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Paper Citation Record · LEDGER

Diffusion Models with Adaptive Negative Sampling Without External Resources

As of 22 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2508.02973.

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

pith.paper-citation-record.v1
2508.02973 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T04:52:34.538179Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

  • verified exact0
  • verified fuzzy28
  • unresolved11
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a0641dd0-ce37-452a-a3a5-68d95da70c7a · outbound

This paper cites Negative prompt: Stable diffusion webui.https://github.com/AUTOMATIC1111/ stable - diffusion - webui / wiki / Negative - prompt.

Diffusion Models with Adaptive Negative Sampling Without External Resources Negative prompt: Stable diffusion webui.https://github.com/AUTOMATIC1111/ stable - diffusion - webui / wiki / Negative - prompt

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation b7a45112-0b6e-4e80-bc7c-f25c99c06839 · outbound

This paper cites Segmentation-free guidance for text-to-image dif- fusion models, 2024.

Diffusion Models with Adaptive Negative Sampling Without External Resources Segmentation-free guidance for text-to-image dif- fusion models, 2024

Reference 2

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verified fuzzy
raw_fallback, observed 2026-08-06T04:52:39.393928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 349087a7-0369-4695-b2fd-64fc55ce3f30 · outbound

This paper cites Retrieval-augmented diffusion models.

Diffusion Models with Adaptive Negative Sampling Without External Resources Retrieval-augmented diffusion models

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation a3f0f2de-ad9f-4726-807f-184125b58364 · outbound

This paper cites Coyo-700m: Image-text pair dataset.https : / / github.

Diffusion Models with Adaptive Negative Sampling Without External Resources Coyo-700m: Image-text pair dataset.https : / / github

Reference 4

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T04:52:31.817831Z digest=sha256:99256cf9a62d6f9a1ca49a341b8848abe6a448923f77edaf69eb7d2dce73c634

Observation 2814d498-0aaa-4fb0-8731-5c808dac491c · outbound

This paper cites Attend-and-excite: Attention-based se- mantic guidance for text-to-image diffusion models, 2023.

Diffusion Models with Adaptive Negative Sampling Without External Resources Attend-and-excite: Attention-based se- mantic guidance for text-to-image diffusion models, 2023

Reference 5

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verified fuzzy
raw_fallback, observed 2026-08-06T04:52:38.716628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T04:52:31.887974Z digest=sha256:2864d4c058de500e040c27060a7487d88ff4213d87b62a4f5eb3d612e30f640a

Observation 1d547997-94f2-4e24-a3c6-e30ea79084cf · outbound

This paper cites Improving image syn- thesis with diffusion-negative sampling, 2024.

Diffusion Models with Adaptive Negative Sampling Without External Resources Improving image syn- thesis with diffusion-negative sampling, 2024

Reference 6

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T04:52:31.967351Z digest=sha256:00ba4f26cf6c8521a8e01d8bb0b9ec205215321cd36c31aa85befed26c767fbb

Observation 2027beec-4dc9-4971-a923-2ec52e518edd · outbound

This paper cites Compositional visual generation with energy based models.

Diffusion Models with Adaptive Negative Sampling Without External Resources Compositional visual generation with energy based models

Reference 7

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raw_fallback, observed 2026-08-06T04:52:38.214571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T04:52:32.073547Z digest=sha256:0cb945d9305a0dff276b61788e1a5dcce4e2114520541dd22e4ae2fee5de3226

Observation 071454ce-1c10-4c59-9592-3d7356ba4266 · outbound

This paper cites Training-free structured diffusion guidance for compositional text-to-image synthesis, 2023.

Diffusion Models with Adaptive Negative Sampling Without External Resources Training-free structured diffusion guidance for compositional text-to-image synthesis, 2023

Reference 8

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:52:32.149151Z digest=sha256:a1737266c2cb092985ee716a0742633b5ccd227f1b700c9af52a2086cff5586d

Observation e4902864-c828-49cc-ae43-c2eb9bcd394e · outbound

This paper cites Expressive text-to-image generation with rich text.

Diffusion Models with Adaptive Negative Sampling Without External Resources Expressive text-to-image generation with rich text

Reference 9

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verified fuzzy
raw_fallback, observed 2026-08-06T04:52:38.012556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 503b970d-5cbe-4c85-af26-a55332363e58 · outbound

This paper cites Clipscore: A reference-free evaluation met- ric for image captioning, 2022.

