Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding | Paper Citation Record

Observation 44de7fcd-6490-404c-aa00-b3099dce6f5a · outbound

This paper cites Au- tomaTikZ: Text-guided synthesis of scientific vector graph- ics with TikZ.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Au- tomaTikZ: Text-guided synthesis of scientific vector graph- ics with TikZ

Reference 1

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Observation b4f7802e-dcef-4357-babd-25f8bb87b9ce · outbound

This paper cites DeTikZify: Synthesizing graphics programs for scientific figures and sketches with TikZ.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding DeTikZify: Synthesizing graphics programs for scientific figures and sketches with TikZ

Reference 2

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Observation a04f5739-81b5-4c47-a927-96adfbdcfaff · outbound

This paper cites Scene text visual question answering.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Scene text visual question answering

Reference 3

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Observation 941fc196-2803-45a1-9ab8-cae16487696a · outbound

This paper cites Nougat: Neural Optical Understanding for Academic Documents.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Nougat: Neural Optical Understanding for Academic Documents

Reference 4

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Observation c90c241d-7996-4298-acb4-4583b69951b8 · outbound

This paper cites Onechart: Purify the chart structural extrac- tion via one auxiliary token.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Onechart: Purify the chart structural extrac- tion via one auxiliary token

Reference 5

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Observation 2c1ff4e6-b7b4-4ae7-8400-3044113cfc7a · outbound

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

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding ShareGPT4V: Improving Large Multi-Modal Models with Better Captions

Reference 6

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Observation f00b2977-ba1c-4df6-a042-a5141caa90f3 · outbound

This paper cites WebSRC: A Dataset for Web-Based Structural Reading Comprehension.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding WebSRC: A Dataset for Web-Based Structural Reading Comprehension

Reference 7

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Observation 6e24ccaf-f037-40a1-a15e-e4c4903d3014 · outbound

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

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites

Reference 8

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Observation 84a9a66b-0bb7-4def-9a8d-d123e137dccf · outbound

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

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks

Reference 9

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Observation 3a87248b-98d1-4384-9a5d-f1bfb82ca3bc · outbound

This paper cites Complicated Table Structure Recognition.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Complicated Table Structure Recognition

Reference 10

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Observation 93c99fa7-5177-4118-a41f-7018fc45724c · outbound

This paper cites DocPedia: Unleashing the Power of Large Multimodal Model in the Frequency Domain for Versatile Document Understanding.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding DocPedia: Unleashing the Power of Large Multimodal Model in the Frequency Domain for Versatile Document Understanding

Reference 11

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Observation 9ccfac39-359c-43f5-ac9d-c6d97d994414 · outbound

This paper cites G-llava: Solving geomet- ric problem with multi-modal large language model, 2023.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding G-llava: Solving geomet- ric problem with multi-modal large language model, 2023

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:54.247784Z

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Observation acf0312e-3aea-4063-a99e-eb6897865239 · outbound

This paper cites G-LLaVA: Solving Geometric Problem with Multi-Modal Large Language Model.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding G-LLaVA: Solving Geometric Problem with Multi-Modal Large Language Model

Reference 13

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Observation 9ae419b8-c3a0-4a49-bc84-717668730182 · outbound

This paper cites MathWriting: A Dataset For Handwritten Mathematical Expression Recognition.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding MathWriting: A Dataset For Handwritten Mathematical Expression Recognition

Reference 14

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Observation 9469f8fe-955f-4044-a005-7086ea73b5cb · outbound

This paper cites ImageBind-LLM: Multi-modality Instruction Tuning.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding ImageBind-LLM: Multi-modality Instruction Tuning

Reference 15

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Observation 0882036b-164f-4497-b06a-85f854dc7042 · outbound

This paper cites Cogagent: A visual language model for gui agents.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Cogagent: A visual language model for gui agents

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:54.233788Z

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Observation 423749f3-1393-4285-a447-57066666117c · outbound

This paper cites mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understanding.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understanding

Reference 17

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Observation 35a35a60-4480-43da-9239-1340ba8b725d · outbound

This paper cites Icdar 2019 robust reading challenge on scanned receipts ocr and information extraction.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Icdar 2019 robust reading challenge on scanned receipts ocr and information extraction

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:54.218188Z

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Observation 537c7fa5-3bf6-4da7-9eeb-275ef31a95b0 · outbound

This paper cites Funsd: A dataset for form understanding in noisy scanned documents.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Funsd: A dataset for form understanding in noisy scanned documents

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:54.178053Z

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Observation 8fca41c6-d9be-48c6-b7b3-ed7f004ba0ca · outbound

This paper cites Revisiting scene text recognition: A data per- spective.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Revisiting scene text recognition: A data per- spective

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:54.131556Z

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Observation dbcb6e44-73f9-4d68-9a63-2ceebab4812a · outbound

