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Paper Citation Record · LEDGER

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling

As of 17 August 2026, this Paper Citation Record lists 100 of 293 outbound references and 0 inbound Pith citation observations for arXiv:2608.12276.

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

pith.paper-citation-record.v1
2608.12276 v2

Coverage vector

measured 100 of 293 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:18:29.941357Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

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

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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

100 of 293 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved100
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ae7a894b-2ca8-449d-812e-e94cf0aea9ae · outbound

This paper cites ICLR , year=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling ICLR , year=

Reference 1

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source=arxiv_source observed=2026-08-16T00:18:28.456003Z digest=sha256:4bb205ab348f5ebd9eaf210f0cb347905a9ca36d40f3573eebf6f2936bc99480

Observation c63acc0f-92c3-4626-ade5-ceaa74f2800f · outbound

This paper cites MDTv2: Masked Diffusion Transformer is a Strong Image Synthesizer.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling MDTv2: Masked Diffusion Transformer is a Strong Image Synthesizer

Reference 2

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source=arxiv_source observed=2026-08-16T00:18:28.473516Z digest=sha256:816c97e467c90fea116c5673f77740765ab66352eb516c918d88d58dccb47b37

Observation 2b90d420-33e4-49ba-96e2-e93e873dded4 · outbound

This paper cites Qwen Technical Report.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Qwen Technical Report

Reference 3

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source=arxiv_source observed=2026-08-16T00:18:28.478744Z digest=sha256:8f7ef2a47d1c6dde4cc8820c6a27e57b835f41813d41f2a7832c615cfaf5eccc

Observation 509f9680-5356-4b24-9c42-e165699b165d · outbound

This paper cites Journal of Machine Learning Research , volume=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Journal of Machine Learning Research , volume=

Reference 4

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source=arxiv_source observed=2026-08-16T00:18:28.484454Z digest=sha256:4735bb9691450d1c29a630a1ab1d3ae5af5e2429d98848586be236e1f05977b3

Observation a2d93bd2-880e-42de-bb13-b9be9b28d9f7 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Gemini: A Family of Highly Capable Multimodal Models

Reference 5

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source=arxiv_source observed=2026-08-16T00:18:28.488773Z digest=sha256:d917fd80cb7a1eefe8babda8cddacd9ce163581889234f09ce312e03275c2dea

Observation faa4df9c-26ed-4ea9-a8bb-81488081d9dc · outbound

This paper cites The Llama 3 Herd of Models.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling The Llama 3 Herd of Models

Reference 6

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source=arxiv_source observed=2026-08-16T00:18:28.493369Z digest=sha256:9196eb4118458c4e3d581de060871f74f526dc2f58828c8331acc29b448f773d

Observation 928e18ba-261f-440d-a126-1af9a5951919 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 7

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source=arxiv_source observed=2026-08-16T00:18:28.498193Z digest=sha256:1adf87f25a459a91ed8447d38d5a4c5dbda3e570538ff587cd0934bd7a0a3fb8

Observation a3d70d31-22c6-4f7d-880d-9b612783c871 · outbound

This paper cites Open-MAGVIT2: An Open-Source Project Toward Democratizing Auto-regressive Visual Generation.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Open-MAGVIT2: An Open-Source Project Toward Democratizing Auto-regressive Visual Generation

Reference 8

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source=arxiv_source observed=2026-08-16T00:18:28.503040Z digest=sha256:90f3490b45ddff8c90485b208fea77d3145565358ffc9faa98f0d09509898e86

Observation eb909a2c-2d45-4e1f-9114-ec6f56c7fc38 · outbound

This paper cites an unresolved cited work.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Unresolved cited work

Reference 9

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source=arxiv_source observed=2026-08-16T00:18:28.507310Z digest=sha256:4d6935665f0b034ae8639ca5865e8270b3a695ed1d18c77da61dd77626178f23

Observation 1ffa0eff-1adf-4711-8bc0-693dfeba00b5 · outbound

This paper cites Large Scale GAN Training for High Fidelity Natural Image Synthesis.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Large Scale GAN Training for High Fidelity Natural Image Synthesis

Reference 10

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source=arxiv_source observed=2026-08-16T00:18:28.511124Z digest=sha256:807e88aa644320e107c4e7884f01e76a50edcbcf5f4fd548b2e47f8486290645

Observation 9b7bd4d8-1800-42d6-bfdf-637d7632f6f8 · outbound

This paper cites CVPR , year=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling CVPR , year=

