Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models | Paper Citation Record

Observation f0c5ce36-7756-4d7f-99c4-076090780625 · outbound

This paper cites Tallyqa: Answering complex counting questions.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Tallyqa: Answering complex counting questions

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This paper cites Macmillan, 2005.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Macmillan, 2005

Reference 2

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

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

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This paper cites Graph of thoughts: Solving elaborate problems with large language models.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Graph of thoughts: Solving elaborate problems with large language models

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This paper cites Vizwiz: nearly real-time answers to visual questions.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Vizwiz: nearly real-time answers to visual questions

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This paper cites Due: End-to-end document understanding benchmark.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Due: End-to-end document understanding benchmark

Reference 6

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This paper cites Allava: Harnessing gpt4v-synthesized data for a lite vision-language model, 2024.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Allava: Harnessing gpt4v-synthesized data for a lite vision-language model, 2024

Reference 7

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This paper cites Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic

Reference 8

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

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models ShareGPT4V: Improving Large Multi-Modal Models with Better Captions

Reference 9

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Observation 65924100-3232-4961-af83-da8e2ddea302 · outbound

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

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Microsoft COCO Captions: Data Collection and Evaluation Server

Reference 10

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

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Gonzalez, Ion Stoica, and Eric P

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This paper cites Instructblip: Towards general-purpose vision-language models with instruction tuning, 2023.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Instructblip: Towards general-purpose vision-language models with instruction tuning, 2023

Reference 12

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Observation 7a573766-a33e-4c3b-a218-901eee60fb5d · outbound

This paper cites Enhancing Chat Language Models by Scaling High-quality Instructional Conversations.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Enhancing Chat Language Models by Scaling High-quality Instructional Conversations

Reference 13

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Observation 417f4e4c-8ac4-44a4-ab4c-d8069a1cf6ed · outbound

This paper cites LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model

Reference 14

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Observation 955f3862-cd6d-425b-9487-cacf88b4967d · outbound

This paper cites Shortcut learning in deep neural networks.Nature Machine Intelligence, 2(11):665–673, 2020.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Shortcut learning in deep neural networks.Nature Machine Intelligence, 2(11):665–673, 2020

Reference 15

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This paper cites Flacuna: Unleashing the problem solving power of vicuna using flan fine-tuning, 2023.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Flacuna: Unleashing the problem solving power of vicuna using flan fine-tuning, 2023

Reference 16

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This paper cites Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Reference 17

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

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Making the v in vqa matter: Elevating the role of image understanding in visual question answering

Reference 18

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Observation 92a0534e-ba85-4e6b-94bb-03596443725c · outbound

This paper cites Visual programming: Compositional visual reasoning without training.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Visual programming: Compositional visual reasoning without training

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This paper cites GREC: Generalized Referring Expression Comprehension.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models GREC: Generalized Referring Expression Comprehension

Reference 20

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This paper cites spaCy: Industrial-strength Natural Language Processing in Python.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models spaCy: Industrial-strength Natural Language Processing in Python

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This paper cites Visual program distillation: Distilling tools and programmatic reasoning into vision-language models.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Visual program distillation: Distilling tools and programmatic reasoning into vision-language models

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Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Hudson and Christopher D

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This paper cites Vcoder: Versatile vision encoders for multimodal large lan- guage models.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Vcoder: Versatile vision encoders for multimodal large lan- guage models

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

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Clevr: A diagnostic dataset for compositional language and elementary visual reasoning

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This paper cites Dvqa: Understanding data visualizations via question answering.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Dvqa: Understanding data visualizations via question answering

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This paper cites Mdetr-modulated detection for end-to-end multi-modal understanding.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Mdetr-modulated detection for end-to-end multi-modal understanding

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This paper cites A diagram is worth a dozen images.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models A diagram is worth a dozen images

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This paper cites Ocr-free document understanding transformer.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Ocr-free document understanding transformer

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Observation fde02abc-151c-4d07-8be9-3d5787e222d9 · outbound

This paper cites Large language models are zero-shot reasoners.Advances in neural information processing systems, 35:22199–22213, 2022.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Large language models are zero-shot reasoners.Advances in neural information processing systems, 35:22199–22213, 2022