This paper cites Dvqa: Understanding data visualizations via ques- tion answering.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Dvqa: Understanding data visualizations via ques- tion answering

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:54.118741Z

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Observation 7adfcb41-ad6f-4f8b-9ac8-af84783e55d8 · outbound

This paper cites Icdar 2013 robust read- ing competition.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Icdar 2013 robust read- ing competition

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:54.105042Z

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Observation c963a7f8-e702-4c4c-a539-633f4b09a945 · outbound

This paper cites Icdar 2015 competition on robust reading.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Icdar 2015 competition on robust reading

Reference 23

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Observation 0aa04442-fd9a-4ecb-93be-248b41e0fd04 · outbound

This paper cites A diagram is worth a dozen images.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding A diagram is worth a dozen images

Reference 24

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Observation ef09898d-750d-46ca-9d18-02d7410f8be8 · outbound

This paper cites Openassistant conversations-democratizing large lan- guage model alignment.Advances in Neural Information Processing Systems, 36, 2024.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Openassistant conversations-democratizing large lan- guage model alignment.Advances in Neural Information Processing Systems, 36, 2024

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:54.039697Z

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Observation 0b92f8a9-5590-471d-b182-e0510bdb4375 · outbound

This paper cites Visual information extraction in the wild: practical dataset and end-to-end solu- tion.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Visual information extraction in the wild: practical dataset and end-to-end solu- tion

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:54.026666Z

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Observation eacbb7c1-5e47-4b45-9a7b-dbc794caf5b4 · outbound

This paper cites Unlocking the conversion of Web Screenshots into HTML Code with the WebSight Dataset.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Unlocking the conversion of Web Screenshots into HTML Code with the WebSight Dataset

Reference 27

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Observation 3740c690-0340-41b4-bba2-d560e50f4f95 · outbound

This paper cites Docmatix dataset.https://huggingface.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Docmatix dataset.https://huggingface

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:53.991853Z

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Observation 0a090194-7485-4d18-93ae-fbe83f66bed8 · outbound

This paper cites When counting meets hmer: counting-aware network for handwritten math- ematical expression recognition.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding When counting meets hmer: counting-aware network for handwritten math- ematical expression recognition

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:53.958651Z

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Observation fc9495b3-c0bf-4d05-b34f-b9b57932b061 · outbound

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

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding LLaVA-OneVision: Easy Visual Task Transfer

Reference 30

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Observation 87faa7b8-5261-4eda-ad00-a04cb3e8ab59 · outbound

This paper cites BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

Reference 31

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Observation 56e408e2-84b5-4f09-bf76-53378ba7cd81 · outbound

This paper cites Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T23:08:51.057587Z

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Observation f3c3310f-88d5-47a6-accf-79aaf5186120 · outbound

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

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Mon- key: Image resolution and text label are important things for large multi-modal models

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:53.943284Z

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Observation 16cdadcf-dcaa-4ee2-8d23-365128d973fa · outbound

This paper cites SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-15T23:08:51.128928Z

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Observation 5f31cc37-4a91-4ef2-8cec-9edded724a85 · outbound

This paper cites Visual Instruction Tuning.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Visual Instruction Tuning

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-15T23:08:51.133780Z

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Observation a4f4326a-ca18-4762-b33c-7be770ea2b88 · outbound

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

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Llava-next: Im- proved reasoning, ocr, and world knowledge, 2024

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T23:08:51.186321Z

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Observation c3850ba8-bba0-4961-96f5-8c67cdf312e2 · outbound

This paper cites Visualwebbench: How far have multimodal llms evolved in web page under- standing and grounding?, 2024.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Visualwebbench: How far have multimodal llms evolved in web page under- standing and grounding?, 2024

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:53.907864Z

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Observation 22bdc781-3999-4107-9311-c625b02c7084 · outbound

This paper cites OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T23:08:51.229797Z

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Observation 3d98969a-46df-4552-8f3e-845d90a7d4ac · outbound

This paper cites TextMonkey: An OCR-Free Large Multimodal Model for Understanding Document.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding TextMonkey: An OCR-Free Large Multimodal Model for Understanding Document

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T23:08:51.234125Z

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Observation f0e294dc-0f64-4f08-8c8f-492a9028c9f1 · outbound

This paper cites Towards end-to-end unified scene text detection and layout analysis.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Towards end-to-end unified scene text detection and layout analysis

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:53.880831Z

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Observation 26c5c5f3-e426-4c74-9b09-b033ce70db73 · outbound

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

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Inter-gps: Interpretable geometry problem solving with formal language and sym- bolic reasoning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:53.851414Z

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Observation e38818c9-4342-4872-9d5d-88f6529e1f4a · outbound

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

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding ChartQA: A benchmark for question answer- ing about charts with visual and logical reasoning

