HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers | Paper Citation Record

Observation 38bdd4f4-4ae6-4cbc-8a8e-6a8b1096aaa4 · outbound

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

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model

Reference 1

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Observation a99eaeac-f037-4288-b968-bc0c8fbd28f7 · outbound

This paper cites International conference on machine learning , pages=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers International conference on machine learning , pages=

Reference 2

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Observation 02e5cf2a-28bb-488d-ad08-6f83a9478409 · outbound

This paper cites 2023 , url=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers 2023 , url=

Reference 3

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Observation cdf36153-0d10-429d-b56b-7db745fd1a9e · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , month =.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , month =

Reference 4

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Observation b1914e40-d6c6-407d-9df5-c0e2b7c05c9a · outbound

This paper cites OpenAI technical report , year=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers OpenAI technical report , year=

Reference 5

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Observation 08509cb8-d874-47df-9501-1200f6c3ecf7 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 6

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T14:28:32.084172Z

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Observation 7d0ad57d-079d-4584-8e35-429289c7a6d9 · outbound

This paper cites Data Filtering Networks.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Data Filtering Networks

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-03T14:28:32.070663Z

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Observation 53ff37cc-e9a1-4912-9182-ffa657609327 · outbound

This paper cites Proceedings of the IEEE/CVF international conference on computer vision , pages=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Proceedings of the IEEE/CVF international conference on computer vision , pages=

Reference 8

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Observation dc11bbb2-f9a0-4fad-b741-701085296d1e · outbound

This paper cites BEiT: BERT Pre-Training of Image Transformers.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers BEiT: BERT Pre-Training of Image Transformers

Reference 9

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T14:28:32.065519Z

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Observation ff37c413-f814-4513-942d-bfb1945f2d36 · outbound

This paper cites Proceedings of the IEEE/CVF international conference on computer vision , pages=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Proceedings of the IEEE/CVF international conference on computer vision , pages=

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

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Observation 78317259-bb70-4afc-9c69-fdc911f8532c · outbound

This paper cites From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T14:28:32.055745Z

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Observation 0d7bb7a9-fab0-4672-9fac-46779c9773a6 · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

Reference 12

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Observation edc35e0a-9445-4d58-bf6f-9bc4420c5c4b · outbound

This paper cites Benchmarking Detection Transfer Learning with Vision Transformers.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Benchmarking Detection Transfer Learning with Vision Transformers

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T14:28:32.128646Z

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Observation 2eebba51-a766-4f17-a24b-dc3f1061cae3 · outbound

This paper cites Proceedings of the IEEE international conference on computer vision , pages=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Proceedings of the IEEE international conference on computer vision , pages=

Reference 14

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Observation 381f1b43-cee1-4214-9fbd-71cc8c8626bd · outbound

This paper cites ArXiv , year=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers ArXiv , year=

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

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Observation e653307c-698c-489a-973d-bb1ded75a413 · outbound

This paper cites ArXiv , year=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers ArXiv , year=

Reference 16

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Observation b1efcd99-a458-4e1a-9a66-5ad02414ec2d · outbound

This paper cites 2024 , eprint=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers 2024 , eprint=

Reference 17

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Observation 41549d01-da07-44bf-8fbf-32f8c137c637 · outbound

This paper cites Repa-e: Unlocking vae for end-to-end tuning with latent diffusion transformers.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Repa-e: Unlocking vae for end-to-end tuning with latent diffusion transformers

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T14:28:32.238647Z

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Observation 03c4caf3-515c-4b02-b175-1eaa7394f102 · outbound

This paper cites Boosting latent diffusion models via disentangled representation alignment.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Boosting latent diffusion models via disentangled representation alignment

Reference 19

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T14:28:29.597324Z

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Observation 9d3abfb3-0d72-45f6-a516-78a74ab5dfc3 · outbound

This paper cites Towards scalable pre-training of visual tokenizers for generation.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Towards scalable pre-training of visual tokenizers for generation

Reference 20

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T14:38:28.907664Z

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Observation 3636ed34-81fc-46cd-be6e-e88d4a862b57 · outbound

This paper cites Wan: Open and Advanced Large-Scale Video Generative Models.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Wan: Open and Advanced Large-Scale Video Generative Models

Reference 21

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T14:38:28.873847Z

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Observation f7d6ba4f-720c-45ab-9dfc-d85a1f4922e2 · outbound

This paper cites Diffusion Transformers with Representation Autoencoders.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Diffusion Transformers with Representation Autoencoders

