Paper Citation Record · LEDGER

Mimir: Improving Video Diffusion Models for Precise Text Understanding

As of 13 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 3 inbound Pith citation observations for arXiv:2412.03085.

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

pith.paper-citation-record.v1
2412.03085 v1

Coverage vector

measured 68 of 68 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T22:51:11.861299Z

measured 71 of 71 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T21:09:07.991313Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T01:53:28.925576Z

Reference resolution

68 of 68 outbound references displayed

verified exact0
verified fuzzy23
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fa3d78dd-059b-486f-854a-f07c0d1cf60d · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 1

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Observation 174ce6c1-871c-49fe-be62-2eee06cdd298 · outbound

This paper cites Qwen Technical Report.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Qwen Technical Report

Reference 2

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Source-reported events for the cited work

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Observation 93c2fe9c-38a0-42f0-b0c2-532a69bedd52 · outbound

This paper cites eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers.

Mimir: Improving Video Diffusion Models for Precise Text Understanding eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers

Reference 3

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Observation 31fbc9c4-a06d-4694-896e-45314cf6334a · outbound

This paper cites LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders.

Mimir: Improving Video Diffusion Models for Precise Text Understanding LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 4

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Observation a5492bb5-849a-4f41-a4fb-73ab32b8f523 · outbound

This paper cites Improving image generation with better captions.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Improving image generation with better captions

Reference 5

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Observation dc2703c4-4df9-446b-ae3f-c6d6f1b7fdb4 · outbound

This paper cites Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 6

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Source-reported events for the cited work

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Observation f009921e-e3c9-4f5c-aa70-1002c1168767 · outbound

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

Mimir: Improving Video Diffusion Models for Precise Text Understanding PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T22:51:11.639492Z

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Observation 5b167563-b1c0-4aff-a3e9-c7d1ffa9c571 · outbound

This paper cites PixArt-\Sigma: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation.

Mimir: Improving Video Diffusion Models for Precise Text Understanding PixArt-\Sigma: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation

Reference 8

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Source-reported events for the cited work

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Observation de3a697d-3f46-4bb7-ad16-68da13c74268 · outbound

This paper cites PIXART-{\delta}: Fast and Controllable Image Generation with Latent Consistency Models.

Mimir: Improving Video Diffusion Models for Precise Text Understanding PIXART-{\delta}: Fast and Controllable Image Generation with Latent Consistency Models

Reference 9

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Observation 66913913-901d-4840-89c9-15821d23ebca · outbound

This paper cites OD-VAE: An Omni-dimensional Video Compressor for Improving Latent Video Diffusion Model.

Mimir: Improving Video Diffusion Models for Precise Text Understanding OD-VAE: An Omni-dimensional Video Compressor for Improving Latent Video Diffusion Model

Reference 10

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Source-reported events for the cited work

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Observation b244d4ae-c421-41b0-a980-c1150df05139 · outbound

This paper cites Taming transformers for high-resolution image synthesis.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Taming transformers for high-resolution image synthesis

Reference 11

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Observation 62d3c6c2-d215-4a35-b549-8674f9f8b614 · outbound

This paper cites Scaling rectified flow transformers for high-resolution image synthesis.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Scaling rectified flow transformers for high-resolution image synthesis

Reference 12

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Observation a619a1c5-5332-4ef9-ba53-7b5841425048 · outbound

This paper cites Perceptual quality assessment of smartphone photography.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Perceptual quality assessment of smartphone photography

Reference 13

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Observation e2a60417-8b0d-4c08-9027-32ec10e61a03 · outbound

This paper cites Ranni: Taming text-to-image diffusion for accurate instruction following.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Ranni: Taming text-to-image diffusion for accurate instruction following

Reference 14

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Observation 45b05fbf-8760-4267-bc57-45d5b7dfed17 · outbound

This paper cites Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers

Reference 15

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Source-reported events for the cited work

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Observation c3cf3bea-0787-4c18-a181-d61f21012c25 · outbound

This paper cites Preserve your own correlation: A noise prior for video diffusion models.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Preserve your own correlation: A noise prior for video diffusion models

Reference 16

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Observation 8db5b61a-cb49-4097-bf14-00676a7ecaf5 · outbound

This paper cites Check locate rectify: A training- free layout calibration system for text-to-image generation.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Check locate rectify: A training- free layout calibration system for text-to-image generation

Reference 17

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Observation 7a1b328f-6ac9-4d11-a578-f0ca4f9f0ed1 · outbound

This paper cites AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning.