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Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Shamma, Michael S

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This paper cites Scaffolding Coordinates to Promote Vision-Language Coordination in Large Multi-Modal Models.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Scaffolding Coordinates to Promote Vision-Language Coordination in Large Multi-Modal Models

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

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension

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Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

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Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Evaluating Object Hallucination in Large Vision-Language Models

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This paper cites VoCoT: Unleashing Visually Grounded Multi-Step Reasoning in Large Multi-Modal Models.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models VoCoT: Unleashing Visually Grounded Multi-Step Reasoning in Large Multi-Modal Models

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This paper cites Monkey: Image resolution and text label are important things for large multi-modal models.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Monkey: Image resolution and text label are important things for large multi-modal models

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This paper cites Openorca: An open dataset of gpt augmented flan reasoning traces.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Openorca: An open dataset of gpt augmented flan reasoning traces

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Observation a2e8eb45-df10-44a8-8d88-9f589bc5a246 · outbound

This paper cites Microsoft coco: Common objects in context.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Microsoft coco: Common objects in context

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This paper cites Sphinx-x: Scaling data and parameters for a family of multi-modal large language models.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Sphinx-x: Scaling data and parameters for a family of multi-modal large language models

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This paper cites Visual spatial reasoning.Transactions of the Association for Computational Linguistics, 11:635–651, 2023.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Visual spatial reasoning.Transactions of the Association for Computational Linguistics, 11:635–651, 2023

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This paper cites Improved baselines with visual instruction tuning, 2023.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Improved baselines with visual instruction tuning, 2023

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Observation 63cea63f-afb5-4efd-bb9f-2b4d68737187 · outbound

This paper cites Llava-next: Improved reasoning, ocr, and world knowledge, 2024.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Llava-next: Improved reasoning, ocr, and world knowledge, 2024

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Observation 9b47bb85-f4b1-4233-ab38-4ddb71c2176b · outbound

This paper cites Visual instruction tuning.Advances in neural information processing systems, 36, 2024.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Visual instruction tuning.Advances in neural information processing systems, 36, 2024

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This paper cites Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

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Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models MMBench: Is Your Multi-modal Model an All-around Player?

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This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models SGDR: Stochastic Gradient Descent with Warm Restarts

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Observation 1c56508f-e269-44b5-96d9-07a2bec943ec · outbound

This paper cites Decoupled Weight Decay Regularization.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Decoupled Weight Decay Regularization

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Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models DeepSeek-VL: Towards Real-World Vision-Language Understanding

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

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Learn to explain: Multimodal reasoning via thought chains for science question answering

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This paper cites WizardCoder: Empowering Code Large Language Models with Evol-Instruct.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models WizardCoder: Empowering Code Large Language Models with Evol-Instruct

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

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Ok-vqa: A visual question answering benchmark requiring external knowledge

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Observation f18f105c-723e-4f69-bc5d-835bcd223f7d · outbound

This paper cites ChartQA: A benchmark for question answering about charts with visual and logical reasoning.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models ChartQA: A benchmark for question answering about charts with visual and logical reasoning

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:41.064270Z

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Observation 99fdb9fa-25a6-4dd7-960c-7f37b6558ffc · outbound

This paper cites DocVQA: A Dataset for VQA on Document Images.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models DocVQA: A Dataset for VQA on Document Images

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Observation c7fb0e1e-04da-47cc-bf80-2bb65dc0b676 · outbound

This paper cites Infographicvqa.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Infographicvqa

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Observation d5a71263-a21a-4380-abef-d0154ff3ec05 · outbound

This paper cites Schema theory revisited.Review of educational research, 75(4):531–566, 2005.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Schema theory revisited.Review of educational research, 75(4):531–566, 2005

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:40.764860Z

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Observation f8731295-39b9-4461-a89c-a346ff2d16ed · outbound

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

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Ocr-vqa: Visual question answering by reading text in images

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:40.564102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 50c017f3-90c0-4df5-a43d-a94f52ab6a77 · outbound

