MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning | Paper Citation Record

Observation 5478395a-a3e2-4910-ab88-b83667be0c36 · outbound

This paper cites Qwen-vl: A versatile vision-language model for un- derstanding, localization, text reading, and beyond.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Qwen-vl: A versatile vision-language model for un- derstanding, localization, text reading, and beyond

Reference 1

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This paper cites Qwen2.5-VL Technical Report.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Qwen2.5-VL Technical Report

Reference 2

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This paper cites Jawahar, Ernest Valveny, and Dimos- thenis Karatzas.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Jawahar, Ernest Valveny, and Dimos- thenis Karatzas

Reference 3

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Observation 5e75490b-058d-4a84-81f8-52a64d95e4fb · outbound

This paper cites An augmented benchmark dataset for geometric question answering through dual parallel text en- coding.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning An augmented benchmark dataset for geometric question answering through dual parallel text en- coding

Reference 4

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Observation 3baeeb8c-e4ef-4928-8cff-4e32bae54d62 · outbound

This paper cites FireAct: Toward Language Agent Fine-tuning.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning FireAct: Toward Language Agent Fine-tuning

Reference 5

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Observation 463991f0-cc00-40b0-8798-d05ee1827c01 · outbound

This paper cites Sharegpt4v: Improving large multi-modal models with better captions.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Sharegpt4v: Improving large multi-modal models with better captions

Reference 6

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This paper cites Are we on the right way for evaluating large vision-language models? In The Thirty-eighth Annual Con- ference on Neural Information Processing Systems, 2024.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Are we on the right way for evaluating large vision-language models? In The Thirty-eighth Annual Con- ference on Neural Information Processing Systems, 2024

Reference 7

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Observation c5d95906-1dc0-47af-9f83-a6d76b8c75b1 · outbound

This paper cites M$^3$CoT: A Novel Benchmark for Multi-Domain Multi-step Multi-modal Chain-of-Thought.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning M$^3$CoT: A Novel Benchmark for Multi-Domain Multi-step Multi-modal Chain-of-Thought

Reference 8

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Observation b2b48a63-6335-45e0-b0b6-35b2fc97d4c6 · outbound

This paper cites Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?

Reference 9

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Observation b41b9cc9-cd38-4c58-9845-d34dd9504529 · outbound

This paper cites Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models

Reference 10

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Observation 92c9f48c-c1af-4cba-885d-a314261a25bf · outbound

This paper cites Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

Reference 11

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Observation 15d9bcc5-8cbf-46d3-96b5-c0b01a6075ea · outbound

This paper cites How far are we to gpt-4v? closing the gap to commercial multimodal models with open-source suites.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning How far are we to gpt-4v? closing the gap to commercial multimodal models with open-source suites

Reference 12

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Observation a80648dd-7b1d-458c-ab83-446322737701 · outbound

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

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks

Reference 13

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Observation 971d3421-3b28-423f-9ecc-977122187998 · outbound

This paper cites Clevr-math: A dataset for compositional language, visual and mathematical reasoning.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Clevr-math: A dataset for compositional language, visual and mathematical reasoning

Reference 14

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Observation 4f5df2a3-68f0-4fb0-9d97-992800e1cd89 · outbound

This paper cites PP-OCR: A Practical Ultra Lightweight OCR System.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning PP-OCR: A Practical Ultra Lightweight OCR System

Reference 15

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Observation b5973dad-5e46-4e7a-9168-241b722a9d96 · outbound

This paper cites Vlmevalkit: An open-source toolkit for evaluating large multi-modality models.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Vlmevalkit: An open-source toolkit for evaluating large multi-modality models

Reference 16

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Observation b764f13b-cc2a-4907-983f-74e1c22f8fe0 · outbound

This paper cites AssistGPT: A General Multi-modal Assistant that can Plan, Execute, Inspect, and Learn.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning AssistGPT: A General Multi-modal Assistant that can Plan, Execute, Inspect, and Learn