Mimir: Improving Video Diffusion Models for Precise Text Understanding AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning

Reference 18

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Observation 7d66e66d-b89d-4fc4-8936-e018f8b2aada · outbound

This paper cites Denoising dif- fusion probabilistic models.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Denoising dif- fusion probabilistic models

Reference 19

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Observation d8128cdc-9f30-49be-9d2c-1ecf7b7ddc93 · outbound

This paper cites Video dif- fusion models.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Video dif- fusion models

Reference 20

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Observation 72645895-e98b-4a7f-a8a6-0a3b48f4dc0d · outbound

This paper cites CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers.

Mimir: Improving Video Diffusion Models for Precise Text Understanding CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers

Reference 21

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Observation a21b01a0-49ff-4cb7-b178-40b54e03990d · outbound

This paper cites CogVLM2: Visual Language Models for Image and Video Understanding.

Mimir: Improving Video Diffusion Models for Precise Text Understanding CogVLM2: Visual Language Models for Image and Video Understanding

Reference 22

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Observation 22e6d8b2-de04-450e-83c8-346d24e7b021 · outbound

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

Mimir: Improving Video Diffusion Models for Precise Text Understanding ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment

Reference 23

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Observation 6baa9656-9778-4f21-8fb8-8ab3caa37b8b · outbound

This paper cites T2i-compbench: A comprehensive bench- mark for open-world compositional text-to-image genera- 9 tion.

Mimir: Improving Video Diffusion Models for Precise Text Understanding T2i-compbench: A comprehensive bench- mark for open-world compositional text-to-image genera- 9 tion

Reference 24

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Observation ebc51882-8616-4c0c-bf7d-49852386588f · outbound

This paper cites VBench: Com- prehensive benchmark suite for video generative models.

Mimir: Improving Video Diffusion Models for Precise Text Understanding VBench: Com- prehensive benchmark suite for video generative models

Reference 25

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Observation 6239cb00-0320-49f7-9520-3e90afdea811 · outbound

This paper cites Musiq: Multi-scale image quality transformer.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Musiq: Multi-scale image quality transformer

Reference 26

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Observation b88ade9b-eb5f-425a-a074-e21a22a4ae57 · outbound

This paper cites VideoPoet: A Large Language Model for Zero-Shot Video Generation.

Mimir: Improving Video Diffusion Models for Precise Text Understanding VideoPoet: A Large Language Model for Zero-Shot Video Generation

Reference 27

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Observation 397fdc8b-b211-4ac7-a6b7-8bfe676f77f5 · outbound

This paper cites Open-sora-plan, 2024.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Open-sora-plan, 2024

Reference 28

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Observation 2220ec05-9a82-44ac-a17d-68ab422d66f6 · outbound

This paper cites Common diffusion noise schedules and sample steps are flawed.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Common diffusion noise schedules and sample steps are flawed

Reference 29

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Observation d22d68cb-0418-4f31-a267-d067f09854cd · outbound

This paper cites Videofusion: Decomposed diffusion models for high-quality video generation.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Videofusion: Decomposed diffusion models for high-quality video generation

Reference 30

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Observation f1f1dd64-9159-4d62-bedc-24cca4f2eeb3 · outbound

This paper cites Exploring the Role of Large Language Models in Prompt Encoding for Diffusion Models.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Exploring the Role of Large Language Models in Prompt Encoding for Diffusion Models

Reference 31

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Observation 94af8bdb-1df9-49ce-be09-ab6e4bf4e9aa · outbound

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

Mimir: Improving Video Diffusion Models for Precise Text Understanding SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 32

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Observation caf6a1f7-5e7c-418c-b5eb-ce2e29ce478f · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Learning transferable visual models from natural language supervi- sion

Reference 33

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Observation 9cdd973b-fe4b-4b4c-ba09-61c8c29dd0c8 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 34

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Observation ca035896-122e-4992-b697-583963c91b40 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 35

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This paper cites High-resolution image synthesis with latent diffusion models.