Reference 11

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source=arxiv_source observed=2026-08-16T00:18:28.515288Z digest=sha256:8a3202b97ba286ba9af5b8621078034228b39c9b4f31553b8ca6996c0113bc47

Observation 2042bd8a-d498-4c2e-9b39-60ef1a212655 · outbound

This paper cites CVPR , year=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling CVPR , year=

Reference 12

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source=arxiv_source observed=2026-08-16T00:18:28.539268Z digest=sha256:8f985dea58ea6e61a8368b6416715a7f0f0b001d83a03350325f601e3ae4afb4

Observation b2652670-4793-4b1b-b3b2-3e01d3b5dd29 · outbound

This paper cites ECCV , year=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling ECCV , year=

Reference 13

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source=arxiv_source observed=2026-08-16T00:18:28.563061Z digest=sha256:5d27f441864d6aeedb9c1a56bb91defa7a06d3d4cd8d43a6e4349d3826bb42f8

Observation 1571b0ee-3d9a-4079-99c8-25cc2f3f6470 · outbound

This paper cites ICCV , year=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling ICCV , year=

Reference 14

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source=arxiv_source observed=2026-08-16T00:18:28.599743Z digest=sha256:1a17080f3b06039b12572565d27e0a9cfe4fc145c6b5d57184b786a28d82579c

Observation 0ee65f3a-6fd9-49cb-bc85-5bcb2ca716e7 · outbound

This paper cites Disorder in Andreev reflection of a quantum Hall edge.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Disorder in Andreev reflection of a quantum Hall edge

Reference 15

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source=arxiv_source observed=2026-08-16T00:18:28.676279Z digest=sha256:f392ab0aa3da952264151879a78187d90941c6481bcad801346c40a38faa2c30

Observation d598f7c5-8946-4227-81b4-87e26c434e1c · outbound

This paper cites Return of Unconditional Generation: A Self-supervised Representation Generation Method , year =.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Return of Unconditional Generation: A Self-supervised Representation Generation Method , year =

Reference 16

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source=arxiv_source observed=2026-08-16T00:18:28.764201Z digest=sha256:66ccf53f15131daa7736dfac5be341f5b463e51d1c49a0aa7a9105f6cfcb9b5e

Observation d9234d8c-c048-4f22-b427-6df2d33f77ac · outbound

This paper cites Mean Flows for One-step Generative Modeling.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Mean Flows for One-step Generative Modeling

Reference 17

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source=arxiv_source observed=2026-08-16T00:18:28.767973Z digest=sha256:5f12234feb7e6240ae50554c63e48d534e6d99279814530dd7887d3ff212ef60

Observation c74f1261-69d9-40b6-b0c2-f173c98bf5c6 · outbound

This paper cites Transition Matching: Scalable and Flexible Generative Modeling.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Transition Matching: Scalable and Flexible Generative Modeling

Reference 19

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source=arxiv_source observed=2026-08-16T00:18:28.775943Z digest=sha256:10717f9f38a32104a31488257933b56e168694fea504c24a7989403a41357293

Observation 6c015523-925c-4acc-b00a-2025465ec6a6 · outbound

This paper cites CVPR , year=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling CVPR , year=

Reference 20

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source=arxiv_source observed=2026-08-16T00:18:28.780462Z digest=sha256:d87c078b511aa368adfdfb22c0d9cb0f78a290716d4e53519675692fc705ba44

Observation d5af6db2-ffad-4cb4-902a-a274d6bd18cf · outbound

This paper cites Building Normalizing Flows with Stochastic Interpolants.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Building Normalizing Flows with Stochastic Interpolants

Reference 21

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source=arxiv_source observed=2026-08-16T00:18:28.785032Z digest=sha256:22e08a2d55dcbbcefe564153f77fa01046e33477e424e71c61f0ba7aa84a8f9d

Observation efe64f62-8ed3-4634-b50e-63a2cbff0ec2 · outbound

This paper cites ICML , year=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling ICML , year=

Reference 22

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source=arxiv_source observed=2026-08-16T00:18:28.788974Z digest=sha256:7e2936d1f9b01cb634d3ae46e5ef6539470edfc5148798aaf4d5a1158eb60073

Observation 27508c57-9aa1-418e-b2ef-53dc0b8217b8 · outbound

This paper cites CVPR , year=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling CVPR , year=

Reference 23

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source=arxiv_source observed=2026-08-16T00:18:28.792590Z digest=sha256:cda594971932f2788ab62da3eb94e5cbaabc00164bd7aacc531e06d84f8cd80e

Observation b28df356-0d4a-48d0-bc49-cfc2dce27f51 · outbound

This paper cites Layer Normalization.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Layer Normalization

Reference 24

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source=arxiv_source observed=2026-08-16T00:18:28.795920Z digest=sha256:293a8ac5081258d624a37fe6108f4aad3527fd928ed5be6ac5e3d28fd108eb8e

Observation 0b6f9a9f-d791-4066-81dd-cc8bb2865e04 · outbound

This paper cites NeurIPS , year=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling NeurIPS , year=

Reference 25

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source=arxiv_source observed=2026-08-16T00:18:28.799575Z digest=sha256:af7b4dd5d91ccc6acf2ce02a080e52e7712b30a01cf47a46111527383d1660af

Observation f7ced0f6-f9af-491b-8a00-e0a33b30d5d5 · outbound

This paper cites NeurIPS , year=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling NeurIPS , year=

Reference 26

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source=arxiv_source observed=2026-08-16T00:18:28.802757Z digest=sha256:ba7c82fa8744f01e96cd2719a9a956b45a1c8644b7162c4e8cea08edbbd73a05

Observation 1fc5205b-8d1c-43ea-82f7-5381f41bfa97 · outbound

This paper cites NeurIPS , year=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling NeurIPS , year=

Reference 27

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source=arxiv_source observed=2026-08-16T00:18:28.806627Z digest=sha256:5c5bd851a96fba68e334b939c55ab4fd39fb29c2e73ffcfcb089365ffb77f7e7

Observation 6689daa9-6420-4a22-b78b-aa3f8075e5e1 · outbound

This paper cites CVPR , year=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling CVPR , year=

Reference 28

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source=arxiv_source observed=2026-08-16T00:18:28.810742Z digest=sha256:a90be8d22a11efc6c9d5c3bd54b4412ed4adafb94834af3cb80d58e8c6f358e4

Observation 3448985d-4932-48f4-b6c9-714e2cb1f507 · outbound

This paper cites Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation

Reference 29

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source=arxiv_source observed=2026-08-16T00:18:28.814374Z digest=sha256:2c3a6e76ec1967dbb51f75bff2d98584c78589e9b23816bce530718fb3f833f7

Observation 435b3d51-9819-4d71-997a-cbf97cd1f0f5 · outbound

This paper cites NeurIPS , year=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling NeurIPS , year=

Reference 30

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source=arxiv_source observed=2026-08-16T00:18:28.818559Z digest=sha256:1e746233b2a1fdb086c586ffb1eb469a34efbd5feb520a9193aa1bf11c95dfc6

Observation 14184933-fca1-44f1-a31e-0e232c6f0fac · outbound

This paper cites NeurIPS , year=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling NeurIPS , year=

Reference 31

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source=arxiv_source observed=2026-08-16T00:18:28.822872Z digest=sha256:32cbc566b39ab0387b3ab7e441ca51ce8ac48a6f326022b02c0ec2207726fd38

Observation d204a963-a574-40b2-84c6-88ecfabb0c54 · outbound

This paper cites Journal of Machine Learning Research , volume=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Journal of Machine Learning Research , volume=

Reference 32

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source=arxiv_source observed=2026-08-16T00:18:28.826414Z digest=sha256:c31636d4488bce5f35b1593a2fdcfeba1dbabbb9da8fcec5ee12e0109eb4037c

Observation 44bbe8ee-785d-4284-baba-ed05bb6cda17 · outbound

This paper cites Large-dit-imagenet , url=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Large-dit-imagenet , url=

Reference 33

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source=arxiv_source observed=2026-08-16T00:18:28.829600Z digest=sha256:b2cf3b1f011236293ec225fd4fa7255dc6df79a6f691cf9cdb33067216bc0a3d

Observation 8a0c79b7-9be6-4b03-9420-afea6c8357ad · outbound

This paper cites Scaling Autoregressive Models for Content-Rich Text-to-Image Generation.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

Reference 34

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source=arxiv_source observed=2026-08-16T00:18:28.833500Z digest=sha256:8f94fcdaea54ffa511d908f2dec90191258c20e8f60554654f6ff94fbabd3315

