Paper Citation Record · LEDGER

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling

As of 4 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 1 inbound Pith citation observation for arXiv:2605.07253.

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

pith.paper-citation-record.v1
2605.07253 v1

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-11T01:43:15.520788Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-31T23:33:08.124759Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

49 of 49 outbound references displayed

verified exact15
verified fuzzy32
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8cdd2f5d-426f-45ff-b3bc-9d74db09f202 · outbound

This paper cites A noise is worth diffusion guidance.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling A noise is worth diffusion guidance

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.524349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 33c84e31-28a4-4cb5-96b6-7b99670f5842 · outbound

This paper cites The Crystal Ball Hypothesis in diffusion models: Anticipating object positions from initial noise.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling The Crystal Ball Hypothesis in diffusion models: Anticipating object positions from initial noise

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-11T01:45:51.159014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 336a5fb9-90fd-4679-9ad1-1ae00b8b69ce · outbound

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

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling D-Flow: Differentiating through Flows for Controlled Generation

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-11T01:45:51.119196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 19824b0e-3155-4e60-b33d-22c341023cff · outbound

This paper cites Sana-sprint: One-step diffusion with continuous-time consistency distillation.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Sana-sprint: One-step diffusion with continuous-time consistency distillation

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.488484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 696b9745-af44-4fea-aff4-b4498e4906b5 · outbound

This paper cites Scaling rectified flow trans- formers for high-resolution image synthesis.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Scaling rectified flow trans- formers for high-resolution image synthesis

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.479452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ac32b658-ea47-495b-96f5-3e885095747b · outbound

This paper cites Reno: Enhancing one-step text-to-image models through reward-based noise optimization.Advances in Neural Information Processing Systems, 37:125487–125519.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Reno: Enhancing one-step text-to-image models through reward-based noise optimization.Advances in Neural Information Processing Systems, 37:125487–125519

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.504856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 35800d98-baa0-463e-86de-9017e33979c9 · outbound

This paper cites Noise hypernetworks: Amortizing test-time compute in diffusion models.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Noise hypernetworks: Amortizing test-time compute in diffusion models

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.507364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5bb50145-cb64-4d02-a65e-017ff25f18d9 · outbound

This paper cites Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-11T01:45:51.123178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f376accd-e857-4e97-847f-bdfb351028a6 · outbound

This paper cites Initno: Boosting text-to-image diffusion models via initial noise optimization.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Initno: Boosting text-to-image diffusion models via initial noise optimization

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.482215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 350e2a97-ca00-4c59-a76a-9390d02398d8 · outbound

This paper cites Clipscore: A reference-free evaluation metric for image captioning.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Clipscore: A reference-free evaluation metric for image captioning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.473219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 538e5e5f-0136-441a-8084-d2dcd93613dc · outbound

This paper cites Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.509683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation bcc755fd-1d06-4b20-97f0-a144fc9f1362 · outbound

This paper cites Lora: Low-rank adaptation of large language models.Iclr, 1(2):3.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Lora: Low-rank adaptation of large language models.Iclr, 1(2):3

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.460522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8d1a2108-f8f5-4632-993a-ed9a458ad591 · outbound

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

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:43:03.755490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9c8c7ce7-fde4-4c5f-8550-959b7bec26ee · outbound

This paper cites T2i-compbench: A compre- hensive benchmark for open-world compositional text-to-image generation.Advances in Neural Information Processing Systems, 36:78723–78747.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling T2i-compbench: A compre- hensive benchmark for open-world compositional text-to-image generation.Advances in Neural Information Processing Systems, 36:78723–78747

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.500078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f1e03a7e-c43e-44f6-90ef-a47f10cd744a · outbound

This paper cites yes" or.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling yes" or

Reference 15

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T01:45:51.115102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3f072506-e74d-4b3f-8925-d94d5f66b75e · outbound

This paper cites Optimizing diffusion noise can serve as universal motion priors.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Optimizing diffusion noise can serve as universal motion priors