Reference 17

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Observation 0b4a3181-5f0a-43df-9a38-1b3f554a584c · outbound

This paper cites Multi-modal agent tuning: Building a vlm-driven agent for efficient tool usage.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Multi-modal agent tuning: Building a vlm-driven agent for efficient tool usage

Reference 18

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Observation e63cb2f6-1627-44cd-8a77-77aa298bd6b9 · outbound

This paper cites Hallusionbench: an advanced diagnos- tic suite for entangled language hallucination and visual il- lusion in large vision-language models.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Hallusionbench: an advanced diagnos- tic suite for entangled language hallucination and visual il- lusion in large vision-language models

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:52.871053Z

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Observation 1d71c6f4-9663-4f51-bedf-903cc3f88412 · outbound

This paper cites Lora: Low-rank adaptation of large language models.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Lora: Low-rank adaptation of large language models

Reference 20

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Observation eae346d7-dea3-4869-a0fd-8ff980354b34 · outbound

This paper cites Icdar2019 compe- tition on scanned receipt ocr and information extraction.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Icdar2019 compe- tition on scanned receipt ocr and information extraction

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:52.676694Z

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Observation 3877697a-21a2-4942-8ffc-e5c49755e270 · outbound

This paper cites Gqa: A new dataset for real-world visual reasoning and compositional 9 question answering.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Gqa: A new dataset for real-world visual reasoning and compositional 9 question answering

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:52.425932Z

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Observation 7624ac31-6ceb-4462-b1f2-92689753961f · outbound

This paper cites GPT-4o System Card.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning GPT-4o System Card

Reference 23

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Observation e6c32f4b-80b9-4a92-ba9d-501d5d813541 · outbound

This paper cites Lawrence Zitnick, and Ross Girshick.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Lawrence Zitnick, and Ross Girshick

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:52.105623Z

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Observation 3cde0bb2-3704-4693-af26-65dc37e6cc7b · outbound

This paper cites A diagram is worth a dozen images.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning A diagram is worth a dozen images

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:51.795759Z

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Observation f257a9f9-e7c1-4d5c-a8b0-4517f4a3ed53 · outbound

This paper cites Are you smarter than a sixth grader? textbook question answer- ing for multimodal machine comprehension.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Are you smarter than a sixth grader? textbook question answer- ing for multimodal machine comprehension

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:51.269990Z

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Observation 71b10f2c-bc23-4add-b219-882ce0430e63 · outbound

This paper cites The hateful memes challenge: Detecting hate speech in multimodal memes.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning The hateful memes challenge: Detecting hate speech in multimodal memes

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:50.997805Z

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Observation 126b78f9-f3f7-4151-8c5e-c6987d0b1265 · outbound

This paper cites The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:50.784892Z

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Observation cb91d659-f428-42cd-9432-dd29556578f5 · outbound

This paper cites What matters when building vision-language models?.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning What matters when building vision-language models?

Reference 29

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Observation 32ee5be9-6523-403c-8df5-f6171b4f14aa · outbound

This paper cites Kankanhalli.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Kankanhalli

Reference 30

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Observation bf4c79f0-08e5-4ae4-9fb8-731fff0b5e2b · outbound

This paper cites Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent

Reference 31

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Observation e6dbb72a-e8be-4707-98fa-1a65ca8b36d9 · outbound

This paper cites Visual spatial reasoning.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Visual spatial reasoning

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:50.268677Z

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Observation 150b32c7-24de-4cd7-a868-11d75292721e · outbound

This paper cites Visual instruction tuning.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Visual instruction tuning

Reference 33

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Observation c24c4f2e-b304-45f7-a192-054771395861 · outbound

This paper cites Improved baselines with visual instruction tuning.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Improved baselines with visual instruction tuning

Reference 34

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Observation da4acb6d-e861-4fe5-bc60-f8c68030414d · outbound

This paper cites Llava-plus: Learning to use tools for creating multi- modal agents.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Llava-plus: Learning to use tools for creating multi- modal agents

