Paper Citation Record · LEDGER

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning

As of 7 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2506.10639.

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

pith.paper-citation-record.v1
2506.10639 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:29:39.770200Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

48 of 48 outbound references displayed

verified exact0
verified fuzzy5
unresolved41
parse uncertain0
malformed identifier2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b84bcf57-58d0-462d-952d-866492ac9364 · outbound

This paper cites Photorealistic video generation with diffusion models.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Photorealistic video generation with diffusion models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:34.683768Z

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Unavailable: canonical work link unavailable.

Observation 54a87cbf-5f78-4e5b-ac5a-2c797a55eedc · outbound

This paper cites VEnhancer: Generative Space-Time Enhancement for Video Generation.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning VEnhancer: Generative Space-Time Enhancement for Video Generation

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:34.726688Z

Source-reported events for the cited work

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Observation 61837416-4c48-4d40-a1ea-744561dedfb4 · outbound

This paper cites ModelScope Text-to-Video Technical Report.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning ModelScope Text-to-Video Technical Report

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:34.788118Z

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Observation 030c8769-eb0a-4444-a42b-0c9c94934cf0 · outbound

This paper cites Videocomposer: Compositional video synthesis with motion controllability.Advances in Neural Information Processing Systems, 36:7594–7611, 2023.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Videocomposer: Compositional video synthesis with motion controllability.Advances in Neural Information Processing Systems, 36:7594–7611, 2023

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:34.871711Z

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Observation cea318d0-78ef-4cac-b84e-9d63aeffe3ff · outbound

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

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:34.950538Z

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Unavailable: canonical work link unavailable.

Observation df4c2c65-6821-48b3-baf5-bbc242c71f17 · outbound

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

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Wan: Open and Advanced Large-Scale Video Generative Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:35.068376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45c4edba-a8a0-4f6d-b90f-8d2bd5484fdc · outbound

This paper cites HunyuanVideo: A Systematic Framework For Large Video Generative Models.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning HunyuanVideo: A Systematic Framework For Large Video Generative Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:35.164284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e670b5d-ee0f-4db5-8991-267f4fa16c44 · outbound

This paper cites Improving Video Generation with Human Feedback.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Improving Video Generation with Human Feedback

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:35.300572Z

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Unavailable: canonical work link unavailable.

Observation 3a0ac913-9b07-423f-aaf0-9bc2807e32a9 · outbound

This paper cites LiFT: Leveraging Human Feedback for Text-to-Video Model Alignment.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning LiFT: Leveraging Human Feedback for Text-to-Video Model Alignment

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:35.464597Z

Source-reported events for the cited work

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Observation 16fa84b6-39c8-44b6-8b70-bf336ecbad5c · outbound

This paper cites VideoDPO: Omni-Preference Alignment for Video Diffusion Generation.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning VideoDPO: Omni-Preference Alignment for Video Diffusion Generation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:35.557881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01fcccbf-f24b-4a32-81c2-f44da00ed37e · outbound

This paper cites VisionReward: Fine-Grained Multi-Dimensional Human Preference Learning for Image and Video Generation.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning VisionReward: Fine-Grained Multi-Dimensional Human Preference Learning for Image and Video Generation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:35.673428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f457a2f-0147-49f8-9192-12bba4d1b7f8 · outbound

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

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:35.783078Z

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Observation c82352ea-09b6-430a-810b-e87296faf42d · outbound

This paper cites VideoCrafter1: Open Diffusion Models for High-Quality Video Generation.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning VideoCrafter1: Open Diffusion Models for High-Quality Video Generation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:35.918527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f7beaeb-07e6-409a-b314-67ff6692abd5 · outbound

This paper cites Flexible diffusion modeling of long videos.Advances in Neural Information Processing Systems, 35:27953–27965, 2022.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Flexible diffusion modeling of long videos.Advances in Neural Information Processing Systems, 35:27953–27965, 2022

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:29:41.422461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 02ec4496-333c-4358-8659-b1e21813a7f7 · outbound

This paper cites Latte: Latent Diffusion Transformer for Video Generation.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Latte: Latent Diffusion Transformer for Video Generation