Observation dfe52f0a-3c19-44cc-a5a0-afda194b46e5 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 35

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source=arxiv_source observed=2026-08-16T00:18:28.893207Z digest=sha256:ca0c06593bb5260a7af3884df5d685d33cf0ee43567ed85578cf348689fa0c24

Observation 34befc36-3a4c-4f10-9f6d-20ba1650afae · outbound

This paper cites an unresolved cited work.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Unresolved cited work

Reference 36

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source=arxiv_source observed=2026-08-16T00:18:28.975783Z digest=sha256:22b301a71099fc9217fd95ff37e6f6c838362b5ea46ffd75158d166176c0c825

Observation 20eb5ceb-87dd-427b-a4dc-9b62452aa60e · outbound

This paper cites NeurIPS , year=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling NeurIPS , year=

Reference 37

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source=arxiv_source observed=2026-08-16T00:18:29.005040Z digest=sha256:c34762f8a215d7ad18c6d2ba37898b12caa5c5b2f77da70ec1c3406f94b8e329

Observation c0cf535e-446a-4f79-b98b-1e1b84fafa0b · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 38

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source=arxiv_source observed=2026-08-16T00:18:29.009048Z digest=sha256:23fd337a491f74209dfd4728c9db97f380628aade9c41ab735dac5d2cc37b385

Observation ae5be7bb-ed60-412a-9065-929c9bf6c5df · outbound

This paper cites Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 39

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source=arxiv_source observed=2026-08-16T00:18:29.013098Z digest=sha256:82d5e910ea62864c5af2c270cd00385faed4f994b4e2870957c13112428c67b4

Observation 09e3fbd1-2c55-4e63-8f6b-008dd57662ed · outbound

This paper cites Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Reference 40

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Observation eb467768-e22c-42c1-b435-f51b9a65914c · outbound

This paper cites Autoregressive Pretraining with Mamba in Vision.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Autoregressive Pretraining with Mamba in Vision

Reference 41

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source=arxiv_source observed=2026-08-16T00:18:29.021500Z digest=sha256:e8c12106dc85d851d3726a668dd133df505cd1047f44f810eed766def52790a0

Observation 0d56c45b-373a-4236-948b-07a0a0d4f5ca · outbound

This paper cites Dimba: Transformer-Mamba Diffusion Models.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Dimba: Transformer-Mamba Diffusion Models

Reference 42

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source=arxiv_source observed=2026-08-16T00:18:29.025359Z digest=sha256:31556dec0577f331fe62ec511f1917a85ec9b1243e640346ab0f5bc28b3164eb

Observation 70dae6f5-34a5-4149-b5f0-f9d655dfad19 · outbound

This paper cites an unresolved cited work.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Unresolved cited work

Reference 43

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source=arxiv_source observed=2026-08-16T00:18:29.030244Z digest=sha256:cdd4fdf0073fb46f6ec4ffa1f51870169e3eab365c273e19d74c3c98069914bb

Observation 5cb83cac-2a5a-40ab-9b7e-fb4a1834950d · outbound

This paper cites an unresolved cited work.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Unresolved cited work

Reference 44

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source=arxiv_source observed=2026-08-16T00:18:29.034426Z digest=sha256:bc49ff00f2917480b18d7dba3796e9bf70ff20a11915027e4ce510a23d32632e

Observation 3d0f1e2d-657d-4a4c-a44d-3ec8e5b64d00 · outbound

This paper cites an unresolved cited work.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Unresolved cited work

Reference 45

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source=arxiv_source observed=2026-08-16T00:18:29.039443Z digest=sha256:131f092cea8bdbc62bbe8ac085e92364d1d339e816b595057867cbe3143d8638

Observation 2405d889-2a8b-470d-ad29-1d7fcb945e1e · outbound

This paper cites an unresolved cited work.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Unresolved cited work

Reference 46

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source=arxiv_source observed=2026-08-16T00:18:29.044271Z digest=sha256:450ca146b21a9e8132a5a237f07570ba02693011e8fbf96ae85ac25e802b19bc

Observation 6f6fe7e4-0c92-4b1d-bedb-b54939b4fea1 · outbound

This paper cites Neural computation , volume=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Neural computation , volume=

Reference 47

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source=arxiv_source observed=2026-08-16T00:18:29.048152Z digest=sha256:9800491fe66998a2a9d8be436c4455b406c9d7de58c88f0f51ea4965ee2f2290

