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Paper Citation Record · LEDGER

ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models

As of 16 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 3 inbound Pith citation observations for arXiv:2505.22569.

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

pith.paper-citation-record.v1
2505.22569 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:09:42.705283Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T17:31:29.288841Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T11:25:18.848495Z

Reference resolution

28 of 28 outbound references displayed

  • verified exact4
  • verified fuzzy0
  • unresolved24
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 167124be-871d-4a49-ab41-ee63566ecec6 · outbound

This paper cites StyleDomain: Efficient and Lightweight Parameterizations of StyleGAN for One-shot and Few-shot Domain Adaptation.

ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models StyleDomain: Efficient and Lightweight Parameterizations of StyleGAN for One-shot and Few-shot Domain Adaptation

Reference 1

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local_arxiv, observed 2026-08-07T13:09:43.930210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T13:09:38.808859Z digest=sha256:025198ad578fa2b49a78c76086c109d907ae0df5f63b0e15a8f831a25707c14f

Observation 62206f9d-5fca-4f9a-b2be-22aebcdb4efe · outbound

This paper cites Training Diffusion Models with Reinforcement Learning.

ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models Training Diffusion Models with Reinforcement Learning

Reference 2

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source=pdf_text observed=2026-08-07T13:09:38.900936Z digest=sha256:b93a8fb1404c931eb0bf57292f2bd2d08e9aec482a379354b519376844986c5a

Observation e97a62ef-f53d-4718-b424-95fa3303182b · outbound

This paper cites Perception Prioritized Training of Diffusion Models.

ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models Perception Prioritized Training of Diffusion Models

Reference 3

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source=pdf_text observed=2026-08-07T13:09:39.017526Z digest=sha256:fd0bb8e5bdc8962976bdaa670a189ddc61c47246d1aadb60dc5a8f232c87e6c8

Observation 024e6c13-5deb-4512-8498-b6b823e44568 · outbound

This paper cites Deep reinforcement learning from human preferences.

ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models Deep reinforcement learning from human preferences

Reference 4

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source=pdf_text observed=2026-08-07T13:09:39.093603Z digest=sha256:7a6ccd5818290de738e1f9cec02fae476cfe6c9da48462cf6ddaa49fd2e925cb

Observation eecca166-7183-4191-85f1-e62cc002b094 · outbound

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

ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models Directly Fine-Tuning Diffusion Models on Differentiable Rewards

Reference 5

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source=pdf_text observed=2026-08-07T13:09:39.182877Z digest=sha256:01b1e36814e6a1c0c7358c4725c0aaa7c930d77ab797422c006d799e95dbd6fa

Observation be7f0c9f-c289-4616-bc49-2edd4a258ccd · outbound

This paper cites On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models.

ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models

Reference 6

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local_arxiv, observed 2026-08-07T13:09:43.683245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T13:09:39.305004Z digest=sha256:83c3565cb4beca02698c1ef7ee9b8f676dd811d3bec2a37ecf09c188af8d2349

Observation 616719d4-0d12-4f10-8884-96ef2090df82 · outbound

This paper cites DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models.

ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models

Reference 7

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source=pdf_text observed=2026-08-07T13:09:39.445251Z digest=sha256:893de02c36ca0abec0188da2c12aa43702a4b41c0ab2a0a9cef46e5f27129788

Observation 83cecc1d-85da-4330-b5a6-49b285c00c7a · outbound

This paper cites GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium.

ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

Reference 8

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source=pdf_text observed=2026-08-07T13:09:39.597425Z digest=sha256:93111b2e5fd00c2e2502a912bd17261e329ea7a4a1628e3e1c9a90e919a770c1

Observation b54c7ee6-d908-45e5-9eca-8988628ef0e9 · outbound

This paper cites Denoising Diffusion Probabilistic Models.

ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models Denoising Diffusion Probabilistic Models

Reference 9

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source=pdf_text observed=2026-08-07T13:09:39.753533Z digest=sha256:63e103a2cbfdc38f236a7e2f046119b8a243b56fc2bce55130e425acb462612f

Observation fbfd8839-5b1f-4565-86e7-febc76218eed · outbound

This paper cites Elucidating Optimal Reward-Diversity Tradeoffs in Text-to-Image Diffusion Models.

ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models Elucidating Optimal Reward-Diversity Tradeoffs in Text-to-Image Diffusion Models

Reference 10

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local_arxiv, observed 2026-08-07T13:09:43.442048Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T13:09:39.926132Z digest=sha256:8803861d369c65dd865dc851f005cb9a5c7b05cb03a4b5dfe0098c85a99433c4

Observation 5f80c253-a45c-4e0c-a294-3c936dc46ab5 · outbound

This paper cites Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation.

ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation

Reference 11

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source=pdf_text observed=2026-08-07T13:09:40.062546Z digest=sha256:42d5de634b165118a0902e1d409a77a6ebe9bc46a9fd93b9925ba10936a82a93

Observation 5c4bb27f-0cfe-4b52-9382-bcb7a4776523 · outbound

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

ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization

Reference 12

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source=pdf_text observed=2026-08-07T13:09:40.253118Z digest=sha256:89af26cc67aef12dcc3d7354e55dac6cc7ab07a2d97b901a639b2fad856d2d67

Observation 0fa83eac-7e56-456b-9a9f-1a63649cd974 · outbound

This paper cites Microsoft COCO: Common Objects in Context.

ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models Microsoft COCO: Common Objects in Context

Reference 13

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source=pdf_text observed=2026-08-07T13:09:40.431444Z digest=sha256:1add1139893dce40bde00579f1d1fcc6f7e65b8c3b6d5e14cc121bcbd1dbe821

Observation 5c93ee90-c882-402d-9d36-cb95fd48fce3 · outbound

This paper cites Training language models to follow instructions with human feedback.

ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models Training language models to follow instructions with human feedback

Reference 15

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source=pdf_text observed=2026-08-07T13:09:40.759914Z digest=sha256:8b3b3a8a3e42bff94460e0295800d5bb3c58ae961872c572e79970aa3c87429c

Observation 965d89f6-03d8-410a-be39-57d56b62377d · outbound

This paper cites Aligning Text-to-Image Diffusion Models with Reward Backpropagation.

ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models Aligning Text-to-Image Diffusion Models with Reward Backpropagation

Reference 16

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source=pdf_text observed=2026-08-07T13:09:40.872626Z digest=sha256:2aca471b19d9adb828db98103f18ba73018ddc1787177ef4e4c3e6d4807df884

Observation 5d7968ad-b8b3-41cf-9aa1-fa13d8d628aa · outbound

This paper cites Learning transferable visual models from natural language supervision.

ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models Learning transferable visual models from natural language supervision

Reference 17

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source=pdf_text observed=2026-08-07T13:09:40.986713Z digest=sha256:5f06a492728944c04e030648ae67b94780c0c640005880651132f54f9744e0dc

Observation 1de87429-f516-417d-b72c-00afc2eaa802 · outbound

This paper cites Direct Preference Optimization: Your Language Model is Secretly a Reward Model.

ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Reference 18

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source=pdf_text observed=2026-08-07T13:09:41.106661Z digest=sha256:9b4bd4e4116e2dae303d58d14a094c1362475f943bb144bc0072b23d5fe53b2d

Observation 65da230f-d7f1-475f-ab15-5b8a2ffb7e8d · outbound

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

ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models High- resolution image synthesis with latent diffusion models

Reference 19

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source=pdf_text observed=2026-08-07T13:09:41.257715Z digest=sha256:3251e448c5060be97444926ac9f5d85375772fe61bf01332ff61e888ad3d4188

Observation 020be9df-d054-41f0-94ec-b8a5c6e01e3d · outbound

This paper cites High-Resolution Image Synthesis with Latent Diffusion Models.

ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models High-Resolution Image Synthesis with Latent Diffusion Models

Reference 20

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source=pdf_text observed=2026-08-07T13:09:41.423996Z digest=sha256:f15aa86b4039c400cc1f51ded14c78329b06aabfdb10c6ad32309aa7a2218a18

Observation ab48ca54-d7c7-4011-bfe9-7803fafc4073 · outbound

This paper cites Diffusion Model Alignment Using Direct Preference Optimization.

ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models Diffusion Model Alignment Using Direct Preference Optimization

Reference 21

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source=pdf_text observed=2026-08-07T13:09:41.558170Z digest=sha256:e8ca660b6c59faab5480abfaf396453230ffbc02b6eb0763196ddfbb903f6544

Observation fb5a4686-f018-4541-9955-97dba92637ca · outbound

This paper cites Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis.

ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

Reference 22

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source=pdf_text observed=2026-08-07T13:09:41.692876Z digest=sha256:384eccf7d99269088458e4e9205e08dbf97cb72f14651a62bcc36e6edf3ad92c

Observation 2bcb3b35-42b2-49cc-ab95-84eb6b973265 · outbound

This paper cites Deep Reward Supervisions for Tuning Text-to-Image Diffusion Models.

ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models Deep Reward Supervisions for Tuning Text-to-Image Diffusion Models

Reference 23

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source=pdf_text observed=2026-08-07T13:09:41.847472Z digest=sha256:fb055397e8c8cc4e56d6f9198352b8a07fb81c266e8f292a2243674d4dcd7e94

Observation 238656fc-7690-47d6-9f9e-1a3064e5cf7a · outbound

This paper cites ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation.

ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation

Reference 24

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source=pdf_text observed=2026-08-07T13:09:41.984003Z digest=sha256:33dfcf6b346e3a3e86763effacec7649a764bfb994814a043fe66b8300b371c3

Observation 6e1cc80d-a992-4490-a4c3-971f50350213 · outbound

This paper cites DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection.

ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection

Reference 25

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source=pdf_text observed=2026-08-07T13:09:42.140420Z digest=sha256:f5eab3ebf72d82ccaf91ca061a24ac681d46b0a6e1a6c3f4af1d5b89c8c40c6e

Observation 9e6a89ab-7a6c-4720-9514-279507aafc5a · outbound

This paper cites Learning Multi-dimensional Human Preference for Text-to-Image Generation.

ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models Learning Multi-dimensional Human Preference for Text-to-Image Generation

Reference 26

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source=pdf_text observed=2026-08-07T13:09:42.325218Z digest=sha256:d002332200499bc92d84ba1e3449c10f0b3c80be984d9169c89a6cd8010f6118

Observation cde418e1-e2b4-4078-8082-0bda8378055e · outbound

This paper cites an unresolved cited work.

ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models Unresolved cited work

Reference 27

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 62fd0dc0-5a4a-49dd-98b6-a4fc35589412 · outbound

This paper cites an unresolved cited work.

ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models Unresolved cited work

Reference 28

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 26611d37-6550-4ad4-a1b4-97958a9c01ba · outbound

This paper cites For each question, annotators could choose between three options: preference for the first row, preference for the second row, or no preference.

ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models For each question, annotators could choose between three options: preference for the first row, preference for the second row, or no preference

Reference 29

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T13:09:42.705283Z digest=sha256:f19dc4ef8d366802f8a93269357abee2f74dab52c875b647fff181cccb1f614b

Pith citing papers

Observation 3c0fd122-5266-4be9-b689-dfe0638dcc24 · inbound

When Models Learn to Ask Why: Adaptive Causal Reasoning for Trustworthy Medical Vision-Language Models cites this paper.

When Models Learn to Ask Why: Adaptive Causal Reasoning for Trustworthy Medical Vision-Language Models ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models

Reference 25

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source=pdf_text observed=2026-07-13T19:53:46.276873Z digest=sha256:06151b509387f8b5041d37b613e8fd356b8858136998ccd1ffa5a09bec7479ed

Observation 632374b5-a23c-4888-868a-eb49cd5ea479 · inbound

Calibri: Enhancing Diffusion Transformers via Parameter-Efficient Calibration cites this paper.

Calibri: Enhancing Diffusion Transformers via Parameter-Efficient Calibration ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models

Reference 30

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Observation 7e7fc559-d2ce-4221-9e62-e6f949839328 · 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 ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models

Reference 44

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arxiv_id, observed 2026-05-10T11:25:18.851458Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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