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:36.113916Z

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Observation 646db30b-0295-441b-bbe5-cc9f92d1ea79 · outbound

This paper cites Open-Sora: Democratizing Efficient Video Production for All.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Open-Sora: Democratizing Efficient Video Production for All

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:36.205019Z

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Observation d6d7c495-716f-4ad0-a45e-60a43486762a · outbound

This paper cites Vidm: Video implicit diffusion models.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Vidm: Video implicit diffusion models

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:29:41.191534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 15ee06df-264b-4605-9721-94e82cb64c0e · outbound

This paper cites Make-A-Video: Text-to-Video Generation without Text-Video Data.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Make-A-Video: Text-to-Video Generation without Text-Video Data

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:36.463830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42f65169-a2fb-4f95-8349-ff02e0bbf80b · outbound

This paper cites Show-1: Marrying pixel and latent diffusion models for text-to-video generation.International Journal of Computer Vision, pages 1–15, 2024.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Show-1: Marrying pixel and latent diffusion models for text-to-video generation.International Journal of Computer Vision, pages 1–15, 2024

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:29:40.957619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3b9c747b-fcd6-4b46-99e3-3dd3ab03be97 · outbound

This paper cites Allegro: Open the Black Box of Commercial-Level Video Generation Model.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Allegro: Open the Black Box of Commercial-Level Video Generation Model

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:36.769463Z

Source-reported events for the cited work

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Observation 5f4ac0bd-6c98-4bf0-81ba-564eb4ca4945 · outbound

This paper cites Scalable diffusion models with transformers.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Scalable diffusion models with transformers

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:36.858137Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b354a7ef-9b20-40eb-917b-c3b8cc792555 · outbound

This paper cites Elucidating the design space of diffusion-based generative models.Advances in neural information processing systems, 35:26565–26577, 2022.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Elucidating the design space of diffusion-based generative models.Advances in neural information processing systems, 35:26565–26577, 2022

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:36.936888Z

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Unavailable: canonical work link unavailable.

Observation ca030abf-da3c-4abc-b9c7-c5165272cde5 · outbound

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

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Scaling rectified flow trans- formers for high-resolution image synthesis

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:37.047405Z

Source-reported events for the cited work

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Observation 25f6955b-fcf0-426c-976e-59c15c8e099f · outbound

This paper cites VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:37.188560Z

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Observation 2bf2fd3b-c7b2-448f-b47f-37f961e3d7f8 · outbound

This paper cites WorldModelBench: Judging Video Generation Models As World Models.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning WorldModelBench: Judging Video Generation Models As World Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:37.277478Z

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Observation 98a0a420-08a3-4f6c-919e-0bf4d2afc50f · outbound

This paper cites Advantage-weighted regression: Simple and scalable off-policy reinforcement learning, 2019.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Advantage-weighted regression: Simple and scalable off-policy reinforcement learning, 2019

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:29:40.739996Z

Source-reported events for the cited work

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Observation c503522c-07ea-432d-b02c-7c13facee579 · outbound

This paper cites Aligning Text-to-Image Models using Human Feedback.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Aligning Text-to-Image Models using Human Feedback

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:37.530489Z

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Observation df2d7333-c8cc-4b69-90b2-6e06dc04ab51 · outbound

This paper cites Improving Dynamic Object Interactions in Text-to-Video Generation with AI Feedback.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Improving Dynamic Object Interactions in Text-to-Video Generation with AI Feedback

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:37.670705Z

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Observation 0ae9246b-95e5-4b64-8af1-497f8d50859c · outbound

This paper cites Latent Video Diffusion Models for High-Fidelity Long Video Generation.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Latent Video Diffusion Models for High-Fidelity Long Video Generation

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:37.767663Z

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Observation 51e41cf4-c842-4f87-92c1-65ee836e1363 · outbound

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

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Direct preference optimization: Your language model is secretly a reward model

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:37.880698Z

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Observation f85c2159-b5f2-4342-9289-d78fc463aa0b · outbound

This paper cites Diffusion model alignment using direct preference optimization.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Diffusion model alignment using direct preference optimization

