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Paper Citation Record · LEDGER

Refining Alignment Framework for Diffusion Models with Intermediate-Step Preference Ranking

As of 10 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 3 inbound Pith citation observations for arXiv:2502.01667.

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

pith.paper-citation-record.v1
2502.01667 v1

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T18:55:14.749369Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T04:36:13.562345Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T02:11:15.563841Z

Reference resolution

13 of 13 outbound references displayed

  • verified exact0
  • verified fuzzy2
  • unresolved11
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 945dad9d-c1d5-4b65-8b50-7656528e41fd · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Refining Alignment Framework for Diffusion Models with Intermediate-Step Preference Ranking Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 1

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unresolved
no resolver link, observed 2026-08-09T18:55:14.698903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T18:55:14.698903Z digest=sha256:b2b8e6b0f1269f5d64fcbdc097c0dbf6532dd76fdf9721b41bd4808b5e8f14c7

Observation 433a15ec-9943-498d-92e6-5ffe794d4747 · outbound

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

Refining Alignment Framework for Diffusion Models with Intermediate-Step Preference Ranking Aligning Text-to-Image Models using Human Feedback

Reference 5

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unresolved
no resolver link, observed 2026-08-09T18:55:14.716939Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T18:55:14.716939Z digest=sha256:6a188cd7c65a47d230c27bf5ca5a8a363e59910bac729eed993d33b934b6edd9

Observation 7ff31df5-0330-4e06-81aa-130ba46954b0 · outbound

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

Refining Alignment Framework for Diffusion Models with Intermediate-Step Preference Ranking Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization

Reference 6

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unresolved
no resolver link, observed 2026-08-09T18:55:14.721267Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T18:55:14.721267Z digest=sha256:3a7bc24e0171ccdc2965c51258c0b371b89279a48be3d3b6660915c03b080774

Observation a1b31773-2c03-4617-b886-7966a2aa280c · outbound

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

Refining Alignment Framework for Diffusion Models with Intermediate-Step Preference Ranking Aligning Text-to-Image Diffusion Models with Reward Backpropagation

Reference 8

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unresolved
no resolver link, observed 2026-08-09T18:55:14.729814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T18:55:14.729814Z digest=sha256:06c858594b41ff1bb2db8f72e69851694805697ad1250c209b45ca7feb292a51

Observation 840a7906-fad3-44d9-ad91-31b0138088e7 · outbound

This paper cites Defining and Characterizing Reward Hacking.

Refining Alignment Framework for Diffusion Models with Intermediate-Step Preference Ranking Defining and Characterizing Reward Hacking

Reference 9

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unresolved
no resolver link, observed 2026-08-09T18:55:14.733622Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T18:55:14.733622Z digest=sha256:de23108a912ba2def0ba3e4dea8aae1042f331c17013f159963ff8761bb25d80

Observation 8d6b33bf-3fec-43d2-9bf1-692c3fee2721 · outbound

This paper cites $\beta$-DPO: Direct Preference Optimization with Dynamic $\beta$.

Refining Alignment Framework for Diffusion Models with Intermediate-Step Preference Ranking $\beta$-DPO: Direct Preference Optimization with Dynamic $\beta$

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-09T18:55:14.737534Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T18:55:14.737534Z digest=sha256:668ed0f276d03c67210ba852b4545896b2c37860420da61fb32fde80840b1f5c

Observation 858e01d3-37e6-4493-bec4-79eece078925 · outbound

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

Refining Alignment Framework for Diffusion Models with Intermediate-Step Preference Ranking Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-09T18:55:14.741389Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T18:55:14.741389Z digest=sha256:f259c410c49f280e65a15ee464b59da9ff6bbc3b247825d6c31bea4d27c496dc

Observation 2f47b8ec-6f15-45c8-b224-56e9f7321be9 · outbound

This paper cites draw” for them. Otherwise, they should label each image with a “win.

Refining Alignment Framework for Diffusion Models with Intermediate-Step Preference Ranking draw” for them. Otherwise, they should label each image with a “win

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T18:55:14.905671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T18:55:14.745241Z digest=sha256:b11af22663a01bde510f5ea5370ea8a48016a6003d507a9613e890be134f1f76

Observation 8c1abe16-c2d0-459c-b0f3-3b5318f0e260 · outbound

This paper cites The prompts are from the Pick-a-Pic dataset.