Diffusion Models with Adaptive Negative Sampling Without External Resources Clipscore: A reference-free evaluation met- ric for image captioning, 2022

Reference 10

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verified fuzzy
raw_fallback, observed 2026-08-06T04:52:37.819853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 591c779e-4760-4808-ab00-e39554ade386 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilib- rium.Advances in neural information processing systems, 30, 2017.

Diffusion Models with Adaptive Negative Sampling Without External Resources Gans trained by a two time-scale update rule converge to a local nash equilib- rium.Advances in neural information processing systems, 30, 2017

Reference 11

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:52:32.411019Z digest=sha256:5ebe4b062e9b1211b7eeb7d454048939ccab5c97d966579768943f4961df149c

Observation 56b2d8c6-31ac-4400-9b7f-d30b65f78277 · outbound

This paper cites Classifier-Free Diffusion Guidance.

Diffusion Models with Adaptive Negative Sampling Without External Resources Classifier-Free Diffusion Guidance

Reference 12

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no resolver link, observed 2026-08-06T04:52:32.492418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:52:32.492418Z digest=sha256:a69a8bf9400f735d007c39d655b3bce5e24ca0995d4ca4635d4fe745473d0d67

Observation d25a8f9f-e677-4f4b-9cd0-4190c44466cb · outbound

This paper cites Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020.

Diffusion Models with Adaptive Negative Sampling Without External Resources Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

Reference 13

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no resolver link, observed 2026-08-06T04:52:32.615529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:52:32.615529Z digest=sha256:77c3799e79a5b08a41834d3e68deb7b231056ba375960d866dc22c4e90860fe2

Observation 5e4502eb-32fc-4f01-8d03-f8dafccbbe7f · outbound

This paper cites Elucidating the design space of diffusion-based generative models, 2022.

Diffusion Models with Adaptive Negative Sampling Without External Resources Elucidating the design space of diffusion-based generative models, 2022

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:37.531472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T04:52:32.696580Z digest=sha256:c1489d38c329947fb4f9059ec57380c0b99d3dfe8a3ca10bcb952cc9178c6e2c

Observation 642c99e8-23cb-49c4-81f9-ed72025e4480 · outbound

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

Diffusion Models with Adaptive Negative Sampling Without External Resources Pick-a-pic: An open dataset of user preferences for text-to-image generation

Reference 15

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no resolver link, observed 2026-08-06T04:52:32.796277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:52:32.796277Z digest=sha256:decd7dbdcd484c53faaf205ce83893c46a286caa154d21998607652d39c222ca

Observation 1af1344e-5b21-4dbe-8fa5-7f8d23ecb470 · outbound

This paper cites Multi-concept customization of text-to-image diffusion.

Diffusion Models with Adaptive Negative Sampling Without External Resources Multi-concept customization of text-to-image diffusion

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:37.363899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T04:52:32.874658Z digest=sha256:1afe802fcd1e2b159c1628a0e0d74e8aefc97fb0e1cd45aeef9fcb909a1bec5e

Observation db369a41-c03c-48eb-b6f4-ec54f6dce4a6 · outbound

This paper cites Gligen: Open-set grounded text-to-image generation.

Diffusion Models with Adaptive Negative Sampling Without External Resources Gligen: Open-set grounded text-to-image generation

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:37.195235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T04:52:32.987822Z digest=sha256:2c0a6f76a21471081db3984f9def2defd2c59459ed60b61b5181df1183fe43b5

Observation 79c43292-985f-43ec-8a0b-e665a013cc5b · outbound

This paper cites Compositional visual generation with composable diffusion models.

Diffusion Models with Adaptive Negative Sampling Without External Resources Compositional visual generation with composable diffusion models

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:37.032231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T04:52:33.076357Z digest=sha256:a7243461fffd7012a909d2e88441a5b9a476e0067116629937a0f0504c4008f8

Observation deb1bed9-8d31-4439-b888-9fc379ce8d02 · outbound

This paper cites Grounded Text-to-Image Synthesis with Attention Refocusing.

Diffusion Models with Adaptive Negative Sampling Without External Resources Grounded Text-to-Image Synthesis with Attention Refocusing

Reference 19

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no resolver link, observed 2026-08-06T04:52:33.142771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:52:33.142771Z digest=sha256:cd8d8a4b15d3888a8c840c1c1dd14836748e89ea0a2f9b27de5c3eb198960a4a

Observation 611784f0-a768-4f7a-9e4a-88b81dd0cb64 · outbound

This paper cites Sdxl: Improving latent diffusion models for high-resolution image synthesis, 2023.

