Paper Citation Record · LEDGER

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference

As of 20 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 9 inbound Pith citation observations for arXiv:2509.06942.

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

pith.paper-citation-record.v1
2509.06942 v3

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T22:58:08.865854Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T14:42:14.456588Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T18:14:59.829960Z

Reference resolution

37 of 37 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 52070015-3c8d-4ab5-a1af-f88d128c5838 · outbound

This paper cites com / discus0434/aesthetic-predictor-v2-5, 2025.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference com / discus0434/aesthetic-predictor-v2-5, 2025

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:58:09.198025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 937e5ece-ff4a-48be-acc5-38fc07c01ef5 · outbound

This paper cites Stochastic Interpolants: A Unifying Framework for Flows and Diffusions.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Stochastic Interpolants: A Unifying Framework for Flows and Diffusions

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T22:58:08.757460Z

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Observation dde1f6ee-f58c-4ec4-829d-5167820bbb4f · outbound

This paper cites Enhancing Reward Models for High-quality Image Generation: Beyond Text-Image Alignment.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Enhancing Reward Models for High-quality Image Generation: Beyond Text-Image Alignment

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T22:58:08.761058Z

Source-reported events for the cited work

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Observation e1241b49-4de3-4534-9528-cc47770100df · outbound

This paper cites Training Diffusion Models with Reinforcement Learning.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Training Diffusion Models with Reinforcement Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T22:58:08.764401Z

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Observation dc7c3733-7051-454a-926e-41d618b2efd2 · outbound

This paper cites Directly Fine-Tuning Diffusion Models on Differentiable Rewards.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Directly Fine-Tuning Diffusion Models on Differentiable Rewards

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T22:58:08.767666Z

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Observation 2411b54e-52c0-4eb5-ac85-12e3d374c44f · outbound

This paper cites Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T22:58:08.771147Z

Source-reported events for the cited work

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Observation e48e195f-45f6-486e-89d3-81e54981447b · outbound

This paper cites Optimizing DDPM Sampling with Shortcut Fine-Tuning.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Optimizing DDPM Sampling with Shortcut Fine-Tuning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T22:58:08.774404Z

Source-reported events for the cited work

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Observation f589bae5-fdda-4c81-a1c6-1ffd31060a87 · outbound

This paper cites Dpok: Reinforcement learning for fine-tuning text-to-image diffu- sion models.Advances in Neural Information Processing Systems, 36:79858–79885, 2023.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Dpok: Reinforcement learning for fine-tuning text-to-image diffu- sion models.Advances in Neural Information Processing Systems, 36:79858–79885, 2023

Reference 8

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Observation bb0f0370-442b-4d34-a567-954c21fd7969 · outbound

This paper cites Geneval: An object-focused framework for evaluating text- to-image alignment.Advances in Neural Information Pro- cessing Systems, 36:52132–52152, 2023.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Geneval: An object-focused framework for evaluating text- to-image alignment.Advances in Neural Information Pro- cessing Systems, 36:52132–52152, 2023

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:58:09.183500Z

Source-reported events for the cited work

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Observation 4269ba9b-8c0a-4483-b8fd-70af9a9ea9e6 · outbound

This paper cites Classifier-Free Diffusion Guidance.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Classifier-Free Diffusion Guidance

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T22:58:08.783694Z

Source-reported events for the cited work

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Observation 8b99b031-d8cb-4512-9ddd-c019e57a7870 · outbound

This paper cites Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

Reference 11

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Observation 90e7f48e-887d-42a6-ab7b-44768cc3bb14 · outbound

This paper cites Pick-a-pic: An open dataset of user preferences for text-to-image generation.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Pick-a-pic: An open dataset of user preferences for text-to-image generation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:58:09.168349Z

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Observation 7d0ff485-5249-493a-b495-b1a3cb9de8d6 · outbound

This paper cites Flux.https://github.com/ black-forest-labs/flux, 2024.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Flux.https://github.com/ black-forest-labs/flux, 2024

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:58:09.158617Z

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Observation 4611180b-1710-4ecf-bcb3-445a55c2831f · outbound

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

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Aligning Text-to-Image Models using Human Feedback

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T22:58:08.795495Z

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Observation 91ccb4a6-a8a6-4951-b6df-9aa612c6462d · outbound

