Paper Citation Record · LEDGER

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO

As of 18 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 10 inbound Pith citation observations for arXiv:2505.13031.

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

pith.paper-citation-record.v1
2505.13031 v2

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:26:58.409542Z

measured 67 of 67 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:09:10.990159Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T03:06:29.440180Z

Reference resolution

57 of 57 outbound references displayed

verified exact0
verified fuzzy7
unresolved50
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7431ca4d-00fc-4ac9-ba5b-c7a196d5b8f2 · outbound

This paper cites Qwen2.5-VL Technical Report.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO Qwen2.5-VL Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:58.092350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a51e9c89-782f-4e8b-966d-59c6fb7bd70c · outbound

This paper cites BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:58.099262Z

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Observation 389c266b-d5d5-4b08-8564-f988b4986220 · outbound

This paper cites PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:58.105088Z

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Observation 1b0cfad7-a596-43a5-9889-8a122e251c33 · outbound

This paper cites r1-v: Reinforcing super generalization ability in vision-language models with less than 3.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO r1-v: Reinforcing super generalization ability in vision-language models with less than 3

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:26:59.518115Z

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Observation 8d82be19-ace8-4e34-a7fa-675d7a3ffa2d · outbound

This paper cites an unresolved cited work.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO Unresolved cited work

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:58.116559Z

Source-reported events for the cited work

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Observation 766af505-288d-4f51-95e5-2adbc908af8d · outbound

This paper cites Science China Information Sciences67(12), 220101 (2024) 10.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO Science China Information Sciences67(12), 220101 (2024) 10

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:26:59.488898Z

Source-reported events for the cited work

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Observation d9523808-2f9f-45e0-8845-8718a37b2ac6 · outbound

This paper cites Emerging Properties in Unified Multimodal Pretraining.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO Emerging Properties in Unified Multimodal Pretraining

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:58.126897Z

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Observation 83349dd2-4b7d-4060-8d87-1c24a8796109 · outbound

This paper cites DreamLLM: Synergistic Multimodal Comprehension and Creation.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO DreamLLM: Synergistic Multimodal Comprehension and Creation

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:58.133735Z

Source-reported events for the cited work

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Observation 71cab7d0-ea13-4b25-88de-9852fedef3cd · outbound

This paper cites GoT: Unleashing Reasoning Capability of Multimodal Large Language Model for Visual Generation and Editing.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO GoT: Unleashing Reasoning Capability of Multimodal Large Language Model for Visual Generation and Editing

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:58.139315Z

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Observation eee365db-c203-4bf0-a66f-19a157c902cb · outbound

This paper cites Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:58.146231Z

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Observation 257300d6-e990-4252-a71f-e6d7c8cfda74 · outbound

This paper cites SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:58.153083Z

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Observation e2edbfb5-4dce-4b66-9659-d94dd2214f9d · outbound

This paper cites Advances in Neural Information Processing Systems pp.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO Advances in Neural Information Processing Systems pp

Reference 12

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Observation 25e2075e-38a0-460a-90fe-77141058ab01 · outbound

This paper cites an unresolved cited work.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO Unresolved cited work

Reference 13

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Source-reported events for the cited work

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Observation 3fcd54f2-d329-46df-825b-d4fff51a8731 · outbound

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

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:58.170393Z

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Observation 7528f7ae-75b6-40a4-bb11-02e6e231d17d · outbound

This paper cites Can We Generate Images with CoT? Let's Verify and Reinforce Image Generation Step by Step.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO Can We Generate Images with CoT? Let's Verify and Reinforce Image Generation Step by Step

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:58.175229Z

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Observation daf4f96a-731d-42e2-8f39-e4b1da2c1424 · outbound

This paper cites ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment

Reference 16

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Observation a90a0824-2081-47b8-928f-21aa3cabc3d1 · outbound

This paper cites ILLUME+: Illuminating Unified MLLM with Dual Visual Tokenization and Diffusion Refinement.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO ILLUME+: Illuminating Unified MLLM with Dual Visual Tokenization and Diffusion Refinement

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:58.185469Z

Source-reported events for the cited work

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Observation 8f7ffe76-90bb-44b7-ad54-10d5e720510a · outbound

This paper cites ARGS: Alignment as Reward-Guided Search.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO ARGS: Alignment as Reward-Guided Search

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:58.192292Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4882627-c6d9-4a15-b758-8c0764825b81 · outbound

This paper cites Training Language Models to Self-Correct via Reinforcement Learning.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO Training Language Models to Self-Correct via Reinforcement Learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:58.197909Z

