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Paper Citation Record · LEDGER

Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 29 inbound Pith citation observations for arXiv:2112.07804.

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

pith.paper-citation-record.v1
2112.07804 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 29 of 29 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T15:20:57.754574Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T10:29:45.256333Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 0f516002-3b44-48c1-9288-7a306d8a37cb · inbound

Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning cites this paper.

Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 21

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arxiv_id, observed 2026-05-15T07:55:15.782473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 91d0e56d-d6d3-4f81-8e7b-f33537a3eb13 · inbound

Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow cites this paper.

Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 91

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arxiv_id, observed 2026-05-10T13:17:51.664048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T13:17:51.511535Z digest=sha256:9d77a7da9af6c5549f4227683c6eed8c31a6ad635336dadf94b16a3c9414f1ad

Observation bba59302-b0c3-456d-b249-635f2829a82d · inbound

Rectified Flow: A Marginal Preserving Approach to Optimal Transport cites this paper.

Rectified Flow: A Marginal Preserving Approach to Optimal Transport Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 38

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arxiv_id, observed 2026-05-18T03:03:13.975794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-18T03:03:13.822582Z digest=sha256:e94b8cdd02c2f7540023990aa6eeba7e0dbbc7f9a9ae14d7f83ba5584110acfc

Observation 08809f5c-61c1-499b-9a82-1df9d79d33d6 · inbound

The Score-Difference Flow for Implicit Generative Modeling cites this paper.

The Score-Difference Flow for Implicit Generative Modeling Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 16

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arxiv_id, observed 2026-05-24T08:59:14.762793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-24T08:59:02.744628Z digest=sha256:c1d88efe5d700a1083aa521ca56fcb2a64c12952524b3b087421e32e63732376

Observation 5d0cddde-8336-4304-b12f-5cd605d90bf0 · inbound

DOLLAR: Few-Step Video Generation via Distillation and Latent Reward Optimization cites this paper.

DOLLAR: Few-Step Video Generation via Distillation and Latent Reward Optimization Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 64

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arxiv_id, observed 2026-05-23T07:02:41.946971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-23T06:57:50.897865Z digest=sha256:d0f9f24a3551f456db671ed574b980fd9b96790c28fd0a7996919a98cc575810

Observation 0e8e0a83-80e6-49cd-9d02-2e3516c69ad8 · inbound

Improved Training Technique for Latent Consistency Models cites this paper.

Improved Training Technique for Latent Consistency Models Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 27

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no resolver link, observed 2026-08-09T15:20:57.754574Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T15:20:57.754574Z digest=sha256:c750a69e748929f664249136795cbea6c77d98c3bcbf9d7fb6f6a325e4005799

Observation 111df363-baec-49e0-898b-d4da4cfbe5de · inbound

Conditional diffusion model with spatial attention and latent embedding for medical image segmentation cites this paper.

Conditional diffusion model with spatial attention and latent embedding for medical image segmentation Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 18

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no resolver link, observed 2026-08-08T14:11:54.445934Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T14:11:54.445934Z digest=sha256:67634601b4e6b720c4d37ce9e3743bd90020469102a8c05f6f0ea4a6d38a3fca

Observation 9ed5f373-0ae7-4bc4-a159-f55f1e5d0ac5 · inbound

IMPLICITSTAINER: Resolution Agnostic Data-Efficient Virtual Staining Using Neural Implicit Functions cites this paper.

IMPLICITSTAINER: Resolution Agnostic Data-Efficient Virtual Staining Using Neural Implicit Functions Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 29

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arxiv_id, observed 2026-05-22T14:44:55.232410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-22T14:42:28.669746Z digest=sha256:a908e5bb0bb442459747325d257bb8ca033db8f4a02e08f815cda808076a85ce

Observation a20ce9c6-56fc-40ff-a931-122c9e46c33e · inbound

Dual-Expert Consistency Model for Efficient and High-Quality Video Generation cites this paper.

Dual-Expert Consistency Model for Efficient and High-Quality Video Generation Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 61

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no resolver link, observed 2026-08-07T11:14:09.657306Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:14:09.657306Z digest=sha256:082a7c8a10b25262d5e86662cfe20f82c966e0e72cd73bf3e486470bebc1a21e

Observation 36a94131-da85-4349-8db8-d42984d445af · inbound

A Robust Local Fr\'echet Regression Using Unbalanced Neural Optimal Transport with Applications to Dynamic Single-cell Genomics Data cites this paper.

A Robust Local Fr\'echet Regression Using Unbalanced Neural Optimal Transport with Applications to Dynamic Single-cell Genomics Data Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 39

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no resolver link, observed 2026-08-07T01:09:46.416990Z

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source=arxiv_source observed=2026-08-07T01:09:46.416990Z digest=sha256:7aeac406263b0367e13d01f36b418f3f75a066f351a3a850b8201c3e58f8d52d

Observation 539aca7f-4579-4796-8a6e-6d4012b487d3 · inbound

WaFusion: A Wavelet-Enhanced Diffusion Framework for Face Morph Generation cites this paper.

WaFusion: A Wavelet-Enhanced Diffusion Framework for Face Morph Generation Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 63

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no resolver link, observed 2026-08-06T17:09:21.801522Z

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source=pdf_text observed=2026-08-06T17:09:21.801522Z digest=sha256:0cbd8e2f3f54b8daeff06fe778000d90a30a8fcbb39bc4a6f9a5c50756cd1b36

Observation b9c7931e-c766-4b5f-bf8a-d6ddaa55ca84 · inbound

fastWDM3D: Fast and Accurate 3D Healthy Tissue Inpainting cites this paper.

fastWDM3D: Fast and Accurate 3D Healthy Tissue Inpainting Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 28

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no resolver link, observed 2026-08-06T16:35:28.752722Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:35:28.752722Z digest=sha256:df1843333abb0813a3458d69ffe63ea7fe2a5a6888075f78b38984f7ea41bac7

Observation 5c295d35-cdb9-4c99-849b-64d00d4f924c · inbound

Turbulent Injection assisted by Diffusion Models for Scale Resolving Simulations cites this paper.

Turbulent Injection assisted by Diffusion Models for Scale Resolving Simulations Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 2020

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no resolver link, observed 2026-08-06T00:48:19.228197Z

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Unavailable: canonical work link unavailable.

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Observation 6da39ede-b95a-4213-959b-5bfd1185d728 · inbound

Inference Time Debiasing Concepts in Diffusion Models cites this paper.

Inference Time Debiasing Concepts in Diffusion Models Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 23

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no resolver link, observed 2026-08-05T18:47:20.825673Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:47:20.825673Z digest=sha256:808fd72a9ae9f24227c20a7d90ec10ceaded330e68abee715eacd976cf30d636

Observation 7bdbf90e-032d-431f-9e62-92b35b2e4f32 · inbound

Quantum latent distributions in deep generative models cites this paper.

Quantum latent distributions in deep generative models Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 58

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no resolver link, observed 2026-08-05T15:33:13.293815Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:33:13.293815Z digest=sha256:0253c2f06f79fb794ce0ba1f6e7cfe904941269a57fb0c5afbe7ea26944d530f

Observation 271f6304-8b54-4a8a-b82c-21f1957a3b3b · inbound

Friend or Foe cites this paper.

Friend or Foe Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 57

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no resolver link, observed 2026-08-05T14:23:13.647639Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:23:13.647639Z digest=sha256:63fefe423e17d398c403c6295881c67431eefe7ab72ee001644ec2ea5accdfa3

Observation f80a022f-8f06-4f89-93bd-2b909bf2e8ad · inbound

MiAD: Mirage Atom Diffusion for De Novo Crystal Generation cites this paper.

MiAD: Mirage Atom Diffusion for De Novo Crystal Generation Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 30

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no resolver link, observed 2026-08-03T21:42:05.372038Z

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source=pdf_text observed=2026-08-03T21:42:05.372038Z digest=sha256:458cf4e6fd503a7c7249d3a8e95abbd5dc8723ecb4bc9bbd655dcaa7806b674b

Observation 2ecd5d4b-1a6c-4aef-bc49-b849fb441aa4 · inbound

FAIL: Flow Matching Adversarial Imitation Learning for Image Generation cites this paper.

FAIL: Flow Matching Adversarial Imitation Learning for Image Generation Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 32

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no resolver link, observed 2026-08-02T23:59:17.581662Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T23:59:17.581662Z digest=sha256:112f6cfe11a37a23363de76dea641b8c20b68d50cb10564f3c9b1b94e1a6620a

Observation f80179ca-352f-43e2-9a19-4a23de118ed1 · inbound

DAG-STL: A Hierarchical Framework for Zero-Shot Trajectory Planning under Signal Temporal Logic Specifications cites this paper.

DAG-STL: A Hierarchical Framework for Zero-Shot Trajectory Planning under Signal Temporal Logic Specifications Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 80

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arxiv_id, observed 2026-05-11T12:21:03.234371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-10T03:53:37.295204Z digest=sha256:3ac8e7860a6b51fc91af782a03b761e920e4e0a019260d53e785431e2603e7c4

Observation ddd1cb09-695e-4c79-9f18-098e8482683f · inbound

Efficient Diffusion Distillation via Embedding Loss cites this paper.

Efficient Diffusion Distillation via Embedding Loss Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 30

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arxiv_id, observed 2026-05-11T19:01:19.520441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-08T12:49:42.621881Z digest=sha256:2537950004623d4468aedc6fcab96810d6fc5da3efbbe795247111a0f92771f2

Observation 09e66cc3-a120-474d-b25e-2baf9058b394 · inbound

A Systematic Benchmark of Intraoperative Ultrasound-to-MR Synthesis for Brain Tumour Surgery cites this paper.

A Systematic Benchmark of Intraoperative Ultrasound-to-MR Synthesis for Brain Tumour Surgery Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 55

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arxiv_id, observed 2026-06-28T19:22:35.247868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-28T19:03:52.766022Z digest=sha256:f74af3a9964112cdb8a4599d304419887f141d439cf0f6a04669aee753f7a1a1

Observation 7e236d92-e765-4165-b88b-eda91f87b8fe · inbound

The Reward Was in Your Data All Along: Correcting Flow Matching with Discriminator-Guided RL cites this paper.

The Reward Was in Your Data All Along: Correcting Flow Matching with Discriminator-Guided RL Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 162

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arxiv_id, observed 2026-07-04T00:49:19.327774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-26T20:54:15.234897Z digest=sha256:c0e1a84de080a4beaf05c18ca433ccd0b772f0c5ee46ae40613aadba4fdf2e39

Observation 289d70c7-9664-4fdd-ac00-7f2f4f4a4006 · inbound

Safe Few-Step Generation via Velocity Editing cites this paper.

Safe Few-Step Generation via Velocity Editing Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 27

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arxiv_id, observed 2026-07-04T10:29:45.257990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-26T08:47:29.211288Z digest=sha256:e40df95d8eaefa01565c438306453033f99ca97cf78c0d5e45e3ed59efaf067b

Observation 6b4119e8-cd42-4ae9-9c19-b151fde46af4 · inbound

Layer-Parallel Inference Reduces Encrypted Nonlinear Depth in Transformers cites this paper.

Layer-Parallel Inference Reduces Encrypted Nonlinear Depth in Transformers Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 15

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no resolver link, observed 2026-07-11T13:03:39.236118Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T13:03:39.236118Z digest=sha256:09736a9c76fe3b7e8d1b04956b59ec089f7e2a66e913f3383b7b89c4087aeb3e

Observation 93ebe841-75ab-4681-b6e2-e8e12d7b3456 · inbound

Layer-Parallel Inference Reduces Encrypted Nonlinear Depth in Transformers cites this paper.

Layer-Parallel Inference Reduces Encrypted Nonlinear Depth in Transformers Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 15

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no resolver link, observed 2026-07-14T16:21:05.570023Z

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source=arxiv_source observed=2026-07-14T16:21:05.570023Z digest=sha256:9f4bd60b8a86270a0b5884cd9b0e5c4bd194aeb2887b3cf57ab762f84977bc2b

Observation 43624415-12f6-49d1-b532-1c88ff79247d · inbound

Efficient Difficulty-Aware Dynamic Routing for Diffusion-Based Real-World Image Super-Resolution cites this paper.

Efficient Difficulty-Aware Dynamic Routing for Diffusion-Based Real-World Image Super-Resolution Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 287

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no resolver link, observed 2026-08-01T22:34:59.016446Z

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source=arxiv_source observed=2026-08-01T22:34:59.016446Z digest=sha256:b615b7d0035ac94dace3db84392666497c958b739c68c3cf44e23ae474e9a748

Observation 4b3bd8df-17fb-4530-a197-33c541c6eaad · inbound

Reinforcement Learning: From Algorithms To Foundation Models cites this paper.

Reinforcement Learning: From Algorithms To Foundation Models Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 94

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no resolver link, observed 2026-08-01T17:45:05.050814Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T17:45:05.050814Z digest=sha256:89a3cebf8df8736164ab79b1fcffcb22cb666221d6a3f1d4439b1e885f972a9c

Observation ca17a4fe-3c62-41b0-bdcd-c92d8802278e · inbound

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling cites this paper.

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 53

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no resolver link, observed 2026-08-01T12:48:19.704759Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T12:48:19.704759Z digest=sha256:e0c185e57eb56dbffa033d14fe0b32ba4e4bbaf2adc2234a48bc870f0354f81c

Observation c7cf0faf-d03f-403c-bf84-3c6363b7cd9a · inbound

Explorative Modeling: Unlocking a Third Pretraining Axis and End-to-End Generation cites this paper.

Explorative Modeling: Unlocking a Third Pretraining Axis and End-to-End Generation Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 38

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no resolver link, observed 2026-08-01T08:41:05.827379Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T08:41:05.827379Z digest=sha256:0f414ca37e2422bcb4e712d8a9a8cfab24c4947c3a6a6e9680c860b14534bd52