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:37.991042Z

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Observation dccc29e0-216f-4101-955f-9675bc0c0288 · outbound

This paper cites RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment

Reference 32

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Observation f1120756-8f57-4e87-87d8-26b565c7a88d · outbound

This paper cites Using human feedback to fine-tune diffusion models without any reward model.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Using human feedback to fine-tune diffusion models without any reward model

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:38.313805Z

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Observation 7b4eefa4-3d83-4a02-bd26-529ca2228b3a · outbound

This paper cites Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization

Reference 34

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Observation 3c3b4cdf-c412-446d-8908-961cc6afba05 · outbound

This paper cites Flow-DPO: Improving LLM Mathematical Reasoning through Online Multi-Agent Learning.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Flow-DPO: Improving LLM Mathematical Reasoning through Online Multi-Agent Learning

Reference 35

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Observation 02ad75a6-c0ce-472d-bfb6-7746a0a3eab8 · outbound

This paper cites GAPO: Learning Preferential Prompt through Generative Adversarial Policy Optimization.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning GAPO: Learning Preferential Prompt through Generative Adversarial Policy Optimization

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:38.537856Z

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Observation 8ec20fa5-d281-453b-a4d9-ae0d45104786 · outbound

This paper cites Proximal Policy Optimization Algorithms.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Proximal Policy Optimization Algorithms

Reference 37

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Observation d04ec081-92e6-4d52-94e4-50207e25cac3 · outbound

This paper cites Training Diffusion Models with Reinforcement Learning.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Training Diffusion Models with Reinforcement Learning

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:38.635088Z

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Observation 3a51d4ba-008c-4158-a518-bddc22a7c095 · outbound

This paper cites Reinforcement learning for fine- tuning text-to-image diffusion models.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Reinforcement learning for fine- tuning text-to-image diffusion models

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:29:40.534519Z

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Observation 43407ae1-ff48-4df2-ac02-5355ae1e2834 · outbound

This paper cites Structure and content-guided video synthesis with diffusion models.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Structure and content-guided video synthesis with diffusion models

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:38.809581Z

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Observation 677b00e3-e171-4d88-b6b8-1faa3eb467ad · outbound

This paper cites Video generation models as world simulators.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Video generation models as world simulators

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:38.937918Z

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Observation 502f77ae-30b4-4e8d-bb6f-98d28247526f · outbound

This paper cites Cotracker: It is better to track together.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Cotracker: It is better to track together

Reference 42

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Source-reported events for the cited work

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Observation 4965863d-13f6-46cf-b683-aba510ec6ae5 · outbound

This paper cites Video generation models as world simulators, 2024.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Video generation models as world simulators, 2024

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:39.144940Z

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Observation 3684ae8c-c882-4b4f-9e96-83db357a0df9 · outbound

This paper cites Kling ai.https://klingai.kuaishou.com/, 2024.06.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Kling ai.https://klingai.kuaishou.com/, 2024.06

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:39.266036Z

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Observation ac7eec90-e1c1-429f-9411-f21d674beb6f · outbound

This paper cites Koala-36M: A Large-scale Video Dataset Improving Consistency between Fine-grained Conditions and Video Content.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Koala-36M: A Large-scale Video Dataset Improving Consistency between Fine-grained Conditions and Video Content

Reference 45

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Observation be29dfc3-72d3-426d-bcdf-c9f4e5149870 · outbound

This paper cites VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T04:29:39.531704Z

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Observation 0b02af15-157f-4dd9-aae1-4c1d4d0dc9f4 · outbound

This paper cites Qwen2.5 Technical Report.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning Qwen2.5 Technical Report

Reference 47

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Observation 28c45976-fa18-49ff-ad2e-50198112175c · outbound

This paper cites LLaVA-Video: Video Instruction Tuning With Synthetic Data.

GigaVideo-1: Advancing Video Generation via Automatic Feedback with 4 GPU-Hours Fine-Tuning LLaVA-Video: Video Instruction Tuning With Synthetic Data

Reference 48

Resolution: malformed identifier; no resolver link, observed 2026-08-07T04:29:39.770200Z

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Pith citing papers

No inbound Pith citation observations are available.