This paper cites Flux.1 krea [dev].https://github.com/krea-ai/flux- krea, 2025.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Flux.1 krea [dev].https://github.com/krea-ai/flux- krea, 2025

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:58:09.149435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2aea1fa6-9b79-49d5-af67-7cdfd5a622c4 · outbound

This paper cites Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T22:58:08.801305Z

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Observation 2a38496e-c1c6-4958-b816-bbf7733a7eca · outbound

This paper cites Aes- thetic post-training diffusion models from generic prefer- ences with step-by-step preference optimization.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Aes- thetic post-training diffusion models from generic prefer- ences with step-by-step preference optimization

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:58:09.140049Z

Source-reported events for the cited work

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Observation 7591b143-3683-43f6-b2ab-59b3d5465bf7 · outbound

This paper cites Flow Matching for Generative Modeling.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Flow Matching for Generative Modeling

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T22:58:08.807313Z

Source-reported events for the cited work

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Observation 9a6dc78c-e6a1-4063-a01f-2df826adbc25 · outbound

This paper cites Flow-GRPO: Training Flow Matching Models via Online RL.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Flow-GRPO: Training Flow Matching Models via Online RL

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T22:58:08.810494Z

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Observation 5f6d553e-f857-401a-827c-1b250f7dd7cd · outbound

This paper cites Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T22:58:08.813669Z

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Observation 25000ad8-be8a-4dff-be0e-cc99ea05a220 · outbound

This paper cites Sit: Explor- ing flow and diffusion-based generative models with scalable interpolant transformers.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Sit: Explor- ing flow and diffusion-based generative models with scalable interpolant transformers

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:58:09.130470Z

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Observation 5134029c-8262-4629-9416-3e3bcc9275c4 · outbound

This paper cites HPSv3: Towards Wide-Spectrum Human Preference Score.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference HPSv3: Towards Wide-Spectrum Human Preference Score

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T22:58:08.819616Z

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Observation 9f810d7d-4446-4519-a6b0-2f31a88963f6 · outbound

This paper cites The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models

Reference 23

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Observation b0a10205-d2cf-419a-ae24-971bee4740a6 · outbound

This paper cites Aligning text-to-image diffusion models with reward backpropagation.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Aligning text-to-image diffusion models with reward backpropagation

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:58:09.120814Z

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Observation 4193fa4e-2e0f-43f8-b61f-d6714ea31c85 · outbound

This paper cites Video Diffusion Alignment via Reward Gradients.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Video Diffusion Alignment via Reward Gradients

Reference 25

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Observation 180c8dc2-073e-4b28-bd09-c7620c33f9f5 · outbound

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

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Learning transferable visual models from natural language supervi- sion

Reference 26

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Observation 2b34f3f7-57b5-4fac-a0dc-f429582478a7 · outbound

This paper cites Laion-aesthetics.https : / / laion.ai/blog/laion- aesthetics/, 2022.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Laion-aesthetics.https : / / laion.ai/blog/laion- aesthetics/, 2022

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:58:09.106260Z

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Observation f702561d-4610-46cb-b88f-fe00408b790c · outbound

This paper cites Denoising Diffusion Implicit Models.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Denoising Diffusion Implicit Models

Reference 28

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Observation fffbbe72-75d6-4031-a142-e7a511c6efdb · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Score-Based Generative Modeling through Stochastic Differential Equations

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T22:58:08.841022Z

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Observation 009cc442-5440-4708-b446-54d7a68e7e11 · outbound

This paper cites Pref-GRPO: Pairwise Preference Reward-based GRPO for Stable Text-to-Image Reinforcement Learning.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Pref-GRPO: Pairwise Preference Reward-based GRPO for Stable Text-to-Image Reinforcement Learning

Reference 30

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Observation c1cba328-40a4-45ef-9b35-ef8d610369e5 · outbound

This paper cites DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-04T22:58:08.847327Z

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Observation 5afdee4f-8697-4eb0-bbb2-bfe977d7516e · outbound

This paper cites Human preference score: Better aligning text- to-image models with human preference.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Human preference score: Better aligning text- to-image models with human preference