Diffusion Models with Adaptive Negative Sampling Without External Resources Sdxl: Improving latent diffusion models for high-resolution image synthesis, 2023

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:36.827822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T04:52:33.232616Z digest=sha256:e28d47a58c2da6abd4a54e9483c19d5080f7fd7666d03290f5676cd966633b5a

Observation a6be1a63-2a7d-4d4b-ad6c-2a2e26fd5382 · outbound

This paper cites Zero-shot text-to-image generation.

Diffusion Models with Adaptive Negative Sampling Without External Resources Zero-shot text-to-image generation

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:36.740198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T04:52:33.331751Z digest=sha256:ce545cc074fde1d5f84e9680c3bcbcb59ba132277d60bb4a47e5b5b0a55d4bc7

Observation 28542aa7-d271-4513-a09a-41da96310ada · outbound

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

Diffusion Models with Adaptive Negative Sampling Without External Resources Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 22

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:52:33.371018Z digest=sha256:9bcbdef511ad9d8e4641e85bcdd626afb2cb015d1823a3b53a2364e97a4dcb5d

Observation e00bb6d5-8ddd-471d-a904-aedfb3f18836 · outbound

This paper cites Linguistic bind- ing in diffusion models: Enhancing attribute correspondence through attention map alignment, 2023.

Diffusion Models with Adaptive Negative Sampling Without External Resources Linguistic bind- ing in diffusion models: Enhancing attribute correspondence through attention map alignment, 2023

Reference 23

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verified fuzzy
raw_fallback, observed 2026-08-06T04:52:36.559145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T04:52:33.458780Z digest=sha256:06b81ac844dbed26f9adcb9b916621212882943df90c392535ad6b6c6c801ad3

Observation 7bb9da86-31b6-4a7e-89a7-a218e210058e · outbound

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

Diffusion Models with Adaptive Negative Sampling Without External Resources High-resolution image synthesis with latent diffusion models

Reference 24

Resolution
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no resolver link, observed 2026-08-06T04:52:33.512523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:52:33.512523Z digest=sha256:28f30252f67ea5d81086a507ba8739a8a5d8e11d1968f053014242bf88f6bf8d

Observation 106ff50a-c801-4d9e-bf2b-6cc26d585996 · outbound

This paper cites Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation.

Diffusion Models with Adaptive Negative Sampling Without External Resources Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:36.376329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T04:52:33.604813Z digest=sha256:3d07fcdebd4e29251087f04ab8485c2df5613d0d6069dc3a74c2522bd3b4a548

Observation f63d7b67-68fa-482f-94dd-a8bf95ad5c36 · outbound

This paper cites Berg, and Li Fei-Fei.

Diffusion Models with Adaptive Negative Sampling Without External Resources Berg, and Li Fei-Fei

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:36.200741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T04:52:33.689783Z digest=sha256:c10b29131a2bb48fedf918f1c5bcafd2dd784066c3a479899fe770619efc6e23

Observation 9883a506-d069-488d-a287-313d46519eb2 · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding.Advances in Neural Information Processing Systems, 35:36479–36494, 2022.

Diffusion Models with Adaptive Negative Sampling Without External Resources Photorealistic text-to-image diffusion models with deep language understanding.Advances in Neural Information Processing Systems, 35:36479–36494, 2022

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:36.073934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T04:52:33.735322Z digest=sha256:dfe8328dff1da16031b5152f129ec72cdd2173a24dae95ccd55367b2fc46781d

Observation f394791f-bf25-4ec7-9d5a-176c9d726655 · outbound

This paper cites Improved techniques for training gans, 2016.

Diffusion Models with Adaptive Negative Sampling Without External Resources Improved techniques for training gans, 2016

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:35.920278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T04:52:33.770754Z digest=sha256:3998c945ffb658fd74626d5fc8c25aa264f350502df06374534ffd0dbc0cdcd8

Observation 51acbde5-9501-4e86-9b6a-2e398527c3ed · outbound

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

Diffusion Models with Adaptive Negative Sampling Without External Resources Laion-5b: An open large-scale dataset for training next generation image-text models

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:35.795602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T04:52:33.826580Z digest=sha256:83d7b0b6cf80350f7f0d895392ba1f530f778711b3b4466b99dd1bcd0b344e20

Observation 87bcda5c-04c1-4637-b5aa-332eac3425c3 · outbound

This paper cites Denoising Diffusion Implicit Models.

Diffusion Models with Adaptive Negative Sampling Without External Resources Denoising Diffusion Implicit Models

Reference 30

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unresolved
no resolver link, observed 2026-08-06T04:52:33.890126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:52:33.890126Z digest=sha256:62a11dc73444097186a23d4766963aa7bd0db24cadff55cd4777606f940a5e8d

Observation 791a26fe-3dfc-4ccc-a42b-1a58030cd623 · outbound

This paper cites Generative modeling by esti- mating gradients of the data distribution.Advances in neural information processing systems, 32, 2019.

Diffusion Models with Adaptive Negative Sampling Without External Resources Generative modeling by esti- mating gradients of the data distribution.Advances in neural information processing systems, 32, 2019

Reference 31

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no resolver link, observed 2026-08-06T04:52:33.976377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:52:33.976377Z digest=sha256:a68e5472c34aff9facc3217511c079ecaadb3beb35e2c3c191e60235fb1cc150

Observation e81d45b0-77aa-40fe-bf9d-06011f2c318b · outbound

This paper cites Adapting diffusion models for improved prompt compliance and controllable image synthesis, 2024.

Diffusion Models with Adaptive Negative Sampling Without External Resources Adapting diffusion models for improved prompt compliance and controllable image synthesis, 2024

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:35.606202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T04:52:34.037147Z digest=sha256:62cbea73512b289c0d5cc9c13156af6d8d5d198ccbb647395d41680f536f7405

Observation eabad5de-4fe2-49a8-8d25-4ad884ccb70c · outbound

This paper cites Sketch-guided text-to-image diffusion models, 2022.

Diffusion Models with Adaptive Negative Sampling Without External Resources Sketch-guided text-to-image diffusion models, 2022

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:35.434415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T04:52:34.092883Z digest=sha256:68e15ae61860d0e926cf8c7f01e3fffe65aa77362f6af9bc8425ca5d5affc665

Observation e1ab6f44-99eb-4593-b13c-5eebbda6fd6a · outbound

This paper cites Bayesian learning via stochas- tic gradient langevin dynamics.

Diffusion Models with Adaptive Negative Sampling Without External Resources Bayesian learning via stochas- tic gradient langevin dynamics

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:35.285132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T04:52:34.150526Z digest=sha256:482445c19b86d50d38cd01501de7aae8d4e45f8b60243233411ab0f5440a433a

Observation 6cb0804d-7cf3-43c7-b50c-e4934bd6b9fd · outbound

This paper cites Human preference score v2: A solid benchmark for evaluating human preferences of text-to-image synthesis, 2023.

Diffusion Models with Adaptive Negative Sampling Without External Resources Human preference score v2: A solid benchmark for evaluating human preferences of text-to-image synthesis, 2023

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-06T04:52:34.243625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:52:34.243625Z digest=sha256:e3c50a83d4e2e15e34275a5102767e67a4fe14faa64d35b598f6b3196a50d7bd

Observation 8a339e04-e8bf-4055-9748-7b27c9ffa1e1 · outbound

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

Diffusion Models with Adaptive Negative Sampling Without External Resources Imagereward: learning and evaluating human preferences for text-to-image generation

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:35.136008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T04:52:34.305158Z digest=sha256:58e71a4a2163c2a3ad9049630ff140289568d3b6dcfea79aac0d754f27cada7d

Observation e2f4a43b-4408-47af-b354-25df8bb3f732 · outbound

This paper cites Scaling autoregressive models for content-rich text-to-image generation, 2022.

Diffusion Models with Adaptive Negative Sampling Without External Resources Scaling autoregressive models for content-rich text-to-image generation, 2022

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:35.032972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T04:52:34.396044Z digest=sha256:26e58d96a9cd2864703b22e577cfed926c98ef18c0ac8d7a2d2b3db353b5da71

Observation a516c474-3196-453c-b862-bd5281593fdf · outbound

This paper cites Adding conditional control to text-to-image diffusion models, 2023.

Diffusion Models with Adaptive Negative Sampling Without External Resources Adding conditional control to text-to-image diffusion models, 2023

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:34.884202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T04:52:34.483121Z digest=sha256:b46dbc5f9b4ce8807b7c28ef1d36591e06d33ceb38ab5fdf52b2ffc85c3ad25c

Observation 42a94cbb-7c30-40eb-9c21-b40dd916cc58 · outbound

This paper cites Layoutdiffusion: Controllable diffu- sion model for layout-to-image generation.

Diffusion Models with Adaptive Negative Sampling Without External Resources Layoutdiffusion: Controllable diffu- sion model for layout-to-image generation

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:34.736182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T04:52:34.538179Z digest=sha256:d9d342e06231984a7b4532064c946b5cdac8cff2cedc873893697cbe788a0397

Pith citing papers

No inbound Pith citation observations are available.