Paper Citation Record · LEDGER

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning

As of 10 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 16 inbound Pith citation observations for arXiv:2505.22019.

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

pith.paper-citation-record.v1
2505.22019 v2

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:24:54.668238Z

measured 70 of 70 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T13:23:34.984840Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T15:09:55.135392Z

Reference resolution

54 of 54 outbound references displayed

verified exact0
verified fuzzy8
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 69531200-78c5-409b-9075-705dee559482 · outbound

This paper cites write newline.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning write newline

Reference 1

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Observation e8ccdf16-eff3-4b06-8bc0-390b31df102e · outbound

This paper cites Qwen2.5-VL Technical Report.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Qwen2.5-VL Technical Report

Reference 2

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Observation 268315af-74ce-4a4d-8877-217f37f1bfc5 · outbound

This paper cites Benchmarking large language models in retrieval-augmented generation.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Benchmarking large language models in retrieval-augmented generation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:56.946878Z

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Observation 5baac46d-a7d7-4111-b649-57ccaf6cddc8 · outbound

This paper cites R1-v: Reinforcing super generalization ability in vision-language models with less than \ 3.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning R1-v: Reinforcing super generalization ability in vision-language models with less than \ 3

Reference 4

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Observation cb69defb-b57e-41c1-bb83-ccf06e98986f · outbound

This paper cites ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning

Reference 5

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Observation 0af78db5-411e-4aa0-afe7-7ceefecb0d9d · outbound

This paper cites Mindsearch: Mimicking human minds elicits deep ai searcher.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Mindsearch: Mimicking human minds elicits deep ai searcher

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:49.352425Z

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Observation 762efeb2-6d0c-4bec-97a0-d036d833bbeb · outbound

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

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models

Reference 7

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Observation e9790918-9649-4cc5-ab71-331c48752194 · outbound

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

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

Reference 8

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Observation d32f2860-0616-4982-b3a8-d69e8cd7924b · outbound

This paper cites M3DocRAG: Multi-modal Retrieval is What You Need for Multi-page Multi-document Understanding.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning M3DocRAG: Multi-modal Retrieval is What You Need for Multi-page Multi-document Understanding

Reference 9

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Observation b8925cca-2976-4e05-b1cd-19f0c477861c · outbound

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

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning PP-OCR: A Practical Ultra Lightweight OCR System

Reference 10

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Observation acb7fd87-8eb6-47a1-bd8b-4ed82d8b5d9e · outbound

This paper cites Colpali: Efficient document retrieval with vision language models.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Colpali: Efficient document retrieval with vision language models

Reference 11

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Observation b48724e1-3d34-4973-8f85-220a4d7e13b9 · outbound

This paper cites Retrieval-Augmented Generation for Large Language Models: A Survey.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 12

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Observation 07fc34a4-9d1a-4da1-a76d-6a940277e99b · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 13

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Observation 7ff22933-ee18-42ad-9a6e-3307aa72aa7c · outbound

This paper cites Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models

Reference 14

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Observation 00eeeb99-9d5a-4aeb-9c23-ed590f381a76 · outbound

This paper cites OpenAI o1 System Card.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning OpenAI o1 System Card

Reference 15

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Observation 00e16907-51c7-4867-a3b2-3f372a8b503a · outbound

This paper cites MMSearch: Benchmarking the Potential of Large Models as Multi-modal Search Engines.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning MMSearch: Benchmarking the Potential of Large Models as Multi-modal Search Engines

Reference 16

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Observation f7548be0-6977-4d9b-8ed3-e540525ea30f · outbound

This paper cites DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:50.943846Z

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Observation 0914aadd-44d0-486a-a3d4-d761414d506c · outbound

This paper cites Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG

Reference 18

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Observation 9ead5704-8345-4c87-852f-5f9e97d29a78 · outbound

This paper cites Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

Reference 19

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Observation 8e205582-82cd-4269-8724-f25b68dd7ad2 · outbound

This paper cites Reinforcement learning: A survey.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Reinforcement learning: A survey

Reference 20

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Observation 6d2d3ac9-3765-48a5-8255-04acfd34cbbf · outbound

This paper cites NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models

Reference 21

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Observation c2d12a3a-869d-4955-84ef-bb3ddd92a9cb · outbound

This paper cites u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \

Reference 22

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VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Search-o1: Agentic Search-Enhanced Large Reasoning Models

Reference 23

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Observation 11459a76-2481-48f2-b128-0ef82e496bae · outbound

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

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent

Reference 24

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Observation 8b9ceca8-29b6-4506-b59b-b18646e7e1e1 · outbound

This paper cites Towards General Text Embeddings with Multi-stage Contrastive Learning.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Towards General Text Embeddings with Multi-stage Contrastive Learning

Reference 25

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Observation e8acbfd5-bd5a-4648-b7b9-5e14039ded2f · outbound

This paper cites Improved baselines with visual instruction tuning.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Improved baselines with visual instruction tuning