Mimir: Improving Video Diffusion Models for Precise Text Understanding High-resolution image synthesis with latent diffusion models

Reference 36

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Observation 3cc13530-d148-47f9-a88c-90caab8fef4e · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Photorealistic text-to-image diffusion models with deep language understanding

Reference 37

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Observation 604fe72a-990a-44bf-a5b3-5b2f919b16ff · outbound

This paper cites Progressive Distillation for Fast Sampling of Diffusion Models.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Progressive Distillation for Fast Sampling of Diffusion Models

Reference 38

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Observation 51edff70-7685-45e7-81c3-482dcdb6b7cb · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Deep unsupervised learning using nonequilibrium thermodynamics

Reference 39

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Observation 2d940805-e791-4ff3-8f01-eb3f97354397 · outbound

This paper cites Stable Diffusion 2.0 Release, 2022.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Stable Diffusion 2.0 Release, 2022

Reference 40

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Observation 8c4b294e-7380-4d32-bce9-ee3cf89529b9 · outbound

This paper cites Galip: Generative adversarial clips for text-to-image synthesis.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Galip: Generative adversarial clips for text-to-image synthesis

Reference 41

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Observation 9b869e0b-9656-42a2-a5a3-2275c644060a · outbound

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

Mimir: Improving Video Diffusion Models for Precise Text Understanding Gemini: A Family of Highly Capable Multimodal Models

Reference 42

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Source-reported events for the cited work

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This paper cites Internlm: A multilingual language model with progressively enhanced capabilities, 2023.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Internlm: A multilingual language model with progressively enhanced capabilities, 2023

Reference 43

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This paper cites LLaMA: Open and Efficient Foundation Language Models.

Mimir: Improving Video Diffusion Models for Precise Text Understanding LLaMA: Open and Efficient Foundation Language Models

Reference 44

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Observation ebbd1558-1adf-4a4b-8507-97a12bac64dc · outbound

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Mimir: Improving Video Diffusion Models for Precise Text Understanding Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 45

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This paper cites Visualizing data using t-sne.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Visualizing data using t-sne

Reference 46

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Source-reported events for the cited work

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Observation 370095dc-e1e5-452a-ba97-170d31db0697 · outbound

This paper cites Cogvlm: Visual expert for pretrained language models, 2023.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Cogvlm: Visual expert for pretrained language models, 2023

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:51:12.305573Z

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Observation b4c52a21-8243-4808-973b-25604af2bee3 · outbound

This paper cites InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation.

Mimir: Improving Video Diffusion Models for Precise Text Understanding InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation

Reference 48

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This paper cites Grit: A gener- ative region-to-text transformer for object understanding.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Grit: A gener- ative region-to-text transformer for object understanding

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:51:12.295080Z

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Observation 078ce21b-e4e4-407b-9b82-e25718a751a9 · outbound

This paper cites Paragraph-to-Image Generation with Information-Enriched Diffusion Model.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Paragraph-to-Image Generation with Information-Enriched Diffusion Model

Reference 50

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Source-reported events for the cited work

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Observation 02eba41d-9f32-41c5-9774-7c3e884688df · outbound

This paper cites SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers.

Mimir: Improving Video Diffusion Models for Precise Text Understanding SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers

Reference 51

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Observation b644eab3-2884-4903-a021-e10675749e39 · outbound

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Mimir: Improving Video Diffusion Models for Precise Text Understanding Baichuan 2: Open Large-scale Language Models

Reference 52

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Observation c6430e2a-71bf-494e-8f17-c1d6284c3712 · outbound

This paper cites CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer.