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:53.818038Z

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Observation f2d84bb7-0985-4d1f-82e4-af39afe7b3db · outbound

This paper cites V Jawahar.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding V Jawahar

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:53.803621Z

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Observation fab00ed6-d167-49a8-b0b8-87ff19e275a0 · outbound

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

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Docvqa: A dataset for vqa on document images

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:53.732117Z

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Observation c58d1bd8-7718-44c6-a068-62ea7207e501 · outbound

This paper cites Plotqa: Reasoning over scientific plots.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Plotqa: Reasoning over scientific plots

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-15T23:08:51.439981Z

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Observation 5a30488f-ad31-4d6c-9b82-c12b060e3bbe · outbound

This paper cites TableFormer: Table Structure Understanding with Transformers.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding TableFormer: Table Structure Understanding with Transformers

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-15T23:08:51.444111Z

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Observation 841dcd17-c17b-4980-b4f1-ec88b48646cb · outbound

This paper cites Chatgpt.https://chat.openai.com, 2023.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Chatgpt.https://chat.openai.com, 2023

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:53.708357Z

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Observation 162f744d-64d1-433b-bedf-63d2467bddd6 · outbound

This paper cites GPT-4 Technical Report.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding GPT-4 Technical Report

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-15T23:08:51.453832Z

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Observation 5c5577d2-9d1e-4491-b448-e12047bacfd2 · outbound

This paper cites Training lan- guage models to follow instructions with human feedback.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Training lan- guage models to follow instructions with human feedback

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:53.673222Z

Source-reported events for the cited work

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Observation 6d581baf-0a6a-4422-ab2f-232eebd5f580 · outbound

This paper cites Cord: a con- solidated receipt dataset for post-ocr parsing.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Cord: a con- solidated receipt dataset for post-ocr parsing

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:53.644893Z

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Observation 9783b180-e7fe-4fc2-a1cc-867a29e12868 · outbound

This paper cites MultiMath: Bridging Visual and Mathematical Reasoning for Large Language Models.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding MultiMath: Bridging Visual and Mathematical Reasoning for Large Language Models

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-15T23:08:51.545097Z

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Observation 9d718dc0-ddd2-4652-bd37-83cac6334546 · outbound

This paper cites Language models are unsu- pervised multitask learners.OpenAI blog, 1(8):9, 2019.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Language models are unsu- pervised multitask learners.OpenAI blog, 1(8):9, 2019

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:53.611521Z

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Observation 58d14f95-aa7a-45f8-bb40-7e2f252b0480 · outbound

This paper cites Visual cot: Unleashing chain-of-thought reasoning in multi-modal language models.arXiv e-prints, pages arXiv–2403, 2024.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Visual cot: Unleashing chain-of-thought reasoning in multi-modal language models.arXiv e-prints, pages arXiv–2403, 2024

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:53.595885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2a6bd541-baf3-48fd-b6c0-6323a2ff5c15 · outbound

This paper cites Icdar2017 competition on reading chinese text in the wild (rctw-17).

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Icdar2017 competition on reading chinese text in the wild (rctw-17)

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:53.523375Z

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Observation 00ce7920-1e11-49b4-ba66-e0ea3c37dcd8 · outbound

This paper cites Towards vqa models that can read.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Towards vqa models that can read

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:53.505733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 81387647-952a-479e-867d-044615ede585 · outbound

This paper cites Textocr: Towards large-scale end-to-end reasoning for arbitrary-shaped scene text.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Textocr: Towards large-scale end-to-end reasoning for arbitrary-shaped scene text

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:53.473925Z

Source-reported events for the cited work

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Observation f64efca0-3d6f-4ff7-aca3-99c6fa340b25 · outbound

This paper cites Spatial Dual-Modality Graph Reasoning for Key Information Extraction.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Spatial Dual-Modality Graph Reasoning for Key Information Extraction

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-15T23:08:51.809034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ee44980-8101-4545-9080-8d4175fe7c0f · outbound

This paper cites Icdar 2019 competition on large-scale street view text with partial labeling-rrc-lsvt.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Icdar 2019 competition on large-scale street view text with partial labeling-rrc-lsvt

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:53.459557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 35ffffdd-eff8-4d0f-8ba9-996962eda127 · outbound

This paper cites Internlm: A multilingual language model with progressively enhanced capabilities, 2023.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Internlm: A multilingual language model with progressively enhanced capabilities, 2023

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-15T23:08:51.818507Z

Source-reported events for the cited work

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Observation cac7b022-3036-4974-babc-824a47373cf1 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-15T23:08:51.878962Z

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Observation c2a191ff-1344-40a5-a6b8-ca3f7b1a630a · outbound

This paper cites COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-15T23:08:51.969524Z

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Observation 2f7fb109-156f-4f24-b39a-101539bb3ab4 · outbound

