation SpatialRGPT: Grounded Spatial Reasoning in Vision Language Models

Reference 6

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This paper cites Gonzalez, Ion Stoica, and Eric P.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Gonzalez, Ion Stoica, and Eric P

Reference 7

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This paper cites Instructblip: Towards general- purpose vision-language models with instruction tuning,.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Instructblip: Towards general- purpose vision-language models with instruction tuning,

Reference 8

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This paper cites Objaverse: A universe of annotated 3d objects.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Objaverse: A universe of annotated 3d objects

Reference 9

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Observation 9ace1d4d-271b-4046-a81c-b2968a3a5186 · outbound

This paper cites Objaverse-xl: A universe of 10m+ 3d objects.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Objaverse-xl: A universe of 10m+ 3d objects

Reference 10

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Observation 67c8ed4e-ed69-437f-8081-08a895706f20 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 11

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Observation e86d4e51-015f-42ad-bb9f-5d43029e79b4 · outbound

This paper cites What Makes for Good Visual Instructions? Synthesizing Complex Visual Reasoning Instructions for Visual Instruction Tuning.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation What Makes for Good Visual Instructions? Synthesizing Complex Visual Reasoning Instructions for Visual Instruction Tuning

Reference 12

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Observation 336ad5c2-4776-4d06-ae92-5c7c304cd5a7 · outbound

This paper cites Training on Synthetic Data Beats Real Data in Multimodal Relation Extraction.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Training on Synthetic Data Beats Real Data in Multimodal Relation Extraction

Reference 13

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This paper cites BLINK: Multimodal Large Language Models Can See but Not Perceive.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation BLINK: Multimodal Large Language Models Can See but Not Perceive

Reference 14

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This paper cites Making the v in vqa matter: Elevating the role of image understanding in visual question answer- ing.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Making the v in vqa matter: Elevating the role of image understanding in visual question answer- ing

Reference 15

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This paper cites Kubric: A scalable dataset generator.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Kubric: A scalable dataset generator

Reference 16

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This paper cites Regiongpt: Towards region understanding vision lan- guage model.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Regiongpt: Towards region understanding vision lan- guage model

Reference 17

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Observation 953075ef-eae8-48c0-b500-13bd6b9b84be · outbound

This paper cites Lvis: A dataset for large vocabulary instance segmentation.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Lvis: A dataset for large vocabulary instance segmentation

Reference 18

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

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Vizwiz grand challenge: Answering visual questions from blind people

Reference 19

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This paper cites Prompt-to-Prompt Image Editing with Cross Attention Control.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Prompt-to-Prompt Image Editing with Cross Attention Control

Reference 20

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This paper cites CLIPScore: A Reference-free Evaluation Metric for Image Captioning.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation CLIPScore: A Reference-free Evaluation Metric for Image Captioning

Reference 21

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This paper cites Gans trained by a two time-scale update rule converge to a local nash equilib- rium.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Gans trained by a two time-scale update rule converge to a local nash equilib- rium

Reference 22

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This paper cites Denoising dif- fusion probabilistic models.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Denoising dif- fusion probabilistic models

Reference 23

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This paper cites Gqa: A new dataset for real-world visual reasoning and compositional question answering.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Gqa: A new dataset for real-world visual reasoning and compositional question answering

Reference 24

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This paper cites Clevr: A diagnostic dataset for compositional language and elementary visual reasoning.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Clevr: A diagnostic dataset for compositional language and elementary visual reasoning

Reference 25

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This paper cites Visual genome: Connecting language and vision using crowdsourced dense image annotations.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Visual genome: Connecting language and vision using crowdsourced dense image annotations

Reference 26

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This paper cites Learning Action and Reasoning-Centric Image Editing from Videos and Simulations.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Learning Action and Reasoning-Centric Image Editing from Videos and Simulations

Reference 27

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This paper cites LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models

Reference 28

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

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 29

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This paper cites What If We Recaption Billions of Web Images with LLaMA-3?.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation What If We Recaption Billions of Web Images with LLaMA-3?

Reference 30

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This paper cites StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data

Reference 31

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Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Vila: On pre-training for vi- sual language models

Reference 32

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Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Microsoft coco: Common objects in context

Reference 33

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Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Visual spa- tial reasoning

Reference 34

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This paper cites Improved baselines with visual instruction tuning.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Improved baselines with visual instruction tuning

Reference 35

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Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Visual instruction tuning

Reference 36

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Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Clevr-ref+: Diagnosing visual reasoning with referring ex- pressions

Reference 37

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Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation SynthVLM: Towards High-Quality and Efficient Synthesis of Image-Caption Datasets for Vision-Language Models

Reference 38

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

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Learn to explain: Multimodal reasoning via thought chains for science question answering

Reference 39

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Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Generating images with 3d annotations using diffusion models

Reference 40

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This paper cites Ok-vqa: A visual question answering benchmark requiring external knowledge.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Ok-vqa: A visual question answering benchmark requiring external knowledge

Reference 41

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This paper cites Llama 3.2 vision, 2024.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Llama 3.2 vision, 2024

Reference 42

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This paper cites Ocr-vqa: Visual question answering by reading text in images.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Ocr-vqa: Visual question answering by reading text in images

Reference 43

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This paper cites GPT-4 Technical Report.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation GPT-4 Technical Report

Reference 44

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Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation DINOv2: Learning Robust Visual Features without Supervision

Reference 45

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Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 46

