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Paper Citation Record · LEDGER

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation

As of 9 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 3 inbound Pith citation observations for arXiv:2510.09733.

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

pith.paper-citation-record.v1
2510.09733 v2

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T10:40:45.271374Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T13:26:30.298761Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T06:11:03.314967Z

Reference resolution

41 of 41 outbound references displayed

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  • malformed identifier0
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Outbound references

Observation 0f676318-d5c5-4301-b671-27ab49a04525 · outbound

This paper cites OpenVLThinker: Complex Vision-Language Reasoning via Iterative SFT-RL Cycles.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation OpenVLThinker: Complex Vision-Language Reasoning via Iterative SFT-RL Cycles

Reference 3

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Observation 13273786-95f4-447f-b371-5cd1191bf618 · outbound

This paper cites ColPali: Efficient Document Retrieval with Vision Language Models.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation ColPali: Efficient Document Retrieval with Vision Language Models

Reference 4

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Observation 18f4960a-efbd-4a31-8668-efc10eade051 · outbound

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

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 5

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Observation db067711-b5e6-41fa-8bca-fe48a7ebde2d · outbound

This paper cites Sufficient context: A new lens on retrieval augmented generation systems.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation Sufficient context: A new lens on retrieval augmented generation systems

Reference 6

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source=pdf_text observed=2026-08-04T10:40:40.397585Z digest=sha256:142fc8377a24359c569f8c88367858368258bc5dfeeb8a726148ab1e325dfd31

Observation 6b112fef-c78b-471e-a5ac-e9abc202bd2f · outbound

This paper cites Retrieval-augmented gener- ation for knowledge-intensive NLP tasks.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation Retrieval-augmented gener- ation for knowledge-intensive NLP tasks

Reference 7

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source=pdf_text observed=2026-08-04T10:40:40.511860Z digest=sha256:f35d2668184de9aa106d0c1f8a079b65312382723b8178e9bb07d51839b3ba11

Observation dc1e5cb2-075b-41e1-b5c1-d43d63842083 · outbound

This paper cites More Thinking, Less Seeing? Assessing Amplified Hallucination in Multimodal Reasoning Models.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation More Thinking, Less Seeing? Assessing Amplified Hallucination in Multimodal Reasoning Models

Reference 9

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source=pdf_text observed=2026-08-04T10:40:40.740096Z digest=sha256:34a2b990b7dcc5839ebad76d7d37619d1f1fba4d8faca6a0c01049020477d74f

Observation a1af78be-1a98-4ab4-9aa8-268e8e5f965b · outbound

This paper cites MMLONGBENCH-DOC: benchmarking long-context document understanding with visualizations.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation MMLONGBENCH-DOC: benchmarking long-context document understanding with visualizations

Reference 10

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source=pdf_text observed=2026-08-04T10:40:40.812295Z digest=sha256:0b1fcc47899e26be5f4e2ada7da31b556f577c5bdb84aef7240f3eb6f90ec332

Observation 2bbdaa40-42cb-4216-9af5-abf927a224a0 · outbound

This paper cites Chartqa: A bench- mark for question answering about charts with visual and logical reasoning.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation Chartqa: A bench- mark for question answering about charts with visual and logical reasoning

Reference 11

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source=pdf_text observed=2026-08-04T10:40:40.888511Z digest=sha256:6ed916a1d968aa74b56ef5f4d89a0de6bf16956d6c2ab28c7b59fe70bf4398d6

Observation 52aa5362-0795-4a85-8fe7-7dc7265b75d2 · outbound

This paper cites MM-Eureka: Exploring the Frontiers of Multimodal Reasoning with Rule-based Reinforcement Learning.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation MM-Eureka: Exploring the Frontiers of Multimodal Reasoning with Rule-based Reinforcement Learning

Reference 13

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source=pdf_text observed=2026-08-04T10:40:41.087844Z digest=sha256:6eb50f4c0215ee23d616ad9a00401289f80112d4f7ac39b902876bc7714d20f6

Observation dc0df9de-a0b7-4b51-b1f2-12b2f71ac1f1 · outbound

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

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation Compositional chain-of- thought prompting for large multimodal models

Reference 14

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source=pdf_text observed=2026-08-04T10:40:41.185656Z digest=sha256:586367a438e2818d3963997d91be44083d9165f09daad7c089c4456a356e5abc

Observation 7dd9a68c-cf7a-486b-a568-c69818d463ae · outbound

This paper cites Mixture-of-Retrieval Experts for Reasoning-Guided Multimodal Knowledge Exploitation.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation Mixture-of-Retrieval Experts for Reasoning-Guided Multimodal Knowledge Exploitation