Reference 26

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This paper cites LlamaIndex , 11 2022.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning LlamaIndex , 11 2022

Reference 27

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Observation 02dc48b8-0155-4e5a-a008-61cb42d6d12e · outbound

This paper cites Visual-RFT: Visual Reinforcement Fine-Tuning.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Visual-RFT: Visual Reinforcement Fine-Tuning

Reference 28

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VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning MMLongBench-Doc: Benchmarking Long-context Document Understanding with Visualizations

Reference 29

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VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning MM-Eureka: Exploring the Frontiers of Multimodal Reasoning with Rule-based Reinforcement Learning

Reference 30

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This paper cites Simpo: Simple preference optimization with a reference-free reward.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Simpo: Simple preference optimization with a reference-free reward

Reference 31

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This paper cites NV-Retriever: Improving text embedding models with effective hard-negative mining.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning NV-Retriever: Improving text embedding models with effective hard-negative mining

Reference 32

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VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Hello gpt-4o

Reference 33

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This paper cites Introducing gemini 2.0: our new ai model for the agentic era, 2024.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Introducing gemini 2.0: our new ai model for the agentic era, 2024

Reference 34

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This paper cites Direct preference optimization: Your language model is secretly a reward model.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Direct preference optimization: Your language model is secretly a reward model

Reference 35

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This paper cites Proximal Policy Optimization Algorithms.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 36

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

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Visual cot: Advancing multi-modal language models with a comprehensive dataset and benchmark for chain-of-thought reasoning

Reference 37

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Observation d74a9cfd-9ee9-48f7-b84d-946c85862326 · outbound

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VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning HybridFlow: A Flexible and Efficient RLHF Framework

Reference 38

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VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Reinforcement learning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:55.809640Z

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Observation ebfbe6e8-0a78-475e-9803-6fd86710633c · outbound

This paper cites Slidevqa: A dataset for document visual question answering on multiple images.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Slidevqa: A dataset for document visual question answering on multiple images

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:55.604920Z

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This paper cites ViDoRAG: Visual Document Retrieval-Augmented Generation via Dynamic Iterative Reasoning Agents.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning ViDoRAG: Visual Document Retrieval-Augmented Generation via Dynamic Iterative Reasoning Agents

Reference 41

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This paper cites RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning

Reference 42

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VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Simple statistical gradient-following algorithms for connectionist reinforcement learning

Reference 43

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This paper cites WebWalker: Benchmarking LLMs in Web Traversal.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning WebWalker: Benchmarking LLMs in Web Traversal

Reference 44

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This paper cites Unfolding the Headline: Iterative Self-Questioning for News Retrieval and Timeline Summarization.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Unfolding the Headline: Iterative Self-Questioning for News Retrieval and Timeline Summarization

Reference 45

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This paper cites Rule: Reliable multimodal rag for factuality in medical vision language models.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Rule: Reliable multimodal rag for factuality in medical vision language models

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:55.380239Z

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Observation 3ec533b4-47bb-425c-bc96-4f0808b4965f · outbound

This paper cites Qwen2.5 Technical Report.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Qwen2.5 Technical Report

Reference 47

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VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning React: Synergizing reasoning and acting in language models

Reference 48

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VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Perception-R1: Pioneering Perception Policy with Reinforcement Learning

Reference 49

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Observation 59e799c9-745e-4f80-8775-79913f385c70 · outbound

This paper cites Introducing Visual Perception Token into Multimodal Large Language Model.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning Introducing Visual Perception Token into Multimodal Large Language Model

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:54.345474Z

Source-reported events for the cited work

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Observation bdcf5e24-caf3-41f1-8888-177af150b37d · outbound

This paper cites VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:54.427481Z

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Observation 23622d0a-3561-49b8-9458-c3817c6b7f97 · outbound

This paper cites LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:54.491170Z

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Observation 7d0bfe84-fb56-4288-a145-74e854cc139d · outbound

This paper cites , " * write output.state after.block = add.period write.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning , " * write output.state after.block = add.period write

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:54.566124Z

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Unavailable: canonical work link unavailable.

Observation 2d2679c0-1349-4812-9f51-95df8bf0e25d · outbound

This paper cites write newline.

VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning write newline

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:54.668238Z

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Pith citing papers

Observation e4dc91ff-51d2-4752-aa5a-47c8c4b80e89 · inbound

Learning Only with Images: Visual Reinforcement Learning with Reasoning, Rendering, and Visual Feedback cites this paper.

Learning Only with Images: Visual Reinforcement Learning with Reasoning, Rendering, and Visual Feedback VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T13:23:34.984840Z

Source-reported events for the cited work

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Observation bf6bdab8-5b24-4a69-9e98-363b25af0fbb · inbound

M2IO-R1: An Efficient RL-Enhanced Reasoning Framework for Multimodal Retrieval Augmented Multimodal Generation cites this paper.