Source-reported events for the cited work

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Observation 970508f6-7142-4887-a75f-fbc7eeecfd46 · outbound

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MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO Unresolved cited work

Reference 20

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Source-reported events for the cited work

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Observation 18aca474-96d7-4523-ba18-750605542886 · outbound

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

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO LLaVA-OneVision: Easy Visual Task Transfer

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:58.208127Z

Source-reported events for the cited work

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Observation 6a3dfc0b-2b32-446e-a4c0-85130968bbd0 · outbound

This paper cites Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:58.214608Z

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Observation 97f83c26-75db-462e-ab39-20c7d35cdd82 · outbound

This paper cites WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation

Reference 23

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Observation 7598a37a-d303-4a7a-b95d-38b2f95001f9 · outbound

This paper cites https://openai.com/o1 (2024).

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO https://openai.com/o1 (2024)

Reference 24

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Observation 1a45b6d6-9c64-4ef2-be54-7642c52af2ef · outbound

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MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO Unresolved cited work

Reference 25

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Observation 291b28c4-8111-430b-9d8c-04cdec535f42 · outbound

This paper cites Transfer between Modalities with MetaQueries.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO Transfer between Modalities with MetaQueries

Reference 26

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Observation 85580cca-6308-4b8d-8487-64a66c2ff527 · outbound

This paper cites In: Proceedings of the IEEE/CVF International Conference on Computer Vision.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO In: Proceedings of the IEEE/CVF International Conference on Computer Vision

Reference 27

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Observation 919c26c5-2940-41b1-b90f-853ae5be2a05 · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 28

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Observation c2050e8c-61c5-498d-b050-32f0d785b733 · outbound

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MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO Prism: A Framework for Decoupling and Assessing the Capabilities of VLMs

Reference 29

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Observation ec67038c-8d8a-4cdd-b29e-5fc2732a4d94 · outbound

This paper cites TokenFlow: Unified Image Tokenizer for Multimodal Understanding and Generation.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO TokenFlow: Unified Image Tokenizer for Multimodal Understanding and Generation

Reference 30

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Observation 28a7daf4-7488-4264-a553-191935772155 · outbound

This paper cites In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 31

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Observation fd21ddc3-1885-4e10-86b5-078be8500a1c · outbound

This paper cites URL https://laion.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO URL https://laion

Reference 32

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Observation dc57ee99-1fd9-474c-8474-4f5352a5cc1e · outbound

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

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 33

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Observation 201000fc-ad77-4f4e-bc67-e3e3d979dec3 · outbound

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MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO Unresolved cited work

Reference 34

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Observation 3c262794-8a07-4f9e-bede-c27c41c66ddf · outbound

This paper cites Advances in Neural Information Processing Systems36(2024).

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO Advances in Neural Information Processing Systems36(2024)

Reference 35

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Observation 4c803fed-c3c5-4f6d-8871-30da9ed25ac7 · outbound

This paper cites Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation

Reference 36

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Observation e4632963-3123-4104-b269-d512bd4d163d · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 37

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Observation 98a15ab1-62d7-4d53-9d89-e417288b8476 · outbound

This paper cites Emu: Generative Pretraining in Multimodality.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO Emu: Generative Pretraining in Multimodality

Reference 38

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This paper cites Chameleon: Mixed-Modal Early-Fusion Foundation Models.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO Chameleon: Mixed-Modal Early-Fusion Foundation Models

Reference 39

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Observation 66b03bf8-fcd7-4704-bded-6c384a40c4e6 · outbound

This paper cites https://qwenlm.github.io/blog/qwen3/ (2025).

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO https://qwenlm.github.io/blog/qwen3/ (2025)

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:26:59.291607Z

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Observation 24f5c3f4-324c-4a48-9a70-8ebbefc79636 · outbound

This paper cites MetaMorph: Multimodal Understanding and Generation via Instruction Tuning.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO MetaMorph: Multimodal Understanding and Generation via Instruction Tuning

Reference 41

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Observation a73daabc-cea4-4e13-a09e-18dec93b933a · outbound

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MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO SimpleAR: Pushing the Frontier of Autoregressive Visual Generation through Pretraining, SFT, and RL

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:58.325121Z

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MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO Emu3: Next-Token Prediction is All You Need

Reference 43

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MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 44

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MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO Advances in neural information processing systems35, 24824–24837 (2022)

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:58.341793Z

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MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation

Reference 46

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MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO OmniGen: Unified Image Generation

Reference 47

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MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 48

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This paper cites Advances in Neural Information Processing Systems37, 75329–75354 (2024).