Reference 22

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T14:28:29.602800Z

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Observation 582b5745-87fd-47e3-9de4-c8010932e8a3 · outbound

This paper cites The prism hypothesis: Harmonizing semantic and pixel representations via unified autoencoding.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers The prism hypothesis: Harmonizing semantic and pixel representations via unified autoencoding

Reference 23

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T14:28:29.616556Z

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Observation 38ef4a00-688e-44c8-8d41-719bbf950efe · outbound

This paper cites Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

Reference 25

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Observation 8dab6d55-943d-4737-8d4b-44524abb0d83 · outbound

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

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 26

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T14:28:29.647041Z

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Observation e115c19a-7ec6-487c-a166-d64469dd806d · outbound

This paper cites Proceedings of the IEEE/CVF international conference on computer vision , pages=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Proceedings of the IEEE/CVF international conference on computer vision , pages=

Reference 27

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Observation 4abdca51-3c1d-4f56-bf19-faf3386e173c · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Advances in Neural Information Processing Systems , volume=

Reference 28

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

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Observation 2338cc16-d921-461a-a725-387847b30ac5 · outbound

This paper cites ArXiv , year=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers ArXiv , year=

Reference 29

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Observation be118554-1f50-4082-89ae-df5a631b0461 · outbound

This paper cites HunyuanImage 3.0 Technical Report.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers HunyuanImage 3.0 Technical Report

Reference 30

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T14:28:29.579017Z

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Observation b7f1b6ff-2dcc-4560-aacb-430297665062 · outbound

This paper cites Latent diffusion model without variational autoencoder.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Latent diffusion model without variational autoencoder

Reference 31

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T14:28:29.587652Z

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Observation d0b9e9da-8cd3-41c8-bef1-7dc9fa1b14e2 · outbound

This paper cites Vector-quantized Image Modeling with Improved VQGAN.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Vector-quantized Image Modeling with Improved VQGAN

Reference 32

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T14:28:29.663135Z

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Observation c5f8e4c3-3d5f-4a0d-a217-7affa712167b · outbound

This paper cites Diffusion Autoencoders are Scalable Image Tokenizers.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Diffusion Autoencoders are Scalable Image Tokenizers

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-07-03T14:28:29.657972Z

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Observation 73b856cd-2489-4a98-a6b8-e50d9e6ec436 · outbound

This paper cites generation: Taming optimization dilemma in latent diffusion models , author=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers generation: Taming optimization dilemma in latent diffusion models , author=

Reference 34

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Observation 294fda3b-4030-4564-84ef-2f2d40ecfee9 · outbound

This paper cites Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

Reference 35

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

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Observation fb04f536-e5c6-4bcc-8909-919f7e7233aa · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Advances in Neural Information Processing Systems , volume=

Reference 36

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Observation b47c3c07-ba25-4c7d-a6cb-35c54c83dfce · outbound

This paper cites arXiv preprint arXiv:2507.08441 , year=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers arXiv preprint arXiv:2507.08441 , year=

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-07-03T14:38:28.917237Z

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Observation 23721df6-51e7-48d1-9b31-dcc526261249 · outbound

This paper cites Advances in neural information processing systems , volume=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Advances in neural information processing systems , volume=

Reference 38

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

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Observation 1929781b-9882-4d01-8de5-f2999afddaa6 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Advances in Neural Information Processing Systems , volume=

Reference 39

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

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Observation 7787fef2-8047-4ad3-a8ac-e50abd4f1246 · outbound

This paper cites Latent denoising makes good visual tokenizers.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Latent denoising makes good visual tokenizers

Reference 40

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T14:38:28.905282Z

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Observation 49bd37e6-f4be-4269-b367-bccfadf8462f · outbound

This paper cites Advances in neural information processing systems , volume=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Advances in neural information processing systems , volume=

Reference 41

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

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Observation f71baa52-ab77-49c2-b5b6-9e760ef08680 · outbound

This paper cites Foundations and Trends.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Foundations and Trends

Reference 42

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Observation 1111e54b-6dcb-417e-b0fd-071e82cd40d2 · outbound

This paper cites Image Processing Algorithms and Techniques II , volume=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Image Processing Algorithms and Techniques II , volume=

Reference 43

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Observation 54209dcf-d4c5-420a-9e2a-0b3aa6947666 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Advances in Neural Information Processing Systems , volume=

Reference 44

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

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Observation bd2a13e7-4617-4f89-b924-0d3b765139af · outbound

This paper cites Open-MAGVIT2: An Open-Source Project Toward Democratizing Auto-regressive Visual Generation.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Open-MAGVIT2: An Open-Source Project Toward Democratizing Auto-regressive Visual Generation

