Paper Citation Record · LEDGER

ReNeg: Learning Negative Embedding with Reward Guidance

As of 11 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 1 inbound Pith citation observation for arXiv:2412.19637.

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

pith.paper-citation-record.v1
2412.19637 v3

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T00:13:43.688688Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:52:16.704032Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T14:52:18.299204Z

Reference resolution

41 of 41 outbound references displayed

verified exact0
verified fuzzy21
unresolved20
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a8b80448-5a72-4c8d-9d66-452cc3f2f1a3 · outbound

This paper cites Understanding the Impact of Negative Prompts: When and How Do They Take Effect?.

ReNeg: Learning Negative Embedding with Reward Guidance Understanding the Impact of Negative Prompts: When and How Do They Take Effect?

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T00:13:43.477075Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa69a118-dc78-41aa-8fa2-38f1159269a7 · outbound

This paper cites Training Diffusion Models with Reinforcement Learning.

ReNeg: Learning Negative Embedding with Reward Guidance Training Diffusion Models with Reinforcement Learning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T00:13:43.483205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dda78e18-6acd-48d8-abe7-9fbb1789b6b6 · outbound

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

ReNeg: Learning Negative Embedding with Reward Guidance Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T00:13:43.488877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c08e72a-2de6-47b5-b9da-309af256d076 · outbound

This paper cites BeautifulPrompt: Towards Automatic Prompt Engineering for Text-to-Image Synthesis.

ReNeg: Learning Negative Embedding with Reward Guidance BeautifulPrompt: Towards Automatic Prompt Engineering for Text-to-Image Synthesis

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T00:13:43.494199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 352762e3-1079-49ad-a0b4-dd5465eb611f · outbound

This paper cites https : / / huggingface.

ReNeg: Learning Negative Embedding with Reward Guidance https : / / huggingface

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:13:44.387756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 51218b77-8317-47aa-8d60-053d6883cb76 · outbound

This paper cites Videocrafter2: Overcoming data limitations for high-quality video diffusion models.

ReNeg: Learning Negative Embedding with Reward Guidance Videocrafter2: Overcoming data limitations for high-quality video diffusion models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T00:13:43.508418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21c6cac4-e751-4428-ba36-aba1df85a2a0 · outbound

This paper cites Deep reinforcement learn- ing from human preferences.

ReNeg: Learning Negative Embedding with Reward Guidance Deep reinforcement learn- ing from human preferences

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:13:44.360864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ccdaeeb7-1ff7-46b1-a5d7-1af66651a600 · outbound

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

ReNeg: Learning Negative Embedding with Reward Guidance Directly Fine-Tuning Diffusion Models on Differentiable Rewards

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T00:13:43.519190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0dd1889-7155-4519-ae9b-d62224b82c3c · outbound

This paper cites Improving image syn- thesis with diffusion-negative sampling.

ReNeg: Learning Negative Embedding with Reward Guidance Improving image syn- thesis with diffusion-negative sampling

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:13:44.345220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation af2c6f64-4bbd-4057-89de-5fd6eb80e824 · outbound

This paper cites Diffusion models beat gans on image synthesis.

ReNeg: Learning Negative Embedding with Reward Guidance Diffusion models beat gans on image synthesis

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:13:44.329743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 556452ff-d018-4e74-8828-a394b84a5658 · outbound

This paper cites A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models.

ReNeg: Learning Negative Embedding with Reward Guidance A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T00:13:43.534804Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 590c8878-1224-4813-82af-ac4ecb460ebd · outbound

This paper cites AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning.

ReNeg: Learning Negative Embedding with Reward Guidance AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T00:13:43.540408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ceaabb5b-5aee-46b5-881f-1a7f4f630ecf · outbound

This paper cites Optimizing prompts for text-to-image generation.

ReNeg: Learning Negative Embedding with Reward Guidance Optimizing prompts for text-to-image generation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:13:44.314435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2522048b-114b-4ada-8b58-2db6e4cd04a6 · outbound

This paper cites Classifier-Free Diffusion Guidance.

ReNeg: Learning Negative Embedding with Reward Guidance Classifier-Free Diffusion Guidance

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T00:13:43.551337Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49272171-4e64-4a46-9078-7ef8073276b5 · outbound

This paper cites Denoising diffu- sion probabilistic models.

ReNeg: Learning Negative Embedding with Reward Guidance Denoising diffu- sion probabilistic models

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:13:44.298523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d7275f3d-bffd-419c-a403-8320b38c0059 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

ReNeg: Learning Negative Embedding with Reward Guidance LoRA: Low-Rank Adaptation of Large Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T00:13:43.561260Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d955b3d7-efba-4562-9c36-d11c5f76a073 · outbound

This paper cites VBench: Com- prehensive benchmark suite for video generative models.

ReNeg: Learning Negative Embedding with Reward Guidance VBench: Com- prehensive benchmark suite for video generative models

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:13:44.282368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 507a7a8c-e543-4879-b716-e75d1da8c6b2 · outbound

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

ReNeg: Learning Negative Embedding with Reward Guidance Pick-a-pic: An open dataset of user preferences for text-to-image generation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:13:44.266291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bbce65a5-cef8-4bbd-b143-0f3cc1dfb1ae · outbound

This paper cites Bloom: A 176b-parameter open-access multilingual language model.

ReNeg: Learning Negative Embedding with Reward Guidance Bloom: A 176b-parameter open-access multilingual language model

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:13:44.248866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 15e3e39b-f1df-44b6-b8b1-31e4b62f5b59 · outbound

This paper cites Motrans: Customized motion transfer with text-driven video diffusion models.