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:50.028090Z

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Observation 3c4785de-f488-46e6-894f-42253a8b2587 · outbound

This paper cites Grounding dino: Marrying dino with grounded pre-training for open-set object detection.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Grounding dino: Marrying dino with grounded pre-training for open-set object detection

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:49.759709Z

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Observation 17eb8dc7-fd91-4c74-a714-e93a0aae3cc0 · outbound

This paper cites On the hidden mystery of ocr in large multimodal models.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning On the hidden mystery of ocr in large multimodal models

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:49.507460Z

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Observation e8520f35-a2c8-4be5-be7d-da8fdc5baeea · outbound

This paper cites Inter-gps: Interpretable geometry problem solving with formal language and sym- bolic reasoning.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Inter-gps: Interpretable geometry problem solving with formal language and sym- bolic reasoning

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:49.280302Z

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Observation eec69d49-8b0a-48c7-834b-7b86943d7753 · outbound

This paper cites Iconqa: A new benchmark for abstract diagram understand- ing and visual language reasoning.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Iconqa: A new benchmark for abstract diagram understand- ing and visual language reasoning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:48.930150Z

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Observation ce7ff31f-0d94-4bb9-8493-408472c1e8f2 · outbound

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

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Learn to explain: Multimodal reasoning via thought chains for science question answering

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:48.664559Z

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Observation 319aec87-0af3-4f86-bfe8-be9ab2818488 · outbound

This paper cites Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning

Reference 41

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Observation 2ab389bd-043c-404e-8d5f-fce2f8f5d213 · outbound

This paper cites Mathvista: Evaluating mathe- matical reasoning of foundation models in visual contexts.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Mathvista: Evaluating mathe- matical reasoning of foundation models in visual contexts

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:48.351154Z

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Observation 0780e952-e4e3-4756-b3fb-50787697b212 · outbound

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

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning ChartQA: A benchmark for question answer- ing about charts with visual and logical reasoning

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:48.243615Z

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Observation 6f07a7d3-977d-4b42-a5e1-42ec6e1e802e · outbound

This paper cites Docvqa: A dataset for vqa on document images.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Docvqa: A dataset for vqa on document images

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:48.168702Z

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Observation 1c296ffe-614f-4b16-b661-1b1a212f80d4 · outbound

This paper cites Infographicvqa.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Infographicvqa

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:48.004564Z

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Observation 6ae9bfaa-2a9c-4804-aa2a-dc2b857795ce · outbound

This paper cites Llama 3.2: Revolutionizing edge ai and vision with open, customizable models.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Llama 3.2: Revolutionizing edge ai and vision with open, customizable models

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:47.920183Z

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Observation 71d02cb0-f2c3-4195-be9c-e0e90823e35f · outbound

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

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Compositional chain-of-thought prompting for large multimodal models

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:47.797006Z

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Observation 89d54ed3-7d75-46e4-9c19-469c7336160e · outbound

This paper cites Compositional semantic parsing on semi-structured tables.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Compositional semantic parsing on semi-structured tables

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:47.676234Z

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Observation 53b939d8-6681-442e-976a-2f9777000580 · outbound

This paper cites Flickr30k entities: Collecting region-to-phrase corre- spondences for richer image-to-sentence models.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Flickr30k entities: Collecting region-to-phrase corre- spondences for richer image-to-sentence models

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:47.533383Z

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Observation 67ba3997-6b2a-4bf6-96b9-68bb29ab71f5 · outbound

This paper cites A benchmark of facial recognition pipelines and co-usability performances of mod- ules.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning A benchmark of facial recognition pipelines and co-usability performances of mod- ules

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:47.367570Z

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Observation 65e6ac54-3195-45f1-bb0a-adf7d0b85a17 · outbound

This paper cites Putting gpt-4o to the sword: A comprehensive evaluation of language, vision, speech, and multimodal proficiency.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Putting gpt-4o to the sword: A comprehensive evaluation of language, vision, speech, and multimodal proficiency

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:47.277223Z

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Observation 1494792a-ecc3-4427-8057-b946dd8698eb · outbound

