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Paper Citation Record · LEDGER

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering

As of 18 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2505.23343.

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

pith.paper-citation-record.v1
2505.23343 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:52:52.248851Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

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  • verified fuzzy8
  • unresolved26
  • parse uncertain0
  • malformed identifier0
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External citation measurements

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Outbound references

Observation 1df3eb9d-bf91-44f8-97e0-fe79eb4b1b23 · outbound

This paper cites Training Diffusion Models with Reinforcement Learning.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering Training Diffusion Models with Reinforcement Learning

Reference 1

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Observation 7f5b7c26-06a0-4ade-a142-051c3261adf8 · outbound

This paper cites Inference-Time Alignment of Diffusion Models with Direct Noise Optimization.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering Inference-Time Alignment of Diffusion Models with Direct Noise Optimization

Reference 2

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Observation 0e9731f9-14fc-43e7-8e18-71aaea3e0583 · outbound

This paper cites Denoising diffusion probabilistic models.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering Denoising diffusion probabilistic models

Reference 3

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Observation 11bdecac-2de3-4cbf-a352-858ec6081983 · outbound

This paper cites Denoising diffusion implicit models.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering Denoising diffusion implicit models

Reference 4

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source=pdf_text observed=2026-08-07T12:52:49.178716Z digest=sha256:b439cf70af68619458b43307f24993ef34df25652d68582a593400036c3beb4a

Observation 8cab8c19-e1b7-4722-9e7d-9f38fc6d527f · outbound

This paper cites Generative modeling by estimating gradients of the data distribution.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering Generative modeling by estimating gradients of the data distribution

Reference 5

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Observation 93fb4aeb-2049-4f6a-aa55-fe3335eb9780 · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 6

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Observation 98f3ea66-188d-43f2-a2ad-48229894c180 · outbound

This paper cites Scalable diffusion models with transformers.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering Scalable diffusion models with transformers

Reference 7

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Observation 128c7e82-d56d-48f0-ab08-8bd30b34cdf3 · outbound

This paper cites Diffusion models beat gans on image synthesis.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering Diffusion models beat gans on image synthesis

Reference 8

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source=pdf_text observed=2026-08-07T12:52:49.667706Z digest=sha256:79e6abee8a0ee93d53d75a1229d0bcb86183781f4568636168c8ed316b852a36

Observation 02a27826-6f4d-4693-8d82-42b34f79036d · outbound

This paper cites Wan: Open and Advanced Large-Scale Video Generative Models.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering Wan: Open and Advanced Large-Scale Video Generative Models

Reference 9

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Observation 62aab061-63c3-45d1-965a-701ab300f827 · outbound

This paper cites Viewdiff: 3d-consistent image generation with text-to-image models.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering Viewdiff: 3d-consistent image generation with text-to-image models

Reference 10

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source=pdf_text observed=2026-08-07T12:52:49.853737Z digest=sha256:8bfa15e544d4c31e46915293a34d6939f594ee7debed6e84a0eb0d9810bdc820

Observation 0453a75e-aee5-4801-8208-49dd726ae19e · outbound

This paper cites Sv3d: Novel multi-view synthesis and 3d generation from a single image using latent video diffusion.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering Sv3d: Novel multi-view synthesis and 3d generation from a single image using latent video diffusion

Reference 11

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Observation 091fe385-3e6b-43cf-a0da-eefb3e5aa164 · outbound

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

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering Score-Based Generative Modeling through Stochastic Differential Equations

Reference 12

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Observation 4b7caf33-2061-476c-a2ef-038845b29d0e · outbound

This paper cites Loraclr: Contrastive adaptation for customization of diffusion models.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering Loraclr: Contrastive adaptation for customization of diffusion models

Reference 13

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T12:52:50.145828Z digest=sha256:00f334b8e60b32fb1daed2e983c8436f018d96089cb7d0a14cc1dd4158a77ed7

Observation 7158a3d4-d9f4-48f7-ad89-010d37166bfe · outbound

This paper cites Diffusion model alignment using direct preference optimization.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering Diffusion model alignment using direct preference optimization

Reference 14

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Observation 2b908c4a-6b72-4f4a-b131-857eea1eed88 · outbound

This paper cites RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment

Reference 15

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Observation f9074d29-51f3-437e-ae2c-fe09bfd24a53 · outbound

This paper cites Dpok: Reinforcement learning for fine-tuning text-to-image diffusion models.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering Dpok: Reinforcement learning for fine-tuning text-to-image diffusion models

Reference 16

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Observation b021fcc4-99dd-4dcc-8583-07f039fc35d1 · outbound

This paper cites End-to-end diffusion latent optimization improves classifier guidance.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering End-to-end diffusion latent optimization improves classifier guidance

Reference 17

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T12:52:50.526678Z digest=sha256:eb032974c1396685faadcb96fe11d5120f3dc4ea8411f5c1c817d817441eb488

Observation 47773be4-0f1c-4a81-bff3-bdf9e2c5ece9 · outbound

This paper cites Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps

Reference 18

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Observation 17462dfc-a565-4b60-ac5a-6e1fcfd39ad1 · outbound

This paper cites D-Flow: Differentiating through Flows for Controlled Generation.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering D-Flow: Differentiating through Flows for Controlled Generation

Reference 19

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Observation 218048d4-398a-4daf-931d-bcd04ad7f22a · outbound

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

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering Pick-a-pic: An open dataset of user preferences for text-to-image generation

Reference 20

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Observation 48185646-ca5f-42ac-b507-e47d8c6459ad · outbound

This paper cites Classifier-Free Diffusion Guidance.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering Classifier-Free Diffusion Guidance

Reference 21

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Observation bcd421a6-dc78-49d5-9321-bf3f1b8d25ce · outbound

This paper cites Imagenet: A large- scale hierarchical image database.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering Imagenet: A large- scale hierarchical image database

Reference 22

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Observation 2e98496c-5a03-452a-8ead-4872814fc46f · outbound

This paper cites Geneval: An object-focused framework for evaluating text-to-image alignment.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering Geneval: An object-focused framework for evaluating text-to-image alignment

Reference 23

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Observation bef65c03-03f4-4345-9018-27b09d0f2359 · outbound

This paper cites ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment

Reference 24

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Observation bb60d439-d560-4de3-b934-d4ace2b59239 · outbound

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

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering Laion- 5b: An open large-scale dataset for training next generation image-text models

Reference 25

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source=pdf_text observed=2026-08-07T12:52:51.236657Z digest=sha256:1d01110898e04eca1dd706a8dc7623af64942247802db395355b1730864acec2

Observation a64fa191-1e75-435b-8269-b7c258255add · outbound

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

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

Reference 26

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Observation fa980655-7518-410d-b14b-b24d2592a4a9 · outbound

This paper cites Elucidating the design space of diffusion-based generative models.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering Elucidating the design space of diffusion-based generative models

Reference 27

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Observation a928482e-2037-477b-a9f6-c668d9d1af48 · outbound

This paper cites Guiding a diffusion model with a bad version of itself.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering Guiding a diffusion model with a bad version of itself

Reference 28

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Observation 7280384e-2cb9-4837-971c-8401f7341894 · outbound

This paper cites Reno: Enhancing one-step text-to-image models through reward-based noise optimization.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering Reno: Enhancing one-step text-to-image models through reward-based noise optimization

Reference 29

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Observation 1c229474-b10b-43f7-816d-afcee5962e20 · outbound

This paper cites Loss-guided diffusion models for plug-and-play controllable generation.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering Loss-guided diffusion models for plug-and-play controllable generation

Reference 30

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Observation 2fb1e799-c049-424d-a906-1c1e4d60fe26 · outbound

This paper cites Verifying the Union of Manifolds Hypothesis for Image Data.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering Verifying the Union of Manifolds Hypothesis for Image Data

Reference 31

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Observation 88f8f15c-7cfc-4ae5-918c-ce752288406d · outbound

This paper cites The Intrinsic Dimension of Images and Its Impact on Learning.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering The Intrinsic Dimension of Images and Its Impact on Learning

Reference 32

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Observation b6e1385a-04ab-4832-af10-fe286c3b0571 · outbound

This paper cites Analyzing and improving the training dynamics of diffusion models.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering Analyzing and improving the training dynamics of diffusion models

Reference 33

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source=pdf_text observed=2026-08-07T12:52:52.078319Z digest=sha256:471e315525ee04088bf410fef8a50ad1abe4aa8c095c2138dbd7e5bf09c90b5d

Observation 8504fe7a-bca6-403d-ba8a-87155e7f41d9 · outbound

This paper cites Lof: identifying density-based local outliers.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering Lof: identifying density-based local outliers

Reference 34

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation f4bcc3b0-9536-4ffd-8d10-f84f89bbe6cf · outbound

This paper cites A beach with shells organized to form the words ’Every grain of sand holds a universe of endless possibilities’.

Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample Filtering A beach with shells organized to form the words ’Every grain of sand holds a universe of endless possibilities’

Reference 35

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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