This paper cites Measuring multimodal mathemat- ical reasoning with math-vision dataset, 2024.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Measuring multimodal mathemat- ical reasoning with math-vision dataset, 2024

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:53.356298Z

Source-reported events for the cited work

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Observation 7a1efaed-b0d0-4f4d-b5ad-a3abd983b7ea · outbound

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

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-15T23:08:51.979528Z

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Observation 2c687a36-62e2-4d01-a1bd-69b66d8fe781 · outbound

This paper cites On the general value of ev- idence, and bilingual scene-text visual question answering.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding On the general value of ev- idence, and bilingual scene-text visual question answering

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:53.343783Z

Source-reported events for the cited work

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Observation 5dcf2f51-9e6b-4896-8c6b-a2070646d065 · outbound

This paper cites Vary: Scaling up the vision vocabulary for large vision-language model.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Vary: Scaling up the vision vocabulary for large vision-language model

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:53.276269Z

Source-reported events for the cited work

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Observation cf6d6f5b-fe80-4f4f-a99c-2df0ec142db4 · outbound

This paper cites Toward understanding wordart: Corner-guided transformer for scene text recognition.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Toward understanding wordart: Corner-guided transformer for scene text recognition

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:53.259690Z

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Observation ab7e3ebd-126c-4a2c-be6e-4037f1143f18 · outbound

This paper cites Xfund: a benchmark dataset for multilingual visually rich form under- standing.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Xfund: a benchmark dataset for multilingual visually rich form under- standing

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:53.243919Z

Source-reported events for the cited work

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Observation 1e2dd512-7d28-422b-9972-c10ec4d8d51b · outbound

This paper cites Tgrnet: A table graph reconstruction net- work for table structure recognition.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Tgrnet: A table graph reconstruction net- work for table structure recognition

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:53.141050Z

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Observation 1ef12314-b89f-4532-8fcf-fbde5f03a3d6 · outbound

This paper cites A large-scale dataset for end-to-end table recognition in the wild.Scientific Data, 10(1):110, 2023.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding A large-scale dataset for end-to-end table recognition in the wild.Scientific Data, 10(1):110, 2023

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:53.127344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 95a8e5eb-970f-4e92-ad66-8d2e24d4f79a · outbound

This paper cites UReader: Universal OCR-free Visually-situated Language Understanding with Multimodal Large Language Model.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding UReader: Universal OCR-free Visually-situated Language Understanding with Multimodal Large Language Model

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-15T23:08:52.092596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 102e7b8a-ffbc-4d6e-be24-f0867ae3c2c6 · outbound

This paper cites Icdar 2023 competition on structured text extraction from visually-rich document im- ages.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Icdar 2023 competition on structured text extraction from visually-rich document im- ages

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:52.987500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 555b5dd8-002c-4868-ac4b-d776363d8406 · outbound

This paper cites Syntax-aware network for handwritten mathematical expression recognition.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Syntax-aware network for handwritten mathematical expression recognition

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:52.974402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 11aefa47-b1fa-445e-bf97-aa85ada1a6b7 · outbound

This paper cites Detecting Curve Text in the Wild: New Dataset and New Solution.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Detecting Curve Text in the Wild: New Dataset and New Solution

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-15T23:08:52.197635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97b5a255-71c8-4c62-9a18-46fd0f6bdf04 · outbound

This paper cites LLaVA-Read: Enhancing Reading Ability of Multimodal Language Models.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding LLaVA-Read: Enhancing Reading Ability of Multimodal Language Models

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-15T23:08:52.203082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f52d260-71b8-4827-8cbb-3806f05b94e7 · outbound

This paper cites Image-based table recognition: data, model, and evaluation.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Image-based table recognition: data, model, and evaluation

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-15T23:08:52.207933Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5ee0e39-02ff-4ed2-b880-fa60c8a1a524 · outbound

This paper cites Lima: Less is more for alignment.Advances in Neural Information Processing Systems, 36, 2024.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding Lima: Less is more for alignment.Advances in Neural Information Processing Systems, 36, 2024

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:52.881171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation dae84381-e88b-4ee2-bb1a-8dd3f0c4e0ba · outbound

This paper cites - Answer: The known answer to the question.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding - Answer: The known answer to the question

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:52.866566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 50b3169c-79a7-4055-905b-18333b55a3e7 · outbound

This paper cites - `<txt_gd></txt_gd>`: Text with coordinates for context.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding - `<txt_gd></txt_gd>`: Text with coordinates for context

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:52.779914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation eaf472a1-940f-4aa9-8f29-aac0b996b54e · outbound

This paper cites - Ensure that the extracted content retains its original formatting.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding - Ensure that the extracted content retains its original formatting

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:08:52.766246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 481d3b8f-4f52-41e7-860a-0a68407ee8f3 · outbound

This paper cites title":.

Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding title":

Reference 80

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T23:08:52.752277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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