Reference 45

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T14:38:28.889202Z

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Observation e9d36f68-0d8f-4812-9bec-57ab91a2194f · outbound

This paper cites Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation

Reference 46

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T14:38:28.909903Z

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Observation 613719cd-5b2a-4053-9b77-3a7047568675 · outbound

This paper cites International Conference on Learning Representations , year=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers International Conference on Learning Representations , year=

Reference 47

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

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Observation ecddbb8c-3993-45c6-a945-3b9b2ab7f84d · outbound

This paper cites Proceedings of the Computer Vision and Pattern Recognition Conference , pages=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Proceedings of the Computer Vision and Pattern Recognition Conference , pages=

Reference 48

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

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Observation e3b60c85-bd9d-43fc-9357-5d022a1a207b · outbound

This paper cites ImageFolder: Autoregressive Image Generation with Folded Tokens.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers ImageFolder: Autoregressive Image Generation with Folded Tokens

Reference 49

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T14:38:28.819341Z

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Observation c41b4ef4-93ed-4183-a671-cb9380c5377e · outbound

This paper cites Forty-second International Conference on Machine Learning , year=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Forty-second International Conference on Machine Learning , year=

Reference 50

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

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Observation a93c7eb2-26e8-4d8a-9c5a-d0af8fbf165e · outbound

This paper cites 2022 , eprint=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers 2022 , eprint=

Reference 51

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

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Observation c453ecf6-ccf9-48e1-a982-2321c425de44 · outbound

This paper cites Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

Reference 52

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

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Observation 092adf5e-e897-4641-8437-ffafa9b149cf · outbound

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

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Chameleon: Mixed-Modal Early-Fusion Foundation Models

Reference 53

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T14:38:28.816961Z

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Observation e6214d90-8861-4a4c-a0f4-2c1b5bfc5f4c · outbound

This paper cites 2025 , eprint=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers 2025 , eprint=

Reference 54

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

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Observation dd51b75a-c007-4e53-b58b-dd6166d1fa01 · outbound

This paper cites DreamLLM: Synergistic Multimodal Comprehension and Creation.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers DreamLLM: Synergistic Multimodal Comprehension and Creation

Reference 55

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T14:38:28.883801Z

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Observation 68408783-f0e0-41fd-9149-ca83de540ac1 · outbound

This paper cites Unified language-vision pretraining in llm with dynamic discrete visual tokenization.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Unified language-vision pretraining in llm with dynamic discrete visual tokenization

Reference 56

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T14:38:28.855777Z

Source-reported events for the cited work

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Observation 9254e3b3-1c2e-4e2b-a23d-dc6f3da66391 · outbound

This paper cites Proceedings of the Computer Vision and Pattern Recognition Conference , pages=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Proceedings of the Computer Vision and Pattern Recognition Conference , pages=

Reference 57

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

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Observation d1ce572f-4dd1-41f7-8b27-b0e4f573064b · outbound

This paper cites arXiv preprint , year=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers arXiv preprint , year=

Reference 58

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

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Observation 7805d954-f801-4294-a566-f745f9568dd5 · outbound

This paper cites Making LLaMA SEE and Draw with SEED Tokenizer.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Making LLaMA SEE and Draw with SEED Tokenizer

Reference 60

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T14:38:28.919623Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0c6aa083-e3d5-41ec-a214-a2276619f066 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Advances in Neural Information Processing Systems , volume=

Reference 61

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

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Observation 0a6b70da-000d-4b30-934e-013547a0b354 · outbound

This paper cites Emu3: Next-Token Prediction is All You Need.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Emu3: Next-Token Prediction is All You Need

Reference 62

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T14:38:28.912359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 65458b22-33de-4bb8-850e-92fe0ca81b9a · outbound

This paper cites Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

Reference 63

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

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Observation 977fd9ec-6dd2-431b-a9f4-f3480148eb34 · outbound

This paper cites Proceedings of the IEEE/CVF International Conference on Computer Vision , year=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Proceedings of the IEEE/CVF International Conference on Computer Vision , year=

Reference 64

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

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Observation 3383bfe0-8243-4391-98a0-1699edfd775d · outbound

This paper cites ILLUME: Illuminating Your LLMs to See, Draw, and Self-Enhance.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers ILLUME: Illuminating Your LLMs to See, Draw, and Self-Enhance

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-07-03T14:38:28.860881Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0f0ae40e-923c-4008-b8ac-c0f24f7e5690 · outbound