Observation 7dd1c9c9-d542-4e8c-b08f-c2c0c61bc234 · outbound

This paper cites Randomized Autoregressive Visual Generation.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Randomized Autoregressive Visual Generation

Reference 48

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source=arxiv_source observed=2026-08-16T00:18:29.052398Z digest=sha256:c144434ad6e3d7a5ce6b4ebc29131fa9b04c483e8fa2f7e4b1aa77a74bccdc35

Observation d8c9527c-99fe-4ade-8a0d-835f1dd4e676 · outbound

This paper cites Vector-quantized Image Modeling with Improved VQGAN.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Vector-quantized Image Modeling with Improved VQGAN

Reference 49

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source=arxiv_source observed=2026-08-16T00:18:29.056475Z digest=sha256:db7abdeead422e430d1f130d61909aa54b58d33fdacbd531f744f6b9db7c7e79

Observation 5aa15114-c872-46cf-8f7b-0af03e50f561 · outbound

This paper cites an unresolved cited work.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Unresolved cited work

Reference 50

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source=arxiv_source observed=2026-08-16T00:18:29.059771Z digest=sha256:395560a76b7fed07d1c4a587dec10120d7fd42f159da7310456102a899d38bbf

Observation 3314cc77-eeb5-4ce7-8247-b5b9a7aedf0b · outbound

This paper cites 2018 , howpublished=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling 2018 , howpublished=

Reference 51

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source=arxiv_source observed=2026-08-16T00:18:29.063504Z digest=sha256:1a046702945f3fd2afd6afa7553e6b22226dc50745311a9aa4f6d6be76eba582

Observation 4aca7c08-a9fb-4191-85a5-bd024e6f74dc · outbound

This paper cites 2020 , url=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling 2020 , url=

Reference 52

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source=arxiv_source observed=2026-08-16T00:18:29.067770Z digest=sha256:24db74752aaca020de2355bd4cf6a0efb53c7024c6d5057e2a6857dbd72683d9

Observation 26b9000e-fe0e-4d51-8878-bb047c9482c7 · outbound

This paper cites an unresolved cited work.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Unresolved cited work

Reference 53

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source=arxiv_source observed=2026-08-16T00:18:29.071669Z digest=sha256:c961d861c2e1afbf1dbd034ccd4dd6ab636b714a322b2cb01d39aa63ebbb7d6d

Observation d7c819f3-df6c-49ff-b1c5-33a7720dfdfe · outbound

This paper cites 2022 , howpublished =.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling 2022 , howpublished =

Reference 54

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source=arxiv_source observed=2026-08-16T00:18:29.075348Z digest=sha256:4bb910c712ab5fe5b50f9c3a3177983a12abf7a39e3e06acfa49ad574d7705b6

Observation 78120551-c619-4749-a8b9-3b282aef6543 · outbound

This paper cites GPT-4 Technical Report.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling GPT-4 Technical Report

Reference 55

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source=arxiv_source observed=2026-08-16T00:18:29.117764Z digest=sha256:f433145e298fed4128221ec4555cdc70b3431efbc83e37a256fad6cc67f9d836

Observation 501a74a1-60db-4381-8605-30e8d86092ae · outbound

This paper cites Proceedings of the IEEE , volume=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Proceedings of the IEEE , volume=

Reference 56

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source=arxiv_source observed=2026-08-16T00:18:29.221419Z digest=sha256:da13094319c18399727a90707422180603a1fc3bb936dacbfbaef3a16b62d386

Observation 206afeae-32e9-4529-b20c-15ebbfd3b2b8 · outbound

This paper cites an unresolved cited work.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Unresolved cited work

Reference 57

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source=arxiv_source observed=2026-08-16T00:18:29.292674Z digest=sha256:682c2eac7b94848d64384a190e95f8d74b95da680c3fec0b1b18d3a2ad21d7ea

Observation f93204bb-ba84-4060-8255-57bfcabf357a · outbound

This paper cites arXiv preprint arXiv:2408.12245 , year=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling arXiv preprint arXiv:2408.12245 , year=

Reference 58

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source=arxiv_source observed=2026-08-16T00:18:29.296748Z digest=sha256:006a093a27ec83e094ddeecef614a5298801354a823da16021a7f431395c9f23

Observation 280ba9de-734f-48ad-a934-a2e904e4e438 · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Efficiently Modeling Long Sequences with Structured State Spaces

Reference 59

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source=arxiv_source observed=2026-08-16T00:18:29.301546Z digest=sha256:bb93fadc357a823c3c4e86353fa63d52c19b95585d029db08cf4c2aa565935c9

Observation bdc1b419-a128-470c-add1-ff4339257003 · outbound

This paper cites an unresolved cited work.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Unresolved cited work

Reference 60

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source=arxiv_source observed=2026-08-16T00:18:29.306356Z digest=sha256:d0f90d7a11d20a18c9b044130d5200d3b671b82a01dbd9320e7e66c44e458565

Observation 47319e62-a617-4405-a693-fa08c047d5bd · outbound

This paper cites Long Range Language Modeling via Gated State Spaces.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Long Range Language Modeling via Gated State Spaces

Reference 61

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source=arxiv_source observed=2026-08-16T00:18:29.310336Z digest=sha256:6d218f898c90784e09e6515080d7b990da02c7cd200ea861cc9578d07599baa1

Observation 71b784e7-905a-4b34-8a3e-5318563eb066 · outbound

This paper cites How to Train Your HiPPO: State Space Models with Generalized Orthogonal Basis Projections.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling How to Train Your HiPPO: State Space Models with Generalized Orthogonal Basis Projections

Reference 62

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source=arxiv_source observed=2026-08-16T00:18:29.315247Z digest=sha256:79bc7c79d62f32f6a0e50e2ee8c3346b157736601f5c309d379f795941da7e74

Observation 6f80e33d-6e15-4100-96e6-ccababf3f677 · outbound

This paper cites an unresolved cited work.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Unresolved cited work

Reference 63

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source=arxiv_source observed=2026-08-16T00:18:29.319039Z digest=sha256:1421767da1eb7d1e58b820324909c2aa5d799c60d4da105690834f89ab17038b

Observation cf77b03d-0187-4d44-9fde-b398aa542877 · outbound

This paper cites MaskBit: Embedding-free Image Generation via Bit Tokens.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling MaskBit: Embedding-free Image Generation via Bit Tokens

Reference 64

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source=arxiv_source observed=2026-08-16T00:18:29.322865Z digest=sha256:0d52f6144c2ae3aa7502d9de6c4d38fcb232380a645344625b950320484910f8

Observation 393cfc63-53ab-431c-bba9-f7580a73b53a · outbound

This paper cites an unresolved cited work.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Unresolved cited work

Reference 65

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source=arxiv_source observed=2026-08-16T00:18:29.327255Z digest=sha256:bb4e3d57a842cc9312b9d39a80c3ec1968a17a441076cc3bb1b106d473209c61

Observation 9df297d6-28bf-43d8-993d-bdfd5803cb5f · outbound

This paper cites Parallelized Autoregressive Visual Generation.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Parallelized Autoregressive Visual Generation

Reference 66

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source=arxiv_source observed=2026-08-16T00:18:29.331175Z digest=sha256:19c7e9f930b82e97933a8872d5ee384fc882d40e13000730506ece1710a4bd93

Observation 7fc783a5-5071-4fc6-a3a8-54f38fc58260 · outbound

This paper cites and Ermon, Stefano and Rudra, Atri and R.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling and Ermon, Stefano and Rudra, Atri and R

Reference 67

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source=arxiv_source observed=2026-08-16T00:18:29.334902Z digest=sha256:e9862bc9d61e26a8ea1bf2b0b8deff2d6e4bf0ef4490f00495915c036c509ffd

Observation 58b8013b-2732-48de-8512-4a154975c323 · outbound

This paper cites an unresolved cited work.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Unresolved cited work

Reference 68

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source=arxiv_source observed=2026-08-16T00:18:29.338642Z digest=sha256:e68b4e760dc79245535adfae0dc6ff88f01833fb6888e53e1abfa9873d323926

Observation 57363b52-a683-4df5-907e-7b3460d37e33 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling LLaMA: Open and Efficient Foundation Language Models

Reference 69

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source=arxiv_source observed=2026-08-16T00:18:29.396792Z digest=sha256:cba55461a943184b9c45d72ae0d4a9a86114669653c6bc598d03f1fd5fb93619

Observation d942c78c-9049-4fd5-b536-e37a15d15444 · outbound

This paper cites International Conference on Artificial Intelligence and Statistics , year=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling International Conference on Artificial Intelligence and Statistics , year=

Reference 70

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source=arxiv_source observed=2026-08-16T00:18:29.462117Z digest=sha256:f0bc0c7319ba38ba068e65ce5a3efc9d5ba220fa46ff2380abfd599d8a41d479

Observation 6b7ec73f-4fed-4606-96dd-c46391d848ef · outbound

This paper cites Discriminator Rejection Sampling.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Discriminator Rejection Sampling

Reference 71

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source=arxiv_source observed=2026-08-16T00:18:29.466158Z digest=sha256:11c6b718315170ed0accc7f8ab96425291b1db2272b9f8fcedf292f6d71e4161

Observation d69d7c4f-8c30-4cbe-a485-b88502a7df90 · outbound

This paper cites an unresolved cited work.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Unresolved cited work

Reference 72

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source=arxiv_source observed=2026-08-16T00:18:29.471253Z digest=sha256:f671e5df6786b30cd9c3cd487692d79e96c9012459b7482e59b973d4c811e37c

Observation 2d04fc78-55bc-4cb4-9450-ef959d51de5f · outbound

This paper cites NeurIPS , year=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling NeurIPS , year=

Reference 73

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source=arxiv_source observed=2026-08-16T00:18:29.475684Z digest=sha256:4622d1370d623e061dfd4bfc85e4b0970ae156d10b12bbd1f706d53647e85d3c

Observation 0b501c37-b3b1-49d3-9527-e760b892b5fa · outbound

This paper cites Denoising Diffusion Implicit Models.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Denoising Diffusion Implicit Models

Reference 74

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source=arxiv_source observed=2026-08-16T00:18:29.480256Z digest=sha256:3e990fe1af87dc3b1d03836bc7062c26a3537099f05fc7558f651c0af61fefc4

Observation 8f740279-487d-4e15-897b-3d3ba9241f85 · outbound

This paper cites an unresolved cited work.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Unresolved cited work

Reference 75

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source=arxiv_source observed=2026-08-16T00:18:29.486233Z digest=sha256:9763048b425de57fd79ea92cfa0287aa2595ac852ca803c92cf3775e8466fb6e

Observation 89e9c131-4e14-48a2-9f3e-3f58bd473645 · outbound

This paper cites ICML , year=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling ICML , year=

Reference 76

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source=arxiv_source observed=2026-08-16T00:18:29.526484Z digest=sha256:3c971f86bf4982f722e6b3c46afbf181ce36469b7157dd979a3032cea7680db2

Observation 7a2546bf-5597-40c4-a393-0106314ac3de · outbound

This paper cites an unresolved cited work.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Unresolved cited work

Reference 77

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source=arxiv_source observed=2026-08-16T00:18:29.570513Z digest=sha256:b855455239d6a51da3a9fc3632920dda39896e2b9ea2495ef67ef22027a172d6

Observation 3f45680d-a785-4969-995c-b1a2d5af13ca · outbound

This paper cites MonoFormer: One Transformer for Both Diffusion and Autoregression.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling MonoFormer: One Transformer for Both Diffusion and Autoregression

Reference 78

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source=arxiv_source observed=2026-08-16T00:18:29.613783Z digest=sha256:e4aabc6ab61f09204248ad32ec797aa2c7824c83c6ffc30f90bec6831bae2985

Observation 07058ce1-9d23-4cbb-be42-d132e0fb4554 · outbound

This paper cites ICLR , year=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling ICLR , year=

Reference 79

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source=arxiv_source observed=2026-08-16T00:18:29.618474Z digest=sha256:4ccc2dccc276d4e68011d842c4240282e366d1c96233521a4cfad5145732dd1f

Observation 9bb8db5c-2e5a-438f-9886-280925bbeeed · outbound

This paper cites Causal Diffusion Transformers for Generative Modeling.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Causal Diffusion Transformers for Generative Modeling

Reference 80

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source=arxiv_source observed=2026-08-16T00:18:29.622519Z digest=sha256:e3f34b11958c330d7653665f7f525adb7079d3c93f6a2fc73e90f82b4805c452

Observation 80c73408-bda4-44a6-8007-2e211e27ecce · outbound

This paper cites ECCV , year=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling ECCV , year=

Reference 81

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Observation 0e5c7cda-c69e-4372-ad07-672e4c8ec00f · outbound

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XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling MICCAI , year=

Reference 82

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Observation 7bc6196f-ddbb-4034-aed4-3936a875fd3c · outbound