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Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation High-resolution image synthesis with latent diffusion models

Reference 47

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This paper cites Imagenet large scale visual recognition challenge.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Imagenet large scale visual recognition challenge

Reference 48

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This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Laion-5b: An open large-scale dataset for training next generation image-text models

Reference 49

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Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Synth$^2$: Boosting Visual-Language Models with Synthetic Captions and Image Embeddings

Reference 50

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This paper cites Towards vqa models that can read.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Towards vqa models that can read

Reference 51

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This paper cites Denoising Diffusion Implicit Models.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Denoising Diffusion Implicit Models

Reference 52

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This paper cites Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs

Reference 53

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This paper cites Eyes wide shut? exploring the visual shortcomings of multimodal llms.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Eyes wide shut? exploring the visual shortcomings of multimodal llms

Reference 54

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Observation 57934c97-273e-42da-9534-3393f50de742 · outbound

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

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation LLaMA: Open and Efficient Foundation Language Models

Reference 55

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This paper cites To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning

Reference 56

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This paper cites Imagen editor and editbench: Advancing and evaluating text-guided im- age inpainting.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Imagen editor and editbench: Advancing and evaluating text-guided im- age inpainting

Reference 57

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Observation fd5b5f6f-9e11-4231-831c-620c733cfe23 · outbound

This paper cites Mebow: Monocular estima- tion of body orientation in the wild.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Mebow: Monocular estima- tion of body orientation in the wild

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:25:19.390347Z

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This paper cites Beyond pascal: A benchmark for 3d object detection in the wild.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Beyond pascal: A benchmark for 3d object detection in the wild

Reference 59

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This paper cites Imagere- ward: Learning and evaluating human preferences for text- to-image generation.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Imagere- ward: Learning and evaluating human preferences for text- to-image generation

Reference 60

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Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)

Reference 61

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Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation From image descriptions to visual denotations: New similarity metrics for semantic inference over event descrip- tions

Reference 62

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Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Modeling context in referring expres- sions

Reference 63

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This paper cites When and why vision-language models behave like bags-of-words, and what to do about it?.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation When and why vision-language models behave like bags-of-words, and what to do about it?

Reference 64

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This paper cites Magicbrush: A manually annotated dataset for instruction- guided image editing.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Magicbrush: A manually annotated dataset for instruction- guided image editing

Reference 65

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This paper cites Adding conditional control to text-to-image diffusion models.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Adding conditional control to text-to-image diffusion models

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:24:57.037280Z

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Observation 40b253b0-067d-4f80-b54f-90b17167e97a · outbound

This paper cites LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding

Reference 67

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Observation c59fd0ac-0659-49c7-88c2-f56a3efe1277 · outbound

This paper cites LLaVA-Video: Video Instruction Tuning With Synthetic Data.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation LLaVA-Video: Video Instruction Tuning With Synthetic Data

Reference 68

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This paper cites Semantic under- standing of scenes through the ade20k dataset.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Semantic under- standing of scenes through the ade20k dataset

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:24:56.904192Z

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Observation 7931a257-a464-4c0c-b3df-4fbbc23f9357 · outbound

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Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Corresponding section in main paper is Sec

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:24:56.816980Z

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This paper cites front" also means.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation front" also means

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:24:56.728795Z

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This paper cites SYSTEM_PROMPT_FOR_GRADING_MLLM_RESPONSE =’You are a helpful and precise assistant for checking the quality of the answer.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation SYSTEM_PROMPT_FOR_GRADING_MLLM_RESPONSE =’You are a helpful and precise assistant for checking the quality of the answer

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:24:56.651563Z

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This paper cites 6, we show an example of using ImageReward [60] for the dataset curation by evaluating the alignment be- tween generated image and text prompts.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation 6, we show an example of using ImageReward [60] for the dataset curation by evaluating the alignment be- tween generated image and text prompts

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:24:56.549201Z

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Observation a833ed89-9c6d-4823-a8ec-80624261cff8 · outbound

This paper cites 3, we perform quantitative comparisons on general image visual quality between different DM backbones: SD V1.5 and SDXL.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation 3, we perform quantitative comparisons on general image visual quality between different DM backbones: SD V1.5 and SDXL

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:24:56.468624Z

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Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Unresolved cited work

Reference 75

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Observation 6fd6cb42-cbf1-427b-a8aa-f8f26501081d · outbound

This paper cites 9 shows the UI page of our user study.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation 9 shows the UI page of our user study

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:24:56.320779Z

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Observation f80025b9-0df0-4d73-a614-011c00d5989c · outbound

This paper cites 10 to show more qualitative comparisons between fine- tuned LLaV A model to commercial SOTAs.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation 10 to show more qualitative comparisons between fine- tuned LLaV A model to commercial SOTAs

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:24:56.259557Z

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Observation ce8877c5-f5bf-41fc-b1a7-c7c842b22480 · outbound

This paper cites 11, we shows more examples of diversity on object categories, camera-object relation, and background con- texts.

Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation 11, we shows more examples of diversity on object categories, camera-object relation, and background con- texts

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:24:56.181755Z

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Observation 7b09579b-8e9e-4749-9a60-76040f022433 · outbound

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Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Unresolved cited work

Reference 79

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Observation ad2ed1f9-ff4d-4f55-9408-d2fbb7629d6e · outbound

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Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation Unresolved cited work

Reference 80

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:24:55.986032Z

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