This paper cites Orthus: Autoregressive Interleaved Image-Text Generation with Modality-Specific Heads.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Orthus: Autoregressive Interleaved Image-Text Generation with Modality-Specific Heads

Reference 66

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T14:38:28.879242Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f02cbc2a-3ecd-4a24-9410-4721b4f3ccfc · outbound

This paper cites Proceedings of the Computer Vision and Pattern Recognition Conference , pages=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Proceedings of the Computer Vision and Pattern Recognition Conference , pages=

Reference 67

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

Source-reported events for the cited work

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Observation 08882222-8ee0-42b4-ae82-e8e2c6eb3999 · outbound

This paper cites Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

Reference 68

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

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Observation 8e32b757-65dd-4f02-a9ef-236b15d8ab85 · outbound

This paper cites 2023 , eprint=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers 2023 , eprint=

Reference 69

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

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Observation 9b7811ee-fe9d-4474-bfaa-5edb250b0eef · outbound

This paper cites 2025 , eprint=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers 2025 , eprint=

Reference 70

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

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Observation 6d93c88c-7d14-4a5d-ae65-d42304fbf072 · outbound

This paper cites QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation

Reference 71

Resolution: verified exact; arxiv_id, observed 2026-07-03T14:38:28.819010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 56505dc3-693d-40eb-bc29-692615926173 · outbound

This paper cites OpenUni: A Simple Baseline for Unified Multimodal Understanding and Generation.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers OpenUni: A Simple Baseline for Unified Multimodal Understanding and Generation

Reference 72

Resolution: verified exact; arxiv_id, observed 2026-07-03T14:38:28.896947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0c738ec6-7764-4a1d-a5fd-22cfe2530c27 · outbound

This paper cites Liquid: Language Models are Scalable and Unified Multi-modal Generators.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Liquid: Language Models are Scalable and Unified Multi-modal Generators

Reference 73

Resolution: verified exact; arxiv_id, observed 2026-07-03T14:38:28.909508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0cebaca9-115c-4a46-9df5-a92158d2c38a · outbound

This paper cites Unitok: A unified tokenizer for visual generation and understanding.arXiv preprint arXiv:2502.20321, 2025a.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Unitok: A unified tokenizer for visual generation and understanding.arXiv preprint arXiv:2502.20321, 2025a

Reference 74

Resolution: verified exact; arxiv_id, observed 2026-07-03T14:38:28.886403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f6597786-6122-437f-9948-ce371a6ddebf · outbound

This paper cites Unilip: Adapting clip for unified multimodal understanding, generation and editing.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Unilip: Adapting clip for unified multimodal understanding, generation and editing

Reference 76

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T14:28:32.190086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0677f3a0-72b9-48e3-9c23-e0e8fa80aea9 · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

Reference 77

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 750863bb-62c3-4cdf-815e-4f3281272cf1 · outbound

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

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers ILLUME+: Illuminating Unified MLLM with Dual Visual Tokenization and Diffusion Refinement

Reference 78

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T14:28:32.222192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 802d7e2d-d8dd-44e7-b3de-2ddda65cc6c1 · outbound

This paper cites XQ-GAN: An Open-source Image Tokenization Framework for Autoregressive Generation.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers XQ-GAN: An Open-source Image Tokenization Framework for Autoregressive Generation

Reference 79

Resolution: verified exact; arxiv_id, observed 2026-07-03T14:28:32.058282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7eb61c66-8c0d-41de-8f2e-6b6208abac1b · outbound

This paper cites Finite Scalar Quantization: VQ-VAE Made Simple.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Finite Scalar Quantization: VQ-VAE Made Simple

Reference 80

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T14:28:32.053215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2f200e47-713e-4aa5-beb2-522225c5c174 · outbound

This paper cites Robust Latent Matters: Boosting Image Generation with Sampling Error Synthesis.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Robust Latent Matters: Boosting Image Generation with Sampling Error Synthesis

Reference 81

Resolution: verified exact; arxiv_id, observed 2026-07-03T14:28:32.060656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2702eb36-e7e9-45bf-a32f-d60ad9f63b47 · outbound

This paper cites Proceedings of the IEEE/CVF International Conference on Computer Vision , year=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Proceedings of the IEEE/CVF International Conference on Computer Vision , year=

Reference 82

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cb9248f-9406-4295-ad51-3e7438e6479f · outbound

This paper cites Selftok: Discrete Visual Tokens of Autoregression, by Diffusion, and for Reasoning.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Selftok: Discrete Visual Tokens of Autoregression, by Diffusion, and for Reasoning