Reference 15

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source=pdf_text observed=2026-08-04T10:40:41.255247Z digest=sha256:6240cc9091fe7a8d557693ff466409e619e7f7ed14ce03924f4f46c979ae3469

Observation 7bf37899-8192-4252-b32b-46004f9abf72 · outbound

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

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation Direct preference optimization: Your language model is secretly a reward model

Reference 16

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source=pdf_text observed=2026-08-04T10:40:41.370251Z digest=sha256:81143930239f0a6a1e1a98c132c9430813a3ee0b5fd252e1ab20ba3112f6c9b4

Observation 50481eac-6e75-479f-bb53-dabbba35c617 · outbound

This paper cites VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model

Reference 19

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source=pdf_text observed=2026-08-04T10:40:41.865785Z digest=sha256:e7c5373da4617c22086332f4996bbaf862d6a74b55f6a4d50d11c30d27f08e22

Observation b894b856-43a3-4224-b398-0ba3694c7389 · outbound

This paper cites R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning

Reference 20

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source=pdf_text observed=2026-08-04T10:40:42.001140Z digest=sha256:a71cb0fdcc72bc67e1df3d9c6ee9496cfabc9cffb303b15286f057a8783c01c9

Observation 89dc95ed-2c01-4b09-84bc-c5f9d8f5d052 · outbound

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

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation Slidevqa: A dataset for document visual question answering on multiple images

Reference 21

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source=pdf_text observed=2026-08-04T10:40:42.115895Z digest=sha256:cd16d4b9f98d1f4c58fc8584742f14a5f1b810f553497a5de2be8ac54c889c52

Observation 52d1dcbe-62e6-40ed-b846-3e4e07250478 · outbound

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

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation ViDoRAG: Visual Document Retrieval-Augmented Generation via Dynamic Iterative Reasoning Agents

Reference 22

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Observation cd68caac-9f0d-4ae3-90d8-da8961af88f8 · outbound

This paper cites MMSearch-R1: Incentivizing LMMs to Search.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation MMSearch-R1: Incentivizing LMMs to Search

Reference 23

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source=pdf_text observed=2026-08-04T10:40:42.435539Z digest=sha256:1b22e04534f9061e08421b7da56e240ed84f7f7f3295088fe6b0d41818b50d2f

Observation f71a09d5-6d54-4b7c-8b97-ee11e5773ca8 · outbound

This paper cites MiMo-VL Technical Report.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation MiMo-VL Technical Report

Reference 24

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source=pdf_text observed=2026-08-04T10:40:42.595790Z digest=sha256:b8882f3003f0d2f1ba3be27c738bfaa55aa5f782a8590bc0bfcb9ef36979883e

Observation 2def8d7a-dddd-4ee8-9c4d-dc480f652b25 · outbound

This paper cites Visrag: Vision-based retrieval-augmented generation on multi- modality documents.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation Visrag: Vision-based retrieval-augmented generation on multi- modality documents

Reference 25

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source=pdf_text observed=2026-08-04T10:40:42.850592Z digest=sha256:15d84fdbd3736b85c944c1fd4cfd94aca82927293390a3181d5b8d1ab1de1551

Observation b41ac04a-da97-4869-a536-9d821f622145 · outbound

This paper cites Vision-R1: Evolving Human-Free Alignment in Large Vision-Language Models via Vision-Guided Reinforcement Learning.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation Vision-R1: Evolving Human-Free Alignment in Large Vision-Language Models via Vision-Guided Reinforcement Learning

Reference 26

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source=pdf_text observed=2026-08-04T10:40:42.936784Z digest=sha256:39a7e6bc200e16f274309bea5d4b55eb269cbf9cf90c4839c6fc6a4609f82190

Observation cd670699-7307-452d-85fd-260683163256 · outbound

This paper cites CoCoT: Contrastive Chain-of-Thought Prompting for Large Multimodal Models with Multiple Image Inputs.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation CoCoT: Contrastive Chain-of-Thought Prompting for Large Multimodal Models with Multiple Image Inputs

Reference 27

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source=pdf_text observed=2026-08-04T10:40:43.075026Z digest=sha256:2c9ee14f630aadbc5a8e444a81cca5943cb2335716b74ab852bd3e31339e524a

Observation 375cf03b-0826-4779-8034-58bf37bed387 · outbound

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

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

Reference 28

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source=pdf_text observed=2026-08-04T10:40:43.207178Z digest=sha256:6ea8d4d61909b95b148bf3747415972df88b299f6fec16346105faa65055133e