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.436505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2966a6f8-0799-4697-9c8d-f59a55279cff · outbound

This paper cites Auto-Encoding Variational Bayes.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Auto-Encoding Variational Bayes

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-05-11T01:45:51.146005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4dc69bcb-c2c4-4949-98ad-e94e7258ad57 · outbound

This paper cites Pick-a-pic: An open dataset of user preferences for text-to-image generation.Advances in neural information processing systems, 36:36652–36663.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Pick-a-pic: An open dataset of user preferences for text-to-image generation.Advances in neural information processing systems, 36:36652–36663

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.520103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 640a69ae-dea9-4b42-a0e8-64c1a335a29a · outbound

This paper cites Enhancing compositional text-to- image generation with reliable random seeds.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Enhancing compositional text-to- image generation with reliable random seeds

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.502211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c9cbbaaa-77f1-4275-9dfc-2e436e12ec6c · outbound

This paper cites NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-11T01:45:51.163286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation bb043021-20ff-4abc-ba12-577951b2f3a6 · outbound

This paper cites Is-diff: Improving diffusion-based inpainting with better initial seed.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Is-diff: Improving diffusion-based inpainting with better initial seed

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-11T01:45:51.154365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 30b1ab22-bc96-45b6-9b3f-11c20a9e0a07 · outbound

This paper cites The lottery ticket hypothesis in denoising: Towards semantic-driven initialization.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling The lottery ticket hypothesis in denoising: Towards semantic-driven initialization

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.425415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 20ada5cc-7f06-4056-8159-f47c9933593c · outbound

This paper cites Noise diffusion for enhancing semantic faithfulness in text-to-image synthesis.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Noise diffusion for enhancing semantic faithfulness in text-to-image synthesis

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.468032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f73d884f-7156-4164-b323-db5dc8f8495e · outbound

This paper cites DITTO: Diffusion Inference-Time T-Optimization for Music Generation.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling DITTO: Diffusion Inference-Time T-Optimization for Music Generation

Reference 24

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T01:45:51.171634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 47e6bd35-8d31-4b29-b943-d952dba0c2bc · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling DINOv2: Learning Robust Visual Features without Supervision

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-05-11T01:45:51.149781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b78caf90-a0c6-49b2-b35b-d6d2cafb7f60 · outbound

This paper cites Do imagenet classifiers generalize to imagenet? InInternational conference on machine learning, pages 5389–5400.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Do imagenet classifiers generalize to imagenet? InInternational conference on machine learning, pages 5389–5400

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.440863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b579d8a9-f1be-4ebf-87e6-e3cbddcd2645 · outbound

This paper cites Hyper-sd: Trajectory segmented consistency model for efficient image synthesis.Advances in neural information processing systems, 37:117340–117362.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Hyper-sd: Trajectory segmented consistency model for efficient image synthesis.Advances in neural information processing systems, 37:117340–117362

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.455599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f2c3a076-9c5f-4f6d-a6a4-a42eb3a00598 · outbound

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

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling High- resolution image synthesis with latent diffusion models

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.497912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7068e456-8a14-42ee-8c08-a8a02ef04c2d · outbound

This paper cites Imagenet large scale visual recognition challenge.International journal of computer vision, 115(3):211–252.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Imagenet large scale visual recognition challenge.International journal of computer vision, 115(3):211–252

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.444887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2219c147-1d8c-4c0c-9390-aaabc7ef828a · outbound

This paper cites Progressive Distillation for Fast Sampling of Diffusion Models.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Progressive Distillation for Fast Sampling of Diffusion Models

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:37:44.815756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 62c853dd-9070-4245-8360-272530f6d85e · outbound

This paper cites Adversarial diffusion distillation.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Adversarial diffusion distillation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.522272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation dd002491-1a5a-4df2-bce7-5aad54a33964 · outbound

This paper cites Stretching each dollar: Diffusion training from scratch on a micro-budget.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Stretching each dollar: Diffusion training from scratch on a micro-budget

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.451923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f97a5b2c-5dfb-4230-821e-381bc8b73560 · outbound