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO Advances in Neural Information Processing Systems37, 75329–75354 (2024)

Reference 49

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MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer

Reference 50

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Unavailable: canonical work link unavailable.

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This paper cites Show-o: One Single Transformer to Unify Multimodal Understanding and Generation.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO Show-o: One Single Transformer to Unify Multimodal Understanding and Generation

Reference 51

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Observation 0d036f42-8e5b-4d3f-914f-31e1b6e806bf · outbound

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MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO LLaVA-CoT: Let Vision Language Models Reason Step-by-Step

Reference 52

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Observation caa0872c-e1fe-4ea6-a846-d49e2085b19a · outbound

This paper cites In: Forty-first International Conference on Machine Learning (2024).

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO In: Forty-first International Conference on Machine Learning (2024)

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:26:59.222106Z

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Observation f0d08762-d7eb-44f3-9d89-59bb95314e85 · outbound

This paper cites MMBench: Is Your Multi-modal Model an All-around Player?.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO MMBench: Is Your Multi-modal Model an All-around Player?

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:58.394455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 343a32e8-aecf-439f-acaa-75b61d7c7461 · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:58.399332Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2da049e6-d79a-4250-93a2-0ed83facdd20 · outbound

This paper cites Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:58.404285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aafa572c-8539-4cdd-bbf2-ea8757a1f41e · outbound

This paper cites R1-Zero's "Aha Moment" in Visual Reasoning on a 2B Non-SFT Model.

MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO R1-Zero's "Aha Moment" in Visual Reasoning on a 2B Non-SFT Model

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:58.409542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 6bdc7cd4-ff7c-49a9-905d-1c637f54ce25 · inbound

TokLIP: Marry Visual Tokens to CLIP for Multimodal Comprehension and Generation cites this paper.

TokLIP: Marry Visual Tokens to CLIP for Multimodal Comprehension and Generation MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-15T23:09:10.990159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b66d188-aedf-42e4-9b63-f192e703d310 · inbound

HaploOmni: Unified Single Transformer for Multimodal Video Understanding and Generation cites this paper.

HaploOmni: Unified Single Transformer for Multimodal Video Understanding and Generation MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:20.624099Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15fdf608-50a4-443a-b870-878a7cd630e2 · inbound

LoRA-Gen: Specializing Large Language Model via Online LoRA Generation cites this paper.

LoRA-Gen: Specializing Large Language Model via Online LoRA Generation MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T04:09:26.605018Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21e7eaad-27fb-40a0-a9c9-acdb4d3b6e1d · inbound

Demystifying Video Reasoning cites this paper.

Demystifying Video Reasoning MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO

Reference 67

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0967b82-5fb2-430d-ae00-a9f2fe1aabca · inbound

Demystifying Video Reasoning cites this paper.

Demystifying Video Reasoning MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-03T02:34:00.137676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17c4f5ff-d6f3-4ac4-b784-185498c4a051 · inbound

SpatialEdit: Benchmarking Fine-Grained Image Spatial Editing cites this paper.

SpatialEdit: Benchmarking Fine-Grained Image Spatial Editing MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO

Reference 62

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T23:00:50.371078Z

Source-reported events for the cited work

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Observation d7b75b5d-a3ce-440c-b68a-90040d96e677 · inbound

JoyAI-Image: Awaking Spatial Intelligence in Unified Multimodal Understanding and Generation cites this paper.

JoyAI-Image: Awaking Spatial Intelligence in Unified Multimodal Understanding and Generation MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO

Reference 91

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:55:43.694718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f35ec559-82e5-404d-9f3a-262d9319b7cb · inbound

JoyAI-Image: Awaking Spatial Intelligence in Unified Multimodal Understanding and Generation cites this paper.

JoyAI-Image: Awaking Spatial Intelligence in Unified Multimodal Understanding and Generation MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO

Reference 91

Resolution: verified exact; arxiv_id, observed 2026-05-21T08:19:52.751849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3c0041f6-f342-4a24-b0d8-2bfd251871a0 · inbound

RCoT-Seg: Reinforced Chain-of-Thought for Video Reasoning and Segmentation cites this paper.

RCoT-Seg: Reinforced Chain-of-Thought for Video Reasoning and Segmentation MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:30:59.889877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a6585bc5-4f90-46ac-91cc-5b059183f3b2 · inbound

UniCanvas: A Diffusion-base Unified Model for Text-in-Image Joint Generation cites this paper.

UniCanvas: A Diffusion-base Unified Model for Text-in-Image Joint Generation MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO

Reference 57

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T03:06:29.442237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.