Observation c6b1a08a-4ce9-46ab-b18e-edb10fcb4c80 · outbound

This paper cites Each dataset provides ground-truth answer image IDs.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation Each dataset provides ground-truth answer image IDs

Reference 29

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source=pdf_text observed=2026-08-04T10:40:43.381867Z digest=sha256:75b595b979200e9d0c80a92d30b90bac22913dff7637bd084a50aa79cc281652

Observation b4443730-2d6f-4012-8e8a-009db839392a · outbound

This paper cites During the retrieval stage, VisRAG-Ret retrieves the top five can- didate images for each query.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation During the retrieval stage, VisRAG-Ret retrieves the top five can- didate images for each query

Reference 30

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Observation d16caa28-0fc5-4fea-a0be-c7bbb885efbe · outbound

This paper cites aha moments.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation aha moments

Reference 31

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source=pdf_text observed=2026-08-04T10:40:43.728043Z digest=sha256:bccbbfd02f6696cf8acc0ca5f2759d97a533e19cf0cdaf6eb1381aa4bbf4ea2c

Observation 9d8ed55e-988c-4eef-90c3-d35a44a09daf · outbound

This paper cites General VLMs.We assessed general vision-language models across different scales, namely Qwen2.5-VL-7B and Qwen2.5-VL-32B (Bai et al., 2025), as well as MiMo-VL-7B-RL (Xiaomi, 2025).

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation General VLMs.We assessed general vision-language models across different scales, namely Qwen2.5-VL-7B and Qwen2.5-VL-32B (Bai et al., 2025), as well as MiMo-VL-7B-RL (Xiaomi, 2025)

Reference 32

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source=pdf_text observed=2026-08-04T10:40:43.914660Z digest=sha256:0b891f81a65dfcfe2db331d1d76b97adc2e49316ae6cffc519705fc733695c3d

Observation 92965a41-cff5-4a4c-a3a4-f2e16949db08 · outbound

This paper cites Why Young Americans are Driving So Much Less Than Their Parents.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation Why Young Americans are Driving So Much Less Than Their Parents

Reference 33

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Observation 41a13791-1866-43c8-a364-ace509891717 · outbound

This paper cites This image contains the information needed to identify the country and its major languages.Image 2 is the China Fact Sheet.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation This image contains the information needed to identify the country and its major languages.Image 2 is the China Fact Sheet

Reference 36

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source=pdf_text observed=2026-08-04T10:40:44.588079Z digest=sha256:06992d09d4940d7a873d0b794503e4ce604b1a0e8ea69a397e1e4ea82a99d219

Observation 6372b211-81ff-4845-95b4-c133ed76e65c · outbound

This paper cites an unresolved cited work.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation Unresolved cited work

Reference 37

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Observation bffc8a2f-b688-4e97-9854-2f432258b54f · outbound

This paper cites an unresolved cited work.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation Unresolved cited work

Reference 38

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Observation 6ee18de2-282a-4466-83d0-a5c95369e45e · outbound

This paper cites an unresolved cited work.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation Unresolved cited work

Reference 39

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source=pdf_text observed=2026-08-04T10:40:45.012731Z digest=sha256:acca13bce28b983a224eff5f29b5b8b81695ce5183fef4815ebfcf577b72ee7d

Observation ffc2d9ef-4a2a-4a8e-a209-4dd6e57e6e02 · outbound

This paper cites These languages are Mandarin, Yue (Cantonese), Wu (Shanghainese), Minbei(Fuzhou), Minnan (Hokkien-Taiwanese), Xiang, and Gan.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation These languages are Mandarin, Yue (Cantonese), Wu (Shanghainese), Minbei(Fuzhou), Minnan (Hokkien-Taiwanese), Xiang, and Gan

Reference 40

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source=pdf_text observed=2026-08-04T10:40:45.142116Z digest=sha256:83d89f652fc864d2854deaafd924701f4ca3da7646f0b2febad812982ef32473

Observation e9c3c62b-efab-4f47-aa01-b93e1b0ed4d1 · outbound

This paper cites an unresolved cited work.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation Unresolved cited work

Reference 41

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source=pdf_text observed=2026-08-04T10:40:45.271374Z digest=sha256:770ae8bcd751cee6a4be6be542b91f4b3c291ce18bcd7ce0dd4c84ce2bf18403

Observation ae741f1b-d388-4662-8b9b-1bc7054fd425 · outbound

This paper cites yes" or.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation yes" or

Reference 1997

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unresolved
no resolver link, observed 2026-08-04T10:40:44.384744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T10:40:44.384744Z digest=sha256:3b75adbb939e496600848fdf621680242a4ae62c115ac0801796a19754105359