This paper cites Denoising Diffusion Implicit Models.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Denoising Diffusion Implicit Models

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-05-11T01:45:51.138389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2f8e6835-efda-44c8-94b0-a5953e9dae59 · outbound

This paper cites Consistency models.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Consistency models

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.491031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d6f82178-51eb-4f35-9142-37a0bc0e2e61 · outbound

This paper cites Tuning-free alignment of diffusion models with direct noise optimization.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Tuning-free alignment of diffusion models with direct noise optimization

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.514972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 83e5a7ae-03c8-40db-bf8f-6cb1161e700c · outbound

This paper cites Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA Design.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA Design

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-11T01:45:51.131139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 343c2c9c-af65-4da2-bd8e-be679e8d505a · outbound

This paper cites Inference-Time Alignment in Diffusion Models with Reward-Guided Generation: Tutorial and Review.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Inference-Time Alignment in Diffusion Models with Reward-Guided Generation: Tutorial and Review

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-11T01:45:51.134869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 08290114-725c-4d0d-abdf-0d3aaaddc60d · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Attention is all you need.Advances in neural information processing systems, 30

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.517441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d1c25eb9-804a-4ecb-8384-c9f87c0b3462 · outbound

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

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling End-to-end diffusion latent optimization improves classifier guidance

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.493292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d86a6473-ae6d-41be-86cc-786d7228ee21 · outbound

This paper cites Seeds of structure: Patch PCA reveals universal compositional cues in diffusion models.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Seeds of structure: Patch PCA reveals universal compositional cues in diffusion models

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.463124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3b8a5d12-cb45-451e-bbb2-60bbad797a4c · outbound

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

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:30:48.951760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ba34c876-3c22-4310-b0c7-1a90f3aac7fc · outbound

This paper cites TLCM: Training-efficient Latent Consistency Model for Image Generation with 2-8 Steps.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling TLCM: Training-efficient Latent Consistency Model for Image Generation with 2-8 Steps

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-11T01:45:51.142498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 69455f36-a15c-4641-b7cc-adf3d24fb5af · outbound

This paper cites Em distillation for one-step diffusion models.Advances in Neural Information Processing Systems, 37:45073–45104.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Em distillation for one-step diffusion models.Advances in Neural Information Processing Systems, 37:45073–45104

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.470499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 43ab2c52-e1e7-4cd7-9514-8e8346899ecd · outbound

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

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Imagereward: Learning and evaluating human preferences for text-to-image generation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.495814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation fa550ee2-586b-4d31-9406-c3847895111a · outbound

This paper cites Good seed makes a good crop: Discovering secret seeds in text-to-image diffusion models.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Good seed makes a good crop: Discovering secret seeds in text-to-image diffusion models

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.431153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d5916463-b845-4394-abb0-e86b4245e91b · outbound

This paper cites One-step diffusion with distribution matching distillation.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling One-step diffusion with distribution matching distillation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.484280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 85eabe9d-93a5-4741-ad81-bd5d0a6a6fef · outbound

This paper cites Text-to-image Diffusion Models in Generative AI: A Survey.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Text-to-image Diffusion Models in Generative AI: A Survey

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-11T01:45:51.111223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 48ef0a80-4f8e-416e-850a-3828da34f0bd · outbound

This paper cites The unrea- sonable effectiveness of deep features as a perceptual metric.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling The unrea- sonable effectiveness of deep features as a perceptual metric

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.512987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 28ecd2e1-749a-4b0e-83a3-07fa39c72483 · outbound

This paper cites Golden noise for diffusion models: A learning framework.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling Golden noise for diffusion models: A learning framework

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-14T16:01:58.476888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Pith citing papers

Observation f614f200-2250-4ef4-9cf3-427b3ce290c7 · inbound

Manifold-Constrained Noise Optimization for Diverse Diffusion Sampling cites this paper.

Manifold-Constrained Noise Optimization for Diverse Diffusion Sampling LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-31T23:33:08.124759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.