This paper cites Visual cot: Advancing multi-modal language models with a comprehen- sive dataset and benchmark for chain-of-thought reasoning.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Visual cot: Advancing multi-modal language models with a comprehen- sive dataset and benchmark for chain-of-thought reasoning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:47.182352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation be5ce8d3-57c9-458f-9ab8-cb9c930acd9a · outbound

This paper cites Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T12:18:38.079081Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7963086-d156-4656-8cc0-45f01486735e · outbound

This paper cites Textcaps: a dataset for image caption- ing with reading comprehension.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Textcaps: a dataset for image caption- ing with reading comprehension

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T12:18:38.200474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 031f965e-c81e-47ce-990d-88f3dc516a55 · outbound

This paper cites Towards vqa models that can read.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Towards vqa models that can read

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:46.982901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 957c6707-7fcb-4195-84a1-e72f0b84820a · outbound

This paper cites Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T12:18:38.403343Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc460c53-afcf-45cc-bb4e-231079fc7c88 · outbound

This paper cites Tang, Angie Boggust, and Arvind Satyanarayan.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Tang, Angie Boggust, and Arvind Satyanarayan

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:46.756525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e04a0778-012f-48fb-91de-e1f8e17e9509 · outbound

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

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Gemini: A Family of Highly Capable Multimodal Models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T12:18:38.622584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 771ed215-2e01-4c95-beb8-e346183c90ee · outbound

This paper cites Document understanding dataset and evaluation (dude).

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Document understanding dataset and evaluation (dude)

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:46.522644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8a635456-6fae-42b1-b5e6-1c6d2232609a · outbound

This paper cites The caltech-ucsd birds-200-2011 dataset.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning The caltech-ucsd birds-200-2011 dataset

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:46.288111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 17cea784-ef6c-4838-ba07-805935d5b36f · outbound

This paper cites Screen2words: Automatic mobile ui summarization with multimodal learning.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Screen2words: Automatic mobile ui summarization with multimodal learning

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:46.064600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3d2fe1c4-0a8c-4ee2-bfd4-ebe8f53cfd67 · outbound

This paper cites LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error

Reference 62

Resolution: verified exact; local_arxiv, observed 2026-08-06T12:18:41.874604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7509c407-ae30-4486-8ab7-12cd21de8e94 · outbound

This paper cites MLLM-Tool: A Multimodal Large Language Model For Tool Agent Learning.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning MLLM-Tool: A Multimodal Large Language Model For Tool Agent Learning

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T12:18:39.118189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29515936-2adf-4b96-8dcd-d245ff7c8558 · outbound

This paper cites Mea- suring multimodal mathematical reasoning with math-vision dataset.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Mea- suring multimodal mathematical reasoning with math-vision dataset

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:45.869578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5e426e2b-2a9f-4ee5-b01b-71570168c1a5 · outbound

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

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-06T12:18:39.306583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2630173-c5e9-4b66-b4b2-b531121a1309 · outbound

This paper cites Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-06T12:18:39.399123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4197aa3f-1e21-4c07-b7c3-0bd0709b8881 · outbound

This paper cites Grok-1.5 vision preview.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Grok-1.5 vision preview

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:45.603526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 744db546-4e65-46b4-96d3-0275004cc8e8 · outbound

This paper cites LLaVA-CoT: Let Vision Language Models Reason Step-by-Step.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning LLaVA-CoT: Let Vision Language Models Reason Step-by-Step

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-06T12:18:39.605249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6af0f074-14a1-49a9-a077-7722867c6ca3 · outbound

This paper cites Llava-cot: Let vision language models reason step- by-step, 2024.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Llava-cot: Let vision language models reason step- by-step, 2024

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:45.387347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6f935308-2bd6-42db-a8bb-dbd13a588ae8 · outbound

This paper cites Gpt4tools: Teaching large language model to use tools via self-instruction.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Gpt4tools: Teaching large language model to use tools via self-instruction