This paper cites Compositional chain of thought prompting for large multimodal models.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Compositional chain of thought prompting for large multimodal models

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:40.342412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c4c16501-60db-4847-a6c4-4a5b7eb6b375 · outbound

This paper cites Modeling context between objects for referring expression understanding.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Modeling context between objects for referring expression understanding

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:25.230063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04158600-31a1-4870-a91c-ace12a753643 · outbound

This paper cites Gpt-4 technical report, 2023.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Gpt-4 technical report, 2023

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:25.330170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 644297db-8b94-4964-ad8d-d9d0c68fa519 · outbound

This paper cites Chatgpt: A large language model for natural language processing, 2024.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Chatgpt: A large language model for natural language processing, 2024

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:40.097424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8742e0ac-8199-418e-b456-4aa0f5a1a862 · outbound

This paper cites Learning to predict visual attributes in the wild.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Learning to predict visual attributes in the wild

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:39.829599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e55e88bc-d4d8-4cd2-9c50-ea5b04d010a0 · outbound

This paper cites Plummer, Liwei Wang, Chris M.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Plummer, Liwei Wang, Chris M

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:39.502500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 43f3024b-f2bc-4c52-91c7-2f7eddfb5c10 · outbound

This paper cites Jack of all tasks master of many: Designing general-purpose coarse-to-fine vision-language model.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Jack of all tasks master of many: Designing general-purpose coarse-to-fine vision-language model

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:39.286746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation efde47e4-f32c-43f0-8ffa-cd59057f1d59 · outbound

This paper cites CogCoM: A Visual Language Model with Chain-of-Manipulations Reasoning.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models CogCoM: A Visual Language Model with Chain-of-Manipulations Reasoning

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:25.907620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 444391bf-6e3d-43a6-988c-750be1226fc7 · outbound

This paper cites Learning transferable visual models from natural language supervision.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Learning transferable visual models from natural language supervision

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:25.963697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc710403-a39d-40f4-a2ec-e273c051da41 · outbound

This paper cites Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:26.136393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d14563a-fa7f-4563-b0d4-e07d0bba71c7 · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region proposal networks.Advances in neural information processing systems, 28, 2015.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Faster r-cnn: Towards real-time object detection with region proposal networks.Advances in neural information processing systems, 28, 2015

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:39.065652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 40e0e4a3-f065-4fdc-94c8-bfc9dde9bf10 · outbound

This paper cites A- okvqa: A benchmark for visual question answering using world knowledge.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models A- okvqa: A benchmark for visual question answering using world knowledge

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:38.744111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f446b261-ed47-44a9-b1e3-e3c89900bad2 · outbound

This paper cites Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoning.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoning

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:26.608996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a79b54ec-36a7-41a4-9f59-194a176e5c8c · outbound

This paper cites Objects365: A large-scale, high-quality dataset for object detection.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Objects365: A large-scale, high-quality dataset for object detection

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:26.721908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d96e4623-5ba0-4a39-a2e4-78bb8261499a · outbound

This paper cites Woodpecker: Hallucination Correction for Multimodal Large Language Models.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Woodpecker: Hallucination Correction for Multimodal Large Language Models

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:26.829722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33b36a20-3f15-494b-9b7e-85c21bb343e3 · outbound

This paper cites Textcaps: a dataset for image captioning with reading comprehension.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Textcaps: a dataset for image captioning with reading comprehension

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:26.964879Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85c83ac2-07e3-4182-ac75-6bc7c05496cb · outbound

This paper cites Towards vqa models that can read.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Towards vqa models that can read

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:27.138590Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63324793-9ecd-451d-83e5-20c423547bf7 · outbound

This paper cites Vipergpt: Visual inference via python execution for reasoning.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Vipergpt: Visual inference via python execution for reasoning

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:27.298358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6a94df1-76aa-44e5-b482-72557d258da4 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Gemini: A Family of Highly Capable Multimodal Models

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:27.425640Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4576e48-1534-43e1-841f-2936f52b26de · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Gemma 2: Improving Open Language Models at a Practical Size

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:27.674640Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8556a4e-68e7-4b55-b6a2-e46ddf9619c1 · outbound

This paper cites Qwen2.5: A party of foundation models, 2024.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Qwen2.5: A party of foundation models, 2024

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:38.385503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 00b24c17-26cc-4e54-9912-f809d9449f77 · outbound

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

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:28.020503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7227ddf-c6ae-4f25-a30b-dc91397c878b · outbound

This paper cites V3det: Vast vocabulary visual detection dataset.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models V3det: Vast vocabulary visual detection dataset

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:38.108520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7ea5dd24-af13-40fd-b158-c0121e0842bd · outbound

This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:28.436180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6664e084-8326-4525-88ac-8aa5900c3b19 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems, 35:24824–24837, 2022.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems, 35:24824–24837, 2022

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:28.654535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99fb6267-65f7-4ac8-bcbe-8a35e9da365b · outbound

This paper cites V?: Guided visual search as a core mechanism in multimodal llms.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models V?: Guided visual search as a core mechanism in multimodal llms

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:28.800849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64d076da-b523-4ae9-95ed-dfada6d68c35 · outbound

This paper cites Universal instance perception as object discovery and retrieval.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Universal instance perception as object discovery and retrieval

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:37.854933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ceb3ff64-846a-4a98-bd96-1e607ffe50f1 · outbound

This paper cites Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:29.090452Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bce61a9-aea9-4e95-9caf-c3fdd31c82ca · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models.Advances in Neural Information Processing Systems, 36, 2024.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Tree of thoughts: Deliberate problem solving with large language models.Advances in Neural Information Processing Systems, 36, 2024

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:29.715909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47d9c578-d58f-4748-af17-7ea6ee6efd43 · outbound

This paper cites Ferret: Refer and ground anything anywhere at any granularity.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Ferret: Refer and ground anything anywhere at any granularity

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:37.509787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 81556e13-6f8a-4852-8d99-066d8bf27378 · outbound

This paper cites Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:32.111913Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c74538d7-e8a4-4a2b-b73e-4170ddac37f4 · outbound

This paper cites Modeling context in referring expressions.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Modeling context in referring expressions

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:32.216567Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88ee7ea1-47a9-43a7-870b-bd16d1a7f6d9 · outbound

This paper cites MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:32.326619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aad44938-ec86-46b1-9972-491612c46e4b · outbound

This paper cites Osprey: Pixel understanding with visual instruction tuning.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Osprey: Pixel understanding with visual instruction tuning

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:32.463254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1db65240-c2c3-444a-a8f7-6bbfac99a870 · outbound

This paper cites MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:32.600372Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60d458aa-0cd3-4836-96de-c49499754562 · outbound

This paper cites Griffon: Spelling out all object locations at any granularity with large language models.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Griffon: Spelling out all object locations at any granularity with large language models

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:37.294153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 250fc1e9-7210-495a-9cb2-b8a494f739d5 · outbound

This paper cites Automatic Chain of Thought Prompting in Large Language Models.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Automatic Chain of Thought Prompting in Large Language Models

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-07T13:53:32.834055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43994f56-3434-4817-80de-4d69d73512ac · outbound

This paper cites yes” or “no.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models yes” or “no

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:37.033887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 420e162c-67b3-4f6f-9d11-b5510053330e · outbound

This paper cites an unresolved cited work.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Unresolved cited work

Reference 96

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:53:36.655327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f776522e-df36-4c05-9dbb-8041809823c6 · outbound

This paper cites an unresolved cited work.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Unresolved cited work

Reference 98

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:53:36.188645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8fa7385a-6ddc-471a-ab2d-f46df1dbc8bb · outbound

This paper cites an unresolved cited work.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Unresolved cited work

Reference 100

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:53:35.804909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation af56e164-1237-4768-94ab-da81c32ec363 · outbound

This paper cites an unresolved cited work.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Unresolved cited work

Reference 101

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:53:35.445068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1d7846c4-193e-4223-9e09-2d8cbd0b1552 · outbound

This paper cites an unresolved cited work.

Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models Unresolved cited work

Reference 102

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:53:36.375321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Reference resolution

Outbound references

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