M2IO-R1: An Efficient RL-Enhanced Reasoning Framework for Multimodal Retrieval Augmented Multimodal Generation VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-05T22:52:00.547279Z

Source-reported events for the cited work

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Observation 1a8a7c1b-2d4e-46f7-b0c4-35065cf68ee2 · inbound

DeepEyesV2: Toward Agentic Multimodal Model cites this paper.

DeepEyesV2: Toward Agentic Multimodal Model VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-16T05:32:29.448501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2967d069-f60e-4f57-8581-64b9eed36b05 · inbound

GeoBrowse: A Geolocation Benchmark for Agentic Tool Use with Expert-Annotated Reasoning Traces cites this paper.

GeoBrowse: A Geolocation Benchmark for Agentic Tool Use with Expert-Annotated Reasoning Traces VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning

Reference 47

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T17:33:02.362467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 97ad0c2a-e9dc-4d15-8928-993a8a37b856 · inbound

ReAlign: Optimizing the Visual Document Retriever with Reasoning-Guided Fine-Grained Alignment cites this paper.

ReAlign: Optimizing the Visual Document Retriever with Reasoning-Guided Fine-Grained Alignment VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning

Reference 85

Resolution: verified exact; arxiv_id, observed 2026-05-11T06:15:58.159597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bec402f7-7a52-459e-b962-8d50ea8d82cf · inbound

VISOR: Agentic Visual Retrieval-Augmented Generation via Iterative Search and Over-horizon Reasoning cites this paper.

VISOR: Agentic Visual Retrieval-Augmented Generation via Iterative Search and Over-horizon Reasoning VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-11T06:11:03.331597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 36ea29d2-7175-46ed-95d2-4625e83a559c · inbound

POINTS-Seeker: An Open Recipe for Multimodal Search Agents with Visual Memory Management cites this paper.

POINTS-Seeker: An Open Recipe for Multimodal Search Agents with Visual Memory Management VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning

Reference 45

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T13:10:26.498983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ca24bd0d-f4ba-4938-9eea-6f75e42bbbf8 · inbound

POINTS-Seeker: An Open Recipe for Multimodal Search Agents with Visual Memory Management cites this paper.

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-02T16:18:24.497209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37e43759-ef2d-4700-bc61-27bd5aa2e355 · inbound

CC-OCR V2: Benchmarking Large Multimodal Models for Literacy in Real-world Document Processing cites this paper.

CC-OCR V2: Benchmarking Large Multimodal Models for Literacy in Real-world Document Processing VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning

Reference 62

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T23:46:49.213351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ce49c680-a71e-42ed-919f-fd2c54d9a638 · inbound

SMMBench: A Benchmark for Source-Distributed Multimodal Agent Memory cites this paper.

SMMBench: A Benchmark for Source-Distributed Multimodal Agent Memory VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-20T19:13:40.493453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 39c126c9-dc8c-43cd-8c4d-2adf8647af8b · inbound

From Structure to Synergy: A Survey of Vision-Language Perception Paradigm Evolution in Multimodal Large Language Models cites this paper.

From Structure to Synergy: A Survey of Vision-Language Perception Paradigm Evolution in Multimodal Large Language Models VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning

Reference 215

Resolution: verified exact; arxiv_id, observed 2026-07-04T15:09:55.136829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6ad6b6c6-2c8d-42bc-8c53-fde35b53ed31 · inbound

SimpleSearch-VL: A Simple Recipe for Multimodal Agentic Deep Search cites this paper.

SimpleSearch-VL: A Simple Recipe for Multimodal Agentic Deep Search VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T09:55:41.069237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c51fda0a-6a8b-47ae-ae27-03d8243df2b8 · inbound

BVS: Bayesian Visual Search with Multimodal Large Language Model for Fine-grained Perception cites this paper.

BVS: Bayesian Visual Search with Multimodal Large Language Model for Fine-grained Perception VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning

Reference 163

Resolution: unresolved; no resolver link, observed 2026-07-12T04:17:40.198357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb841e23-0e1c-448e-9519-cdee7432d478 · inbound

Enhancing Large Multimodal Models in Key Information Extraction via Scene-Aware Document Synthesis cites this paper.

Enhancing Large Multimodal Models in Key Information Extraction via Scene-Aware Document Synthesis VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-11T16:02:00.920066Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b77f11b1-eb43-4aaa-a7ba-0da6aa9f7156 · inbound

HierDoc: Hierarchical Page-to-Region Evidence Routing for Long-Document Visual Question Answering cites this paper.

HierDoc: Hierarchical Page-to-Region Evidence Routing for Long-Document Visual Question Answering VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-03T02:57:49.554753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07c32dab-92fe-471c-a56d-9a84f9aad946 · inbound

Video-DeepResearch: Towards the Next-Generation Multimodal Deepresearch Agent cites this paper.

Video-DeepResearch: Towards the Next-Generation Multimodal Deepresearch Agent VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T04:44:27.203082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.