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:58:09.096797Z

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Observation 10982d8d-9e14-4341-bbfc-9049c883b985 · outbound

This paper cites Imagere- ward: Learning and evaluating human preferences for text- to-image generation.Advances in Neural Information Pro- cessing Systems, 36:15903–15935, 2023.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Imagere- ward: Learning and evaluating human preferences for text- to-image generation.Advances in Neural Information Pro- cessing Systems, 36:15903–15935, 2023

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:58:09.086823Z

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Observation b31f2086-0538-4936-8c03-06d9442abf05 · outbound

This paper cites DanceGRPO: Unleashing GRPO on Visual Generation.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference DanceGRPO: Unleashing GRPO on Visual Generation

Reference 34

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Observation c5448a8f-781c-4075-b2f7-9a8baaa2d00c · outbound

This paper cites Teaching large language models to regress accurate image quality scores using score distribution.arXiv preprint arXiv:2501.11561, 2025.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Teaching large language models to regress accurate image quality scores using score distribution.arXiv preprint arXiv:2501.11561, 2025

Reference 35

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This paper cites Learning multi- dimensional human preference for text-to-image generation.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Learning multi- dimensional human preference for text-to-image generation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:58:09.076792Z

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Observation 2599237f-f1be-4309-9df1-f3a845038047 · outbound

This paper cites Diffusion model as a noise-aware latent reward model for step-level preference optimization.arXiv preprint arXiv:2502.01051, 2025.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Diffusion model as a noise-aware latent reward model for step-level preference optimization.arXiv preprint arXiv:2502.01051, 2025

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-04T22:58:08.865854Z

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Pith citing papers

Observation 050f2db6-1155-40b3-addd-b4a38dca59bf · inbound

HunyuanImage 3.0 Technical Report cites this paper.

HunyuanImage 3.0 Technical Report Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference

Reference 42

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Observation 75ffebf9-39d7-455f-a228-277a91f4a04c · inbound

HunyuanImage 3.0 Technical Report cites this paper.

HunyuanImage 3.0 Technical Report Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference

Reference 41

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Observation 36adf26e-5a56-4acb-b6b1-11c20e671a27 · inbound

Distribution Matching Distillation Meets Reinforcement Learning cites this paper.

Distribution Matching Distillation Meets Reinforcement Learning Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference

Reference 47

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Observation 5e2f6747-0baf-421d-ace6-06ded4e6f846 · inbound

TAGRPO: Boosting GRPO on Image-to-Video Generation with Direct Trajectory Alignment cites this paper.

TAGRPO: Boosting GRPO on Image-to-Video Generation with Direct Trajectory Alignment Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference

Reference 19

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Observation 3226be7c-d242-46f4-a027-d3641c51b797 · inbound

FAIL: Flow Matching Adversarial Imitation Learning for Image Generation cites this paper.

FAIL: Flow Matching Adversarial Imitation Learning for Image Generation Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-02T23:59:17.056636Z

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Observation 8cda3702-eab3-4ec1-be36-9373d8cc1d2d · inbound

LeapAlign: Post-Training Flow Matching Models at Any Generation Step by Building Two-Step Trajectories cites this paper.

LeapAlign: Post-Training Flow Matching Models at Any Generation Step by Building Two-Step Trajectories Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:25:18.802132Z

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Observation d5fa8438-4f51-41fb-a518-a42ffbd777e5 · inbound

When Preference Labels Fall Short: Aligning Diffusion Models from Real Data cites this paper.

When Preference Labels Fall Short: Aligning Diffusion Models from Real Data Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-20T06:13:05.225166Z

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Observation 469810ea-6acd-4b6d-b676-8de9a8b02b7b · inbound

When Preference Labels Fall Short: Aligning Diffusion Models from Real Data cites this paper.

When Preference Labels Fall Short: Aligning Diffusion Models from Real Data Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-06-30T18:14:59.831627Z

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Observation 81f85514-ed9c-4283-a6e2-1aa71b2e527e · inbound

WearWow: Native 2K Multi-Garment Virtual Try-On via Adaptive Token Packing and Preference Alignment cites this paper.

WearWow: Native 2K Multi-Garment Virtual Try-On via Adaptive Token Packing and Preference Alignment Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference

Reference 36

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