Reference 83

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T14:28:32.068307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 17636725-4e5a-4026-b180-1e1a1c2987e4 · outbound

This paper cites Forty-second International Conference on Machine Learning , year=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Forty-second International Conference on Machine Learning , year=

Reference 84

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

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Observation b2f6432a-7602-4fae-ae8c-d99b66e7788e · outbound

This paper cites MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities

Reference 85

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T14:28:32.043849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1be63069-928f-4b15-8b98-f36a20dfec22 · outbound

This paper cites Proceedings of the IEEE conference on computer vision and pattern recognition , pages=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

Reference 86

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84b5e0b9-3509-4e9d-bca9-9d2a67a885eb · outbound

This paper cites 2025 , url=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers 2025 , url=

Reference 87

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

Source-reported events for the cited work

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Observation 7221f5a7-2264-4f00-8acf-956dbc3cc119 · outbound

This paper cites Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

Reference 88

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0032e54-cc03-4315-9e48-79806020c9e8 · outbound

This paper cites Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

Reference 89

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

Source-reported events for the cited work

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Observation 13c1b387-1eb9-41e9-b899-4dab3c353bb0 · outbound

This paper cites European conference on computer vision , pages=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers European conference on computer vision , pages=

Reference 90

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

Source-reported events for the cited work

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Observation c50e2deb-5af4-4a25-a40d-015970efef42 · outbound

This paper cites Evaluating Object Hallucination in Large Vision-Language Models.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Evaluating Object Hallucination in Large Vision-Language Models

Reference 91

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T14:28:32.046198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation cd070a70-ef08-4dfa-924b-ca50fadc5394 · outbound

This paper cites nature , volume=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers nature , volume=

Reference 92

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2bb8a214-b480-438d-8ad8-f116101d8372 · outbound

This paper cites Proceedings of the European conference on computer vision (ECCV) , pages=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Proceedings of the European conference on computer vision (ECCV) , pages=

Reference 93

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

Source-reported events for the cited work

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Observation afc4c253-6112-4d28-911f-b7793737ffa0 · outbound

This paper cites Proceedings of the Computer Vision and Pattern Recognition Conference , pages=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Proceedings of the Computer Vision and Pattern Recognition Conference , pages=

Reference 94

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

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Observation fc4ef71f-64a8-4405-8b4f-590c9b026413 · outbound

This paper cites European conference on computer vision , pages=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers European conference on computer vision , pages=

Reference 96

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1e95262-2d95-41aa-a8c3-8d0ace513f77 · outbound

This paper cites International conference on machine learning , pages=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers International conference on machine learning , pages=

Reference 97

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e683a672-62fb-4d26-b850-ea2426fbc961 · outbound

This paper cites CoCa: Contrastive Captioners are Image-Text Foundation Models.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers CoCa: Contrastive Captioners are Image-Text Foundation Models

Reference 98

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T14:28:32.048574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 64630893-3eba-400a-ba49-bb6c866a70ea · outbound

This paper cites Internvideo-next: Towards general video foundation models without video-text supervision.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Internvideo-next: Towards general video foundation models without video-text supervision

Reference 99

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T14:28:32.051049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 36fac3a4-3f89-4464-afef-b3b4c1f6c9bc · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers DINOv2: Learning Robust Visual Features without Supervision

Reference 100

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T14:38:28.922207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 24e8eaff-199f-4922-a142-f9ee555bd31b · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Advances in Neural Information Processing Systems , volume=

Reference 103

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd201d84-672c-4b9e-bd3f-8edb0fb4ad87 · outbound

This paper cites Proceedings of the IEEE/CVF international conference on computer vision , pages=.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers Proceedings of the IEEE/CVF international conference on computer vision , pages=

Reference 104

Resolution: unresolved; no resolver link, observed 2026-06-27T07:01:07.362430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6948834e-a774-4b49-8d4d-7ada40ba6c8b · outbound

This paper cites DualToken: Towards Unifying Visual Understanding and Generation with Dual Visual Vocabularies.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers DualToken: Towards Unifying Visual Understanding and Generation with Dual Visual Vocabularies

Reference 105

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T14:28:32.073325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 470607fd-e8a9-4ccc-bfa1-82f4ba8a0c71 · outbound

This paper cites arXiv preprint arXiv:2503.06764 (2025) 4, 7, 9, 10, 1.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers arXiv preprint arXiv:2503.06764 (2025) 4, 7, 9, 10, 1

Reference 106

Resolution: verified exact; arxiv_id, observed 2026-07-03T14:28:32.098762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers