Pith. sign in

Paper Citation Record · LEDGER

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

As of 9 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

Resolution
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=sha256:60b176365ddaf33f2fefc14ce9d88f9d1c2745813362014f21489d894a008b1c

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

Resolution
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=sha256:9c6eb45fa98ce22f2c9ff5b56bea7a25ea64ad43f4ac418d1bcce65fbd4185a7

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

Resolution
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=sha256:256b09dcaa312f6958f5c920fc9d71635a4e3c042b45c3d5ea635f367c42cd03

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

Resolution
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=sha256:915f7cdef9c28efec01e301b8e91c56b20cee9729a25f991b374741d98ede182

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

Resolution
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:ffa4a26564ddeefc061721e25d850dd6b5a343a4c68d2b9595ecbcf3e4f1c505

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=sha256:5559bda23423ecf21d7b0a82cdd2c1efb0f5472a8756a4bea425a4486f477a2f

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:de61f1e1791a5b608b8c5e332aaa8b54ce9d7f21bbacc740601453f9888bca65

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=sha256:2392afdc00823a60c76d1318e2f18248d5447578cbdff53abd435550ce7db73d

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=sha256:711bccbaefd880ad6b54ee08a6ff07a3101eaec2ded95292f3daecfb93a1805c

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

Resolution
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:d559f707b886fde0904d5f4ea67e47ac4edffcc49d76fd65bbe4ef4dbeb4b9da

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=sha256:6c28b2e8caa3486394f59c14a1d30814471808381bbca4090220e2c5da88b792

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=sha256:4a21009948795c1ef9b1301054bdc7b49e5caa7884c487fd4ee1912ff65d60b7

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=sha256:291df6a9860841a5ebb7cca966f2f60496c19ffdb8f848ab305cfa3f6f0130c8

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

Resolution
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:f61ec79928aa1c329bbf456b952ed4a98507820e4ece77eabcab55c9c9721625

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:df144b84d74c4fa2d8dffbaa38a7916a30490b8ccee76f6faa3c824bef8841e0

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=sha256:51196eecc4f5622e3ebce9cad65d29bae2b6aa0c5069638e602e8542b7226a26