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:45.106833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fc6a8b88-5b9e-4155-82de-657a638a8d80 · outbound

This paper cites React: Synergizing rea- soning and acting in language models.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning React: Synergizing rea- soning and acting in language models

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:44.865434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6aed4166-fd33-4939-ae15-4e8f720b5c6a · outbound

This paper cites MiniCPM-V: A GPT-4V Level MLLM on Your Phone.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning MiniCPM-V: A GPT-4V Level MLLM on Your Phone

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-06T12:18:40.013192Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bed3ff95-f0d1-42b0-b8b5-0604add61aee · outbound

This paper cites Agent lumos: Unified and modular training for open-source language agents.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Agent lumos: Unified and modular training for open-source language agents

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:44.640480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 09ac949a-d7ff-4981-a42b-e5acb84a3f31 · outbound

This paper cites Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for ex- pert agi.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for ex- pert agi

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:44.437025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 565e3f9f-ecd4-4be0-a9e7-b16f64f828c3 · outbound

This paper cites AgentTuning: Enabling Generalized Agent Abilities for LLMs.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning AgentTuning: Enabling Generalized Agent Abilities for LLMs

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-06T12:18:40.313560Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0967ca33-3478-437a-911a-75eb29b5b5d5 · outbound

This paper cites Raven: A dataset for relational and analogical visual reasoning.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Raven: A dataset for relational and analogical visual reasoning

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:44.189547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0cc7d3f4-c98e-406c-8321-11f839aad7c7 · outbound

This paper cites Swift:a scal- able lightweight infrastructure for fine-tuning, 2024.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Swift:a scal- able lightweight infrastructure for fine-tuning, 2024

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:43.965856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4411d2fb-c374-493c-8caa-d4770086b41b · outbound

This paper cites Seq2sql: Generating structured queries from natural language using reinforcement learning, 2017.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Seq2sql: Generating structured queries from natural language using reinforcement learning, 2017

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:43.750174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation de5ef537-70bf-40f3-9766-c216ab3a5fd5 · outbound

This paper cites Visual7w: Grounded question answering in images.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Visual7w: Grounded question answering in images

Reference 79

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T12:18:43.524971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5256053a-33dd-46de-8251-152ba8f8af31 · outbound

This paper cites an unresolved cited work.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Unresolved cited work

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-06T12:18:40.808887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c81563ba-84aa-460c-a47c-c3f38c58dd28 · outbound

This paper cites an unresolved cited work.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Unresolved cited work

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-06T12:18:40.922255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c1b6981-1d26-4138-b7c8-99894dea8acf · outbound

This paper cites objects": [.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning objects": [

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:43.246936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 05128088-b15e-47bb-ac2d-8c23e84e2e5c · outbound

This paper cites an unresolved cited work.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Unresolved cited work

Reference 86

Resolution: unresolved; raw_fallback, observed 2026-08-06T12:18:42.999932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3f7bae2a-c3bb-48b8-98f5-9085610ac3ff · outbound

This paper cites image_caption.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning image_caption

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:42.706027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d5f08797-6684-43ed-88c0-c5610b21feb7 · outbound

This paper cites needed": true,.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning needed": true,

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:42.471282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 72e7adf1-f915-45ba-b014-9a7760bf60b4 · outbound

This paper cites continue.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning continue

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:18:42.265587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d63ce0b8-1ee8-4fc6-a71d-aea9de96110b · outbound

This paper cites an unresolved cited work.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Unresolved cited work

Reference 170

Resolution: unresolved; no resolver link, observed 2026-08-06T12:18:33.027058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf2204af-5146-43f9-9318-0ccc42de42dc · outbound

This paper cites an unresolved cited work.

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Unresolved cited work

Reference 251

Resolution: parse uncertain; raw_fallback, observed 2026-08-06T12:18:51.528283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 846a591b-e1c1-4d71-bf21-87c1e714b30f · outbound

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

MMAT-1M: A Large Reasoning Dataset for Multimodal Agent Tuning Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T12:18:31.416961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers