Paper Citation Record · LEDGER

Optimizing Few-Step Generation with Adaptive Matching Distillation

As of 5 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 7 inbound Pith citation observations for arXiv:2602.07345.

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

pith.paper-citation-record.v1
2602.07345 v2

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T03:42:46.314874Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T21:44:21.427570Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-02T02:26:26.234442Z

Reference resolution

39 of 39 outbound references displayed

verified exact0
verified fuzzy0
unresolved38
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a94e8041-b270-41da-a0ca-88f9520d7e19 · outbound

This paper cites Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection.

Optimizing Few-Step Generation with Adaptive Matching Distillation Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:44.895516Z

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Unavailable: canonical work link unavailable.

Observation 2e5593ea-f52a-4ddf-bbac-4615925ae80d · outbound

This paper cites an unresolved cited work.

Optimizing Few-Step Generation with Adaptive Matching Distillation Unresolved cited work

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:46.265651Z

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Observation 27961fb5-3e63-4fde-ae89-4d08408ee536 · outbound

This paper cites GenEval: An Object-Focused Framework for Evaluating Text-to-Image Alignment.

Optimizing Few-Step Generation with Adaptive Matching Distillation GenEval: An Object-Focused Framework for Evaluating Text-to-Image Alignment

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:45.045598Z

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Observation 73cc5f58-33af-4aca-b1f5-27cb1ebd8d40 · outbound

This paper cites Distribution Matching Distillation Meets Reinforcement Learning.

Optimizing Few-Step Generation with Adaptive Matching Distillation Distribution Matching Distillation Meets Reinforcement Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:45.145549Z

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Observation 1595193f-539d-4176-b337-20d30d908c95 · outbound

This paper cites Crafting papers on machine learning.

Optimizing Few-Step Generation with Adaptive Matching Distillation Crafting papers on machine learning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:45.176585Z

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Observation 17e289a0-9967-4014-bf20-f52c2613b7d6 · outbound

This paper cites SDXL-Lightning: Progressive Adversarial Diffusion Distillation.

Optimizing Few-Step Generation with Adaptive Matching Distillation SDXL-Lightning: Progressive Adversarial Diffusion Distillation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:45.208286Z

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Observation 0b308a77-be6d-41d2-b7ea-1973d3a6cc5b · outbound

This paper cites Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference.

Optimizing Few-Step Generation with Adaptive Matching Distillation Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:45.271070Z

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Observation 0b5951ea-e949-44ab-ace6-c1a0022aea16 · outbound

This paper cites SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers.

Optimizing Few-Step Generation with Adaptive Matching Distillation SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:45.300743Z

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Observation 78f6fba3-ea58-4f7d-a067-f2d6c2c3aedd · outbound

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

Optimizing Few-Step Generation with Adaptive Matching Distillation SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:45.328230Z

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Observation 821a0163-ccbd-4482-89de-2e0b86f37a2c · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision.

Optimizing Few-Step Generation with Adaptive Matching Distillation Learning Transferable Visual Models From Natural Language Supervision

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:45.361337Z

Source-reported events for the cited work

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Observation dc39026e-d410-4cd4-9519-6dfb796a1b3d · outbound

This paper cites Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding.

Optimizing Few-Step Generation with Adaptive Matching Distillation Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:45.470306Z

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Observation ccf3a98c-940b-40e6-97dc-aa7e692db703 · outbound

This paper cites MagicDistillation: Weak-to-Strong Video Distillation for Large-Scale Few-Step Synthesis.

Optimizing Few-Step Generation with Adaptive Matching Distillation MagicDistillation: Weak-to-Strong Video Distillation for Large-Scale Few-Step Synthesis

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:45.505715Z

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Observation 0709519f-fc91-442a-96da-f11fdb5d5639 · outbound

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

Optimizing Few-Step Generation with Adaptive Matching Distillation Wan: Open and Advanced Large-Scale Video Generative Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:45.651558Z

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Observation 7d93e999-a01b-4029-a8e1-86816cca1e73 · outbound

This paper cites Phased Consistency Models.

Optimizing Few-Step Generation with Adaptive Matching Distillation Phased Consistency Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:45.684650Z

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Observation 8bd2ca99-e133-4fae-8c4a-5a43809d9c17 · outbound

This paper cites LongLive: Real-time Interactive Long Video Generation.

Optimizing Few-Step Generation with Adaptive Matching Distillation LongLive: Real-time Interactive Long Video Generation

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:45.719431Z

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Observation 8595e3e4-53ae-4c1a-9479-5531b6b09e7f · outbound

This paper cites I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models.

Optimizing Few-Step Generation with Adaptive Matching Distillation I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:45.750170Z

Source-reported events for the cited work

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Observation 738b0bb5-a1b9-4e84-aa95-0291161c62e0 · outbound

This paper cites DiffusionNFT: Online Diffusion Reinforcement with Forward Process.

Optimizing Few-Step Generation with Adaptive Matching Distillation DiffusionNFT: Online Diffusion Reinforcement with Forward Process

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:45.779360Z

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Observation 792b40ff-6f97-4111-a398-d8f0370f8e07 · outbound

This paper cites Implementation Details In this section, we provide an overview of the benchmarks, evaluation metrics, diffusion models and the hyperparameter settings in our main paper.

Optimizing Few-Step Generation with Adaptive Matching Distillation Implementation Details In this section, we provide an overview of the benchmarks, evaluation metrics, diffusion models and the hyperparameter settings in our main paper

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:45.809208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83d61f16-98f2-4586-b36a-bd42bd3f459e · outbound

This paper cites Second, for assessing text-image alignment and fine-grained details, we utilize a subset of 10,000 prompts (COCO-10k).

Optimizing Few-Step Generation with Adaptive Matching Distillation Second, for assessing text-image alignment and fine-grained details, we utilize a subset of 10,000 prompts (COCO-10k)

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:45.841527Z

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Unavailable: canonical work link unavailable.

Observation 9568cb58-18de-4b4c-8c2c-ac1d13c8a100 · outbound

This paper cites Following the rigorous protocols established in SiT (Ma et al., 2024), we assess generation fidelity using the Fr´echet Inception Distance (FID-50K).

Optimizing Few-Step Generation with Adaptive Matching Distillation Following the rigorous protocols established in SiT (Ma et al., 2024), we assess generation fidelity using the Fr´echet Inception Distance (FID-50K)

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:45.877883Z

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Observation 7a4880a0-5d37-4781-8df2-227f670f5576 · outbound

This paper cites With 798,090 binary preference labels across 433,760 image pairs, it addresses the limitations of conventional evaluation metrics that fail to accurately reflect human preferences.

Optimizing Few-Step Generation with Adaptive Matching Distillation With 798,090 binary preference labels across 433,760 image pairs, it addresses the limitations of conventional evaluation metrics that fail to accurately reflect human preferences

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:45.921774Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e551c19-2783-4a83-a2e3-47f65cbaa88b · outbound

This paper cites By learning from a vast collection of expert-annotated comparisons, it addresses the limitations of standard metrics by mitigating ”gamification” issues.

Optimizing Few-Step Generation with Adaptive Matching Distillation By learning from a vast collection of expert-annotated comparisons, it addresses the limitations of standard metrics by mitigating ”gamification” issues

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:45.966771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1bafee2c-b248-4ad7-84a1-5e4fb17e018f · outbound

This paper cites an unresolved cited work.

Optimizing Few-Step Generation with Adaptive Matching Distillation Unresolved cited work

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:46.012713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee9eea0f-1a80-4eb9-8fb9-52a5225d4769 · outbound

This paper cites For experiments on text-to-image, especially for SDXL, as shown in Tab.

Optimizing Few-Step Generation with Adaptive Matching Distillation For experiments on text-to-image, especially for SDXL, as shown in Tab

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:46.050633Z

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Observation ca4ac00a-35c3-4ab0-85e5-655b8a387a14 · outbound

This paper cites an unresolved cited work.

Optimizing Few-Step Generation with Adaptive Matching Distillation Unresolved cited work

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:46.095351Z

Source-reported events for the cited work

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Observation fa4bbb3f-da2e-4882-9ad2-f56bd0948020 · outbound

This paper cites an unresolved cited work.

Optimizing Few-Step Generation with Adaptive Matching Distillation Unresolved cited work

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:46.161221Z

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Observation c22ea6bd-c28f-48f7-9591-aaf0ecd2302a · outbound

This paper cites Most pertinent to our work is the recently proposedReward Forcing(Lu et al., 2025), which biases the student’s distribution by prioritizing training samples with high reward scores.

Optimizing Few-Step Generation with Adaptive Matching Distillation Most pertinent to our work is the recently proposedReward Forcing(Lu et al., 2025), which biases the student’s distribution by prioritizing training samples with high reward scores

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:46.221622Z

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Observation 5b199734-7aea-4fc6-85a1-6864dea5884f · outbound

This paper cites an unresolved cited work.

Optimizing Few-Step Generation with Adaptive Matching Distillation Unresolved cited work

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:46.290387Z

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Unavailable: canonical work link unavailable.

Observation fd2d1d99-6520-4303-bfae-bd572592079e · outbound

This paper cites (base model: SDXL).

Optimizing Few-Step Generation with Adaptive Matching Distillation (base model: SDXL)

Reference 39

Resolution: malformed identifier; no resolver link, observed 2026-08-03T03:42:46.314874Z

Source-reported events for the cited work

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Observation d3a2357b-592b-4ddc-8da7-68a8526427ab · outbound

This paper cites Decoupled dmd: Cfg augmentation as the spear, distribution matching as the shield.arXiv preprint arXiv:2511.22677, 2025a.

Optimizing Few-Step Generation with Adaptive Matching Distillation Decoupled dmd: Cfg augmentation as the spear, distribution matching as the shield.arXiv preprint arXiv:2511.22677, 2025a

Reference 312

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:45.241006Z

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Observation e4518888-9283-4dc9-8933-e9440e923133 · outbound

This paper cites ImageNet Large Scale Visual Recognition Challenge.

Optimizing Few-Step Generation with Adaptive Matching Distillation ImageNet Large Scale Visual Recognition Challenge

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:45.438217Z

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Observation 3fdfb9b5-3c6c-4149-a3fa-dbcae5b838e7 · outbound

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

Optimizing Few-Step Generation with Adaptive Matching Distillation GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:45.108206Z

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Observation 85a5b5d1-d76b-4824-8671-b6ad246bc654 · outbound

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

Optimizing Few-Step Generation with Adaptive Matching Distillation Score-Based Generative Modeling through Stochastic Differential Equations

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:45.589668Z

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Observation 1d01fc7f-0c92-490a-bac4-4cded94decaf · outbound

This paper cites MAGI-1: Autoregressive Video Generation at Scale.

Optimizing Few-Step Generation with Adaptive Matching Distillation MAGI-1: Autoregressive Video Generation at Scale

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:45.618222Z

Source-reported events for the cited work

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Observation 4a0c160b-0a02-4a17-92e3-d4e6a559386e · outbound

This paper cites Emerging Properties in Self-Supervised Vision Transformers.

Optimizing Few-Step Generation with Adaptive Matching Distillation Emerging Properties in Self-Supervised Vision Transformers

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:45.005609Z

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Observation e98487b8-f7c6-48fb-bd16-55380319048f · outbound

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

Optimizing Few-Step Generation with Adaptive Matching Distillation High-Resolution Image Synthesis with Latent Diffusion Models

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:45.405545Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebb63d88-1e93-43c3-9e95-34d2a6f43ad1 · outbound

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

Optimizing Few-Step Generation with Adaptive Matching Distillation Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:44.937840Z

Source-reported events for the cited work

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Observation 5b7854cf-5885-4cef-9760-b3faea783d58 · outbound

This paper cites Gardo: Reinforcing diffusion models without reward hacking.arXiv preprint arXiv:2512.24138,.

Optimizing Few-Step Generation with Adaptive Matching Distillation Gardo: Reinforcing diffusion models without reward hacking.arXiv preprint arXiv:2512.24138,

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:45.077104Z

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Unavailable: canonical work link unavailable.

Observation 3a0d5f6d-c118-42bf-a854-a1022d7974b1 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Optimizing Few-Step Generation with Adaptive Matching Distillation DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:45.543473Z

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Pith citing papers

Observation 99365bf3-c715-410c-904b-af6d733a60c4 · inbound

Efficient Video Diffusion Models: Advancements and Challenges cites this paper.

Efficient Video Diffusion Models: Advancements and Challenges Optimizing Few-Step Generation with Adaptive Matching Distillation

Reference 211

Resolution: verified exact; arxiv_id, observed 2026-06-09T03:07:10.782702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 13ffa131-4aad-43b7-b4f4-bcd60e2b5c1c · inbound

FlashMol: High-Quality Molecule Generation in as Few as Four Steps cites this paper.

FlashMol: High-Quality Molecule Generation in as Few as Four Steps Optimizing Few-Step Generation with Adaptive Matching Distillation

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-06-09T03:07:10.782702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ca2f7bfc-48d9-4a42-8158-db9c81fdc8b3 · inbound

Delta Forcing: Trust Region Steering for Interactive Autoregressive Video Generation cites this paper.

Delta Forcing: Trust Region Steering for Interactive Autoregressive Video Generation Optimizing Few-Step Generation with Adaptive Matching Distillation

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-06-09T03:07:10.782702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 456556a0-8c83-478b-89c7-ba166cbe1b06 · inbound

Delta Forcing: Trust Region Steering for Interactive Autoregressive Video Generation cites this paper.

Delta Forcing: Trust Region Steering for Interactive Autoregressive Video Generation Optimizing Few-Step Generation with Adaptive Matching Distillation

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-06-09T03:07:10.782702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c4519b5f-81d3-4e5d-b540-bb88074422ba · inbound

Delta Forcing: Trust Region Steering for Interactive Autoregressive Video Generation cites this paper.

Delta Forcing: Trust Region Steering for Interactive Autoregressive Video Generation Optimizing Few-Step Generation with Adaptive Matching Distillation

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-06-09T03:07:10.782702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e55bab76-b2be-4bd4-baf0-be248b5403d7 · inbound

Delta Forcing: Trust Region Steering for Interactive Autoregressive Video Generation cites this paper.

Delta Forcing: Trust Region Steering for Interactive Autoregressive Video Generation Optimizing Few-Step Generation with Adaptive Matching Distillation

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-06-30T21:45:05.522498Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5b003e45-fd9b-4591-bab6-1dd2e3f94645 · inbound

Qwen-Image-Flash: Beyond Objective Design cites this paper.

Qwen-Image-Flash: Beyond Objective Design Optimizing Few-Step Generation with Adaptive Matching Distillation

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-07-02T02:26:26.235927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.