Paper Citation Record · LEDGER

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model?

As of 19 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 1 inbound Pith citation observation for arXiv:2602.03300.

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

pith.paper-citation-record.v1
2602.03300 v2

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T05:06:46.943358Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T13:56:44.033301Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T13:56:45.816309Z

Reference resolution

31 of 31 outbound references displayed

verified exact0
verified fuzzy0
unresolved31
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 17697da4-e1db-493a-97b9-7fcc9dfb9c1f · outbound

This paper cites GPT-4 Technical Report.

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:43.811080Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec4a6470-c181-4530-a9a6-63935d15c1ff · outbound

This paper cites Qwen2.5-VL Technical Report.

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? Qwen2.5-VL Technical Report

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:44.059719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa159330-4121-4a8a-8e79-959a5537da5b · outbound

This paper cites Is synthetic data from generative models ready for image recognition?.

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? Is synthetic data from generative models ready for image recognition?

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:44.477234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf72144b-7df0-4ce3-86a4-f40582470776 · outbound

This paper cites Visual instruction tuning towards general-purpose multimodal large language model: A survey.International Journal of Computer Vision, 133 (11):8151–8189, 2025a.

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? Visual instruction tuning towards general-purpose multimodal large language model: A survey.International Journal of Computer Vision, 133 (11):8151–8189, 2025a

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:44.649028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68f0a91c-0dee-40ed-a57b-c99ac2814977 · outbound

This paper cites UniECG: Understanding and Generating ECG in One Unified Model.

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? UniECG: Understanding and Generating ECG in One Unified Model

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:44.804933Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 965a491d-2088-4e67-8a0e-2669f9c837a1 · outbound

This paper cites Kimi k1.5: Scaling Reinforcement Learning with LLMs.

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:44.871776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7bfd4cd8-0a78-4151-99e0-3beed869f9b6 · outbound

This paper cites Gem: Empowering mllm for grounded ecg under- standing with time series and images.arXiv preprint arXiv:2503.06073,.

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? Gem: Empowering mllm for grounded ecg under- standing with time series and images.arXiv preprint arXiv:2503.06073,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:44.938057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e695fbad-ccea-4b01-bdc7-1092f756f5ce · outbound

This paper cites Mmr1: Enhancing multimodal reasoning with variance- aware sampling and open resources.arXiv preprint arXiv:2509.21268,.

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? Mmr1: Enhancing multimodal reasoning with variance- aware sampling and open resources.arXiv preprint arXiv:2509.21268,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:44.994330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f470398-3349-426e-897d-3bc00111618e · outbound

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

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? LLaVA-OneVision: Easy Visual Task Transfer

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:45.072240Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65c1534e-1b89-400c-b96a-1bbc0500d2d5 · outbound

This paper cites InternVL-X: Advancing and Accelerating InternVL Series with Efficient Visual Token Compression.

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? InternVL-X: Advancing and Accelerating InternVL Series with Efficient Visual Token Compression

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:45.171417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc3aeafc-1426-42e2-80d4-9d567d253966 · outbound

This paper cites MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts.

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:45.322935Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f5682fd-b576-4ba5-be75-b908b83c5943 · outbound

This paper cites MathGenie: Generating Synthetic Data with Question Back-translation for Enhancing Mathematical Reasoning of LLMs.

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? MathGenie: Generating Synthetic Data with Question Back-translation for Enhancing Mathematical Reasoning of LLMs

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:45.572114Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da051922-2eb1-4dad-ab9b-78e5aad807db · outbound

This paper cites WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct.

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:45.671562Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0426af21-8af8-4ad0-bb22-8647ac544c3f · outbound

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

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? MM-Eureka: Exploring the Frontiers of Multimodal Reasoning with Rule-based Reinforcement Learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:45.784740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8725eab-6f66-49ac-bad5-05a659a1356d · outbound

This paper cites GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:45.899185Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80734e38-b2c9-4dda-acf6-17b720a4d969 · outbound

This paper cites Skywork R1V: Pioneering Multimodal Reasoning with Chain-of-Thought.

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? Skywork R1V: Pioneering Multimodal Reasoning with Chain-of-Thought

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:46.007695Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95052162-93d6-465b-b5b6-70b34aee1fc5 · outbound

This paper cites H., Fung, Y.

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? H., Fung, Y

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:46.065196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b013b36-0928-4115-bc33-ba8f96582e80 · outbound

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

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:46.246364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e97d76e5-79c5-4349-a417-00d673fa408c · outbound

This paper cites Mathcanvas: Intrin- sic visual chain-of-thought for multimodal mathematical reasoning.arXiv preprint arXiv:2510.14958,.

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? Mathcanvas: Intrin- sic visual chain-of-thought for multimodal mathematical reasoning.arXiv preprint arXiv:2510.14958,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:46.270959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1084d84-77ae-41c2-b84b-f66cc494ec91 · outbound

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

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? Gemini: A Family of Highly Capable Multimodal Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:46.370801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5894514a-0018-455e-9f0b-7cb375f7ce2f · outbound

This paper cites TreeSynth: Synthesizing Diverse Data from Scratch via Tree-Guided Subspace Partitioning.

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? TreeSynth: Synthesizing Diverse Data from Scratch via Tree-Guided Subspace Partitioning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:46.500989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8d4a86e-227e-4eb6-b32a-43cb1ac76d0b · outbound

This paper cites Valley2: Exploring Multimodal Models with Scalable Vision-Language Design.

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? Valley2: Exploring Multimodal Models with Scalable Vision-Language Design

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:46.616733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c126139-4733-4df9-b69f-9a0686b97e27 · outbound

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

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? LLaVA-CoT: Let Vision Language Models Reason Step-by-Step

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:46.661605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebdaf347-1ef0-4878-9454-1c338d963c0d · outbound

This paper cites R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization.

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:46.742733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65c49fc8-9273-4a28-b8b2-57b3a991ecdb · outbound

This paper cites MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark.

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:46.873839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73306920-f347-499a-a979-9995f2a2946d · outbound

This paper cites R1-VL: Learning to Reason with Multimodal Large Language Models via Step-wise Group Relative Policy Optimization.

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? R1-VL: Learning to Reason with Multimodal Large Language Models via Step-wise Group Relative Policy Optimization

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:46.943358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 861ed301-16a0-4dfa-8d71-9e49c904e3d6 · outbound

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

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:44.343829Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fccf839b-370a-4c67-a2c1-ce69ca1812d9 · outbound

This paper cites FLAMES: Improving LLM Math Reasoning via a Fine-Grained Analysis of the Data Synthesis Pipeline.

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? FLAMES: Improving LLM Math Reasoning via a Fine-Grained Analysis of the Data Synthesis Pipeline

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:46.167416Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 349738ae-b01c-42f3-91c0-9874107e2033 · outbound

This paper cites LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training.

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:43.872810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 144a3e77-020a-4d30-8563-7d187666c987 · outbound

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

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? OpenVLThinker: Complex Vision-Language Reasoning via Iterative SFT-RL Cycles

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:44.231599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47a1da03-6225-4e4d-a2c4-691835633a38 · outbound

This paper cites Qwen Technical Report.

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? Qwen Technical Report

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-03T05:06:43.939865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation c3c1546c-e411-47a9-bb16-a3fd71806f16 · inbound

Failure-Informed Image Self-Augmentation for Multimodal Large Language Model Self-Improvement cites this paper.

Failure-Informed Image Self-Augmentation for Multimodal Large Language Model Self-Improvement R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model?

Reference 20

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T13:56:45.865689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.