ReNeg: Learning Negative Embedding with Reward Guidance Motrans: Customized motion transfer with text-driven video diffusion models

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:13:44.232541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f25dab04-5d2c-4be1-8a91-39914d3eee2f · outbound

This paper cites Textcraftor: Your text encoder can be image quality controller.

ReNeg: Learning Negative Embedding with Reward Guidance Textcraftor: Your text encoder can be image quality controller

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:13:44.215168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0e9bb5d7-1ea2-43e5-9838-a5574f88e0c9 · outbound

This paper cites Decoupled Weight Decay Regularization.

ReNeg: Learning Negative Embedding with Reward Guidance Decoupled Weight Decay Regularization

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T00:13:43.593907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc0c5c96-b03c-40d5-8f27-4d0e6e44e8e9 · outbound

This paper cites Training lan- guage models to follow instructions with human feedback.

ReNeg: Learning Negative Embedding with Reward Guidance Training lan- guage models to follow instructions with human feedback

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:13:44.199213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4b93f70c-b0eb-4a09-89e2-f9a08db984a0 · outbound

This paper cites Language models are unsu- pervised multitask learners.

ReNeg: Learning Negative Embedding with Reward Guidance Language models are unsu- pervised multitask learners

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:13:44.182412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b5dca8ce-262c-466d-9cee-14b9f34d81eb · outbound

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

ReNeg: Learning Negative Embedding with Reward Guidance Direct preference optimization: Your language model is secretly a reward model

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:13:44.164214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ca2d9597-7d66-4bae-84e0-97d5514ef2ac · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

ReNeg: Learning Negative Embedding with Reward Guidance Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T00:13:43.614666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d477d235-e029-4546-a36f-9f5679b29f84 · outbound

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

ReNeg: Learning Negative Embedding with Reward Guidance High-resolution image syn- thesis with latent diffusion models

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:13:44.148308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 224f7bd2-e049-4b40-8171-3f47fecc71a8 · outbound

This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models.

ReNeg: Learning Negative Embedding with Reward Guidance Laion-5b: An open large-scale dataset for training next generation image-text models

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:13:44.129825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7d3b4b5d-a5b4-4531-ba5f-72458d9b1dc0 · outbound

This paper cites Denoising Diffusion Implicit Models.

ReNeg: Learning Negative Embedding with Reward Guidance Denoising Diffusion Implicit Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T00:13:43.629902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67f71b14-64bd-4a97-ad8a-145ccd7f3d2c · outbound

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

ReNeg: Learning Negative Embedding with Reward Guidance Score-Based Generative Modeling through Stochastic Differential Equations

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T00:13:43.634287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 533400d6-004a-4b77-91d3-a66abf2ceabc · outbound

This paper cites Diffusion model align- ment using direct preference optimization.

ReNeg: Learning Negative Embedding with Reward Guidance Diffusion model align- ment using direct preference optimization

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:13:44.112659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 76a3db19-a823-42a9-a326-1c561a05fcb9 · outbound

This paper cites On Discrete Prompt Optimization for Diffusion Models.

ReNeg: Learning Negative Embedding with Reward Guidance On Discrete Prompt Optimization for Diffusion Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T00:13:43.642839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d957a185-b34e-4d60-a271-a07aa32d33c2 · outbound

This paper cites Investigating Prompt Engineering in Diffusion Models.

ReNeg: Learning Negative Embedding with Reward Guidance Investigating Prompt Engineering in Diffusion Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T00:13:43.647701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee2a0264-68bf-47b9-b7a7-95976fc8599a · outbound

This paper cites Stable diffusion 2.0 and the importance of negative prompts for good results, 2022.

ReNeg: Learning Negative Embedding with Reward Guidance Stable diffusion 2.0 and the importance of negative prompts for good results, 2022

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:13:44.095499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 348b8756-4d95-4180-903d-fa64a60313c1 · outbound

This paper cites Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation.

ReNeg: Learning Negative Embedding with Reward Guidance Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-11T00:13:43.657804Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5ef638f-fd0a-4e65-b6bb-e98b08825dca · outbound

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

ReNeg: Learning Negative Embedding with Reward Guidance Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T00:13:43.662477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 525e35fa-fbda-434f-93dc-8941e8c3f434 · outbound

This paper cites Fastcomposer: Tuning-free multi- subject image generation with localized attention.

ReNeg: Learning Negative Embedding with Reward Guidance Fastcomposer: Tuning-free multi- subject image generation with localized attention

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:13:44.066701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3cb413d2-8392-41e0-afbe-206cbb8e4770 · outbound

This paper cites Imagere- ward: Learning and evaluating human preferences for text- to-image generation.

ReNeg: Learning Negative Embedding with Reward Guidance Imagere- ward: Learning and evaluating human preferences for text- to-image generation

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:13:44.048372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b81c66ec-e33c-4b39-9591-18074c91767b · outbound

This paper cites Scaling Autoregressive Models for Content-Rich Text-to-Image Generation.

ReNeg: Learning Negative Embedding with Reward Guidance Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-11T00:13:43.677898Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8030b84-cd87-45e1-9494-09b01e4fc4bb · outbound

This paper cites Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation.

ReNeg: Learning Negative Embedding with Reward Guidance Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-11T00:13:43.683200Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ac36792-18f7-4548-ab40-a46340fe5848 · outbound

This paper cites Adding conditional control to text-to-image diffusion models.

ReNeg: Learning Negative Embedding with Reward Guidance Adding conditional control to text-to-image diffusion models

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:13:44.029983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

Observation 01dcb167-e932-45cc-9985-c688cc3748a5 · inbound

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models cites this paper.

A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models ReNeg: Learning Negative Embedding with Reward Guidance

Reference 2024

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:52:18.369441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.