Refining Alignment Framework for Diffusion Models with Intermediate-Step Preference Ranking The prompts are from the Pick-a-Pic dataset

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T18:55:14.893465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T18:55:14.749369Z digest=sha256:52c921c38ee42c2a4306b7c88d4373c9ac8dd6cc47540ffa130a67dd4b1e10f2

Observation 71ee1119-9219-45c5-8cba-044b0e8ef46a · outbound

This paper cites Imagen Video: High Definition Video Generation with Diffusion Models.

Refining Alignment Framework for Diffusion Models with Intermediate-Step Preference Ranking Imagen Video: High Definition Video Generation with Diffusion Models

Reference 2020

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unresolved
no resolver link, observed 2026-08-09T18:55:14.712504Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T18:55:14.712504Z digest=sha256:689ae4e1c5be6d0302a7a61028fa31203d7266126aace32c254e860c3d9c94b0

Observation d1a7150b-1240-4cd3-9283-f6c68d7e2d29 · outbound

This paper cites GPT-4 Technical Report.

Refining Alignment Framework for Diffusion Models with Intermediate-Step Preference Ranking GPT-4 Technical Report

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-09T18:55:14.725815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T18:55:14.725815Z digest=sha256:c4a092da961d8333cdf651621de382f31b2228df3d0070c74c5479f341914896

Observation d07a524e-7ca7-4b88-af9b-401b38a86a46 · outbound

This paper cites Gradient Guidance for Diffusion Models: An Optimization Perspective.

Refining Alignment Framework for Diffusion Models with Intermediate-Step Preference Ranking Gradient Guidance for Diffusion Models: An Optimization Perspective

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-09T18:55:14.703873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T18:55:14.703873Z digest=sha256:7a603282ac4bf249665cc56ef14539668a128606be55b37a7599044e4538e0d6

Observation c54edfc0-f631-4b00-aa97-f5859962cff2 · outbound

This paper cites Classifier-Free Diffusion Guidance.

Refining Alignment Framework for Diffusion Models with Intermediate-Step Preference Ranking Classifier-Free Diffusion Guidance

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-09T18:55:14.708261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T18:55:14.708261Z digest=sha256:1dbccdd66cc684b1b026e5722e71f3dc4f6d34b2395305c31b453674a293a53b

Pith citing papers

Observation 4db1fb50-de6b-458a-9aae-e8c32b57898e · inbound

Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion cites this paper.

Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion Refining Alignment Framework for Diffusion Models with Intermediate-Step Preference Ranking

Reference 46

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unresolved
no resolver link, observed 2026-08-06T04:36:13.562345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:36:13.562345Z digest=sha256:345b4c369e9acf71d1ae94975061739535bc65b148863d85949c23ac40b8ae35

Observation 6cb0ffd8-16d6-4209-9e19-775b9fdf14bf · inbound

Threshold-Guided Optimization for Visual Generative Models cites this paper.

Threshold-Guided Optimization for Visual Generative Models Refining Alignment Framework for Diffusion Models with Intermediate-Step Preference Ranking

Reference 47

Resolution
verified exact
arxiv_id, observed 2026-05-11T17:46:07.962171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-08T17:14:36.632493Z digest=sha256:759133f73590259deecc0b6874e01f898742fec6f613688b64576618bf1a8046

Observation 4b007bb3-661e-42ef-8b3a-00ded0a14f6c · inbound

Offline Preference Optimization for Rectified Flow with Noise-Tracked Pairs cites this paper.

Offline Preference Optimization for Rectified Flow with Noise-Tracked Pairs Refining Alignment Framework for Diffusion Models with Intermediate-Step Preference Ranking

Reference 35

Resolution
verified exact
arxiv_id, observed 2026-05-12T02:11:15.565659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-12T02:10:27.595446Z digest=sha256:ca52ad2f9cc774bc007f3dbe4d998a9b0ff9f4d79e48be5b51d9da3e43c768bb