Mimir: Improving Video Diffusion Models for Precise Text Understanding CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

Reference 53

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Observation f7e04b10-0996-4aa7-9f3e-29316a62060f · outbound

This paper cites Yi: Open Foundation Models by 01.AI.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Yi: Open Foundation Models by 01.AI

Reference 54

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This paper cites Magvit: Masked generative video transformer.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Magvit: Masked generative video transformer

Reference 55

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Observation d22537aa-a24e-42e8-bc1b-85ac610b88c6 · outbound

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

Mimir: Improving Video Diffusion Models for Precise Text Understanding Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation

Reference 56

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This paper cites Spae: Semantic pyramid autoencoder for multimodal generation with frozen llms.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Spae: Semantic pyramid autoencoder for multimodal generation with frozen llms

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:51:12.274098Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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This paper cites Bridging Different Language Models and Generative Vision Models for Text-to-Image Generation.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Bridging Different Language Models and Generative Vision Models for Text-to-Image Generation

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-11T22:51:11.822595Z

Source-reported events for the cited work

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Observation f36d674f-0dae-4596-95b0-33d22e2afff5 · outbound

This paper cites CV-VAE: A Compatible Video VAE for Latent Generative Video Models.

Mimir: Improving Video Diffusion Models for Precise Text Understanding CV-VAE: A Compatible Video VAE for Latent Generative Video Models

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-11T22:51:11.827049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1199afe8-f167-4d7b-abc3-248dd0a6b825 · outbound

This paper cites Open-sora: Democratizing efficient video production for all, march 2024.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Open-sora: Democratizing efficient video production for all, march 2024

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:51:12.263393Z

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Observation 2fde7757-3226-4491-89de-2b0d958a469c · outbound

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Mimir: Improving Video Diffusion Models for Precise Text Understanding Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-08-11T22:51:12.254031Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f956b5f7-5c53-49a0-8f7e-41a835d0d893 · outbound

This paper cites • Videos with a motion score of 0, determined using optical flow, are excluded.

Mimir: Improving Video Diffusion Models for Precise Text Understanding • Videos with a motion score of 0, determined using optical flow, are excluded

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:51:12.243362Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8515fc69-f386-4409-8cea-ba987206a161 · outbound

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Mimir: Improving Video Diffusion Models for Precise Text Understanding Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-11T22:51:12.231669Z

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Observation ed2bb0e4-2c56-40f2-a982-cc8cd9bf3c35 · outbound

This paper cites Input text prompt.

Mimir: Improving Video Diffusion Models for Precise Text Understanding Input text prompt

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:51:12.217479Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 537e7d6e-a8cd-4598-83db-03bae41de380 · outbound

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Mimir: Improving Video Diffusion Models for Precise Text Understanding Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-08-11T22:51:12.206404Z

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Observation a4417213-4970-4ef3-9ecb-4ba35cbc2b33 · outbound

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Mimir: Improving Video Diffusion Models for Precise Text Understanding containing watermarks

Reference 66

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Observation b87fc7b9-720d-48af-8501-26cd4788d9ce · outbound

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Mimir: Improving Video Diffusion Models for Precise Text Understanding Unresolved cited work

Reference 67

Resolution: unresolved; raw_fallback, observed 2026-08-11T22:51:12.186046Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a99ad1bb-e85d-4473-823a-b87f226ba4cb · outbound

This paper cites top”, “ below.

Mimir: Improving Video Diffusion Models for Precise Text Understanding top”, “ below

Reference 68

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

Observation 71f99619-f953-4740-9ac9-36f852f104e7 · inbound

Animate-X++: Universal Character Image Animation with Dynamic Backgrounds cites this paper.

Animate-X++: Universal Character Image Animation with Dynamic Backgrounds Mimir: Improving Video Diffusion Models for Precise Text Understanding

Reference 40

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Observation ed581e4b-c3b5-4117-9062-2b5d37bed3de · inbound

EDTalk++: Full Disentanglement for Controllable Talking Head Synthesis cites this paper.

EDTalk++: Full Disentanglement for Controllable Talking Head Synthesis Mimir: Improving Video Diffusion Models for Precise Text Understanding

Reference 39

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8513a963-5d84-4bb1-b70d-40f6565b1a0b · inbound

Bridging Brain and Semantics: A Hierarchical Framework for Semantically Enhanced fMRI-to-Video Reconstruction cites this paper.

Bridging Brain and Semantics: A Hierarchical Framework for Semantically Enhanced fMRI-to-Video Reconstruction Mimir: Improving Video Diffusion Models for Precise Text Understanding

Reference 86

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.