Observation fe31c3db-833c-4e61-b948-75c84f96beb5 · outbound

This paper cites Why Young Americans Are Driving So Much Less Than Their Parents.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation Why Young Americans Are Driving So Much Less Than Their Parents

Reference 2012

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unresolved
no resolver link, observed 2026-08-04T10:40:44.210080Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T10:40:44.210080Z digest=sha256:9be2df4690893216dab4a7bfd3e31c024bde310ebe95ef86dbbfb3b236edc011

Observation 27b4a6d3-525d-4e6c-aa8a-d60657cf72bb · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 2017

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unresolved
no resolver link, observed 2026-08-04T10:40:41.676641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T10:40:41.676641Z digest=sha256:8cf5be17ea876e85695192c91747162e0a80851c4588f9a3fac4fd5b9f21b844

Observation 4f9ef482-114f-4fb3-99e0-b3ffb326e293 · outbound

This paper cites Search-o1: Agentic Search-Enhanced Large Reasoning Models.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation Search-o1: Agentic Search-Enhanced Large Reasoning Models

Reference 2020

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unresolved
no resolver link, observed 2026-08-04T10:40:40.583884Z

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source=pdf_text observed=2026-08-04T10:40:40.583884Z digest=sha256:b9d34df0f02d6e9b2c4e47c773975bb4a779a1c6db7ef4fff929d4870ead6370

Observation 9b1dbc2d-de7f-4284-ae36-ce4007a7ac8c · outbound

This paper cites Infographicvqa.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation Infographicvqa

Reference 2022

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unresolved
no resolver link, observed 2026-08-04T10:40:40.976385Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T10:40:40.976385Z digest=sha256:f4e19800ef37034668f6d9595d3673b1187fd13c8be6871f0fddf14481baf2f6

Observation d9277bbb-9f91-4800-83d6-38035ce912e4 · outbound

This paper cites Proximal Policy Optimization Algorithms.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation Proximal Policy Optimization Algorithms

Reference 2023

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unresolved
no resolver link, observed 2026-08-04T10:40:41.495917Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T10:40:41.495917Z digest=sha256:2b70cc9136e5a04250e99d26e754a9abf8307eda71033c4e048447173b7457ae

Observation 1a25c138-4432-4899-9b93-a37674037146 · outbound

This paper cites Qwen2.5-VL Technical Report.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation Qwen2.5-VL Technical Report

Reference 2024

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unresolved
no resolver link, observed 2026-08-04T10:40:39.822350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T10:40:39.822350Z digest=sha256:c2f4d0476884de67e4cc30abaf43e3b1a478d8353a5648663e3e17f02802eef5

Observation 3092fd19-e853-46fa-a63f-510b2ef40d08 · outbound

This paper cites github.io/blog/2025/Polaris.

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation github.io/blog/2025/Polaris

Reference 2025

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unresolved
no resolver link, observed 2026-08-04T10:40:39.676363Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-04T10:40:39.676363Z digest=sha256:654568d4aed93e1ea6301b76ce0e2e96983b4a26e1a4856ce2d59bdf2049d538

Pith citing papers

Observation 8314f735-3cd5-4026-a69b-37eb60ce1769 · 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 VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation

Reference 23

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verified exact
arxiv_id, observed 2026-07-29T02:25:09.242061Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-10T17:45:21.088097Z digest=sha256:5c8faf986d5bb8927692e4515fade4f69db8ce76e046ea2713be3b194dcc5340

Observation c5b73bbf-e22c-4165-9cb5-f1312655973b · inbound

VLD-RAG: Agentic Vision-Language Retrieval-Augmented Generation for Long, Visually-Rich Multi-Page Documents cites this paper.

VLD-RAG: Agentic Vision-Language Retrieval-Augmented Generation for Long, Visually-Rich Multi-Page Documents VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation

Reference 1166

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no resolver link, observed 2026-08-02T13:26:30.298761Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T13:26:30.298761Z digest=sha256:74dac2123ce5062009ac1b6b3d9a5d3899777c27f0a9b0d1e6b364dd8301f527

Observation 0c1c64e5-43e1-4b3d-936e-77c1ad65fa16 · inbound

HiEviDR-Bench: A Benchmark for Hierarchical Evidence Aggregation in Deep Research cites this paper.

HiEviDR-Bench: A Benchmark for Hierarchical Evidence Aggregation in Deep Research VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation

Reference 21

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no resolver link, observed 2026-07-31T00:05:16.817803Z

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source=pdf_text observed=2026-07-31T00:05:16.817803Z digest=sha256:cd6bcee720939e7d5c3dc870ed2224b0cdb8d96ec92df718e1bad11c9d97295a