This paper cites Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models

Reference 83

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Observation f8d90475-aa67-4be3-8df4-c3e9eb2cca18 · outbound

This paper cites Proceedings of Machine Learning and Systems , volume=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Proceedings of Machine Learning and Systems , volume=

Reference 84

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Observation 5663f742-a6a3-49fb-9c7f-91226002f6cd · outbound

This paper cites 2002 , publisher=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling 2002 , publisher=

Reference 85

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Observation d0810b2e-97c9-4f0a-a459-44333a8a420b · outbound

This paper cites NeurIPS , year=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling NeurIPS , year=

Reference 86

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Observation 1b739a28-e5d2-4f43-a1a9-94885e060fc3 · outbound

This paper cites Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation

Reference 87

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Observation 555acbd6-cdb7-4ea1-8cd2-f67580410e5a · outbound

This paper cites an unresolved cited work.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Unresolved cited work

Reference 88

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Observation 1c55581a-c32a-4690-a494-50a0592c16c0 · outbound

This paper cites an unresolved cited work.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Unresolved cited work

Reference 89

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Observation 69c618fc-42e7-446e-a049-0ffec0bd4bdc · outbound

This paper cites Simpler Diffusion (SiD2): 1.5 FID on ImageNet512 with pixel-space diffusion.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Simpler Diffusion (SiD2): 1.5 FID on ImageNet512 with pixel-space diffusion

Reference 90

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source=arxiv_source observed=2026-08-16T00:18:29.799380Z digest=sha256:c9950ef6437dccfb959bec436404eca0255a5411315740b85d11208a9b96a40d

Observation 1629ab7f-faec-4ccc-855b-d9d43f81c017 · outbound

This paper cites Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think

Reference 91

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source=arxiv_source observed=2026-08-16T00:18:29.803412Z digest=sha256:11c2f230f3b288c27bff4c4f3f76d5d0c59ccdfca41e27b84130fe384aae99a1

Observation 299aafc6-be85-449a-aa3a-071e93332035 · outbound

This paper cites Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 92

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source=arxiv_source observed=2026-08-16T00:18:29.808278Z digest=sha256:2cfe8acb673658f65c4ef6234123d9b8d131042da6e6a60451f9dc017eecae3b

Observation 58b9dccb-01a1-4550-ab60-af422db73e26 · outbound

This paper cites Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models

Reference 93

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Observation ab8a5693-e725-4f9d-87df-4302b29e93c7 · outbound

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XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling ICCV , year=

Reference 94

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Observation 648619f1-ab95-45b5-84a9-24ef16b52159 · outbound

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

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling DINOv2: Learning Robust Visual Features without Supervision

Reference 95

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Observation 72569ace-1ae2-401d-a4b3-c30c67dd0731 · outbound

This paper cites ICML , year=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling ICML , year=

Reference 96

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Observation 49d31b32-b6f8-4c6b-add5-26564605184a · outbound

This paper cites CVPR , year=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling CVPR , year=

Reference 97

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source=arxiv_source observed=2026-08-16T00:18:29.924977Z digest=sha256:fe947082884ba2950e0aef6715b2db0326cc8230a09516208a289865c1f873e2

Observation fecac4af-7170-40f9-b198-f58c7e227285 · outbound

This paper cites CVPR , year=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling CVPR , year=

Reference 98

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source=arxiv_source observed=2026-08-16T00:18:29.929173Z digest=sha256:0be8a6244d7711e0142b03a670065e92c16a0559e59bf06dc373949c3beaa63d

Observation 0d3de23d-3765-4046-a369-a7b0711b2cd9 · outbound

This paper cites CVPR , year=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling CVPR , year=

Reference 99

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source=arxiv_source observed=2026-08-16T00:18:29.933499Z digest=sha256:21e7192b5bf329215cad646586a3d3bf3a202a64f1ae4cd3711cfe8f098266ae

Observation 8327ddd5-1575-43a1-981a-4b60c9bbc248 · outbound

This paper cites CVPR , year=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling CVPR , year=

Reference 100

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Observation ec114d58-d2e3-4e08-8ada-3b17f7b379f8 · outbound

This paper cites CVPR , year=.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling CVPR , year=

Reference 101

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source=arxiv_source observed=2026-08-16T00:18:29.941357Z digest=sha256:f7845f89fb08a3d396e5d4f1271c0e39d4021d0a4db06b5d075eccce187ec954

Pith citing papers

No inbound Pith citation observations are available.