Paper Citation Record · LEDGER

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design

As of 15 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:2607.24665.

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

pith.paper-citation-record.v1
2607.24665 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-31T08:31:50.218574Z

measured 51 of 51 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

51 of 51 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cac886b2-b803-45b6-86ab-744b7f9e7600 · outbound

This paper cites Outrageously large neural networks: The sparsely-gated mixture-of-experts layer,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Outrageously large neural networks: The sparsely-gated mixture-of-experts layer,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:49.474751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87484d0d-b587-4cbf-9063-5e92a3e662e8 · outbound

This paper cites GShard: Scaling giant models with condi- tional computation and automatic sharding,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design GShard: Scaling giant models with condi- tional computation and automatic sharding,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:49.634754Z

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Observation 0c09a726-ada1-4fa8-94d4-76243040c9c6 · outbound

This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:49.735178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c776fa79-3278-4220-a270-c7d8019b358b · outbound

This paper cites Mixtral of experts,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Mixtral of experts,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:49.884281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e32dc96-cba9-46b1-af87-75f70c6be119 · outbound

This paper cites MoE++: Accelerating mixture- of-experts methods with zero-computation experts,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design MoE++: Accelerating mixture- of-experts methods with zero-computation experts,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:49.984719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea96a5c2-a79b-4177-bae1-e702c95316cc · outbound

This paper cites Scalable diffusion models with transformers,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Scalable diffusion models with transformers,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.055005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ede4df91-bf68-4ac4-8ade-6706415b619f · outbound

This paper cites SiT: Exploring flow and diffusion-based generative models with scalable interpolant transformers,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design SiT: Exploring flow and diffusion-based generative models with scalable interpolant transformers,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.099852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77f8e3c0-37ae-46ff-b119-7e8ad458bff0 · outbound

This paper cites Scaling diffusion transformers to 16 billion parameters,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Scaling diffusion transformers to 16 billion parameters,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.102981Z

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Unavailable: canonical work link unavailable.

Observation a7b7ab65-0ec1-420e-b844-e0443d7ff505 · outbound

This paper cites EC-DIT: Scaling diffusion transformers with adaptive expert- choice routing,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design EC-DIT: Scaling diffusion transformers with adaptive expert- choice routing,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.105382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f5d69f8-161b-472d-94c6-ead09a5274b7 · outbound

This paper cites Diff-MoE: Diffusion transformer with time-aware and space- adaptive experts,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Diff-MoE: Diffusion transformer with time-aware and space- adaptive experts,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.107838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba2ad4d9-0d69-457e-845b-072275aba873 · outbound

This paper cites Expert race: A flexible routing strategy for scaling diffusion transformer with mixture of experts,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Expert race: A flexible routing strategy for scaling diffusion transformer with mixture of experts,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.110148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9fae13e8-ff2f-4e55-8820-cfade210a970 · outbound

This paper cites Routing matters in MoE: Scaling diffusion transformers with explicit routing guidance,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Routing matters in MoE: Scaling diffusion transformers with explicit routing guidance,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.113263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61ad410b-29e6-40bf-b19e-13f883e5ec89 · outbound

This paper cites Efficient training of diffusion mixture-of-experts models: A practical recipe,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Efficient training of diffusion mixture-of-experts models: A practical recipe,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.115653Z

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Unavailable: canonical work link unavailable.

Observation 1f1863a4-2832-4e71-94ae-61f39b04eb6a · outbound

This paper cites A convnet for the 2020s,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design A convnet for the 2020s,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.118583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8026867e-5492-4165-966b-0138ab758421 · outbound

This paper cites Denoising diffusion probabilistic models,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Denoising diffusion probabilistic models,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.121016Z

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Observation 867b26c8-a4b2-4929-8989-9f21a6aba9ab · outbound

This paper cites Score-based generative modeling through stochastic differen- tial equations,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Score-based generative modeling through stochastic differen- tial equations,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.123579Z

Source-reported events for the cited work

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Observation adb3c9d2-ed17-4e7b-a8da-1b6f5a441a4b · outbound

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

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Elucidating the design space of diffusion-based generative models,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.126246Z

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Observation 98fbb7d2-13cc-46e6-8a24-28bd66a23062 · outbound

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

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design High- resolution image synthesis with latent diffusion models,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.128957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d50817c-09d3-4d27-bd08-f89de2317b18 · outbound

This paper cites Flow matching for generative modeling,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Flow matching for generative modeling,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.131644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb90e865-d2bc-4cfe-a93b-52798ccdc781 · outbound

This paper cites Stochastic in- terpolants: A unifying framework for flows and diffusions,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Stochastic in- terpolants: A unifying framework for flows and diffusions,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.134769Z

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Observation 26eb75c1-39d5-4b5e-a671-04c7edaa1c17 · outbound

This paper cites All are worth words: A ViT backbone for diffusion models,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design All are worth words: A ViT backbone for diffusion models,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.137436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3aabbf3d-c6d9-4310-ba69-c8b67b8ea567 · outbound

This paper cites Representation alignment for generation: Training diffusion transform- ers is easier than you think,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Representation alignment for generation: Training diffusion transform- ers is easier than you think,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.140011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9a71ed7-efba-496f-bfd5-792cbdb5e584 · outbound

This paper cites MMGen: Unified Multi-modal Image Generation and Understanding in One Go.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design MMGen: Unified Multi-modal Image Generation and Understanding in One Go

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.142918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 476d6aae-665c-4ca2-8e70-23e6ff06ae20 · outbound

This paper cites Omnivdiff: Omni controllable video diffusion for generation and understanding,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Omnivdiff: Omni controllable video diffusion for generation and understanding,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.146023Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c17d106-8f2b-4f30-a92e-69e57cc79e99 · outbound

This paper cites Ctrlvdiff: Controllable video generation via unified multimodal video diffusion,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Ctrlvdiff: Controllable video generation via unified multimodal video diffusion,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.148561Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c951180d-226b-46c7-8171-df2fe4ce47a8 · outbound

This paper cites EduStory: A Unified Framework for Pedagogically-Consistent Multi-Shot STEM Instructional Video Generation.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design EduStory: A Unified Framework for Pedagogically-Consistent Multi-Shot STEM Instructional Video Generation

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.151717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ea8a7fa-4622-4438-ab4c-efc5bd958818 · outbound

This paper cites Nero: Neural geometry and brdf reconstruction of reflective objects from multiview images,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Nero: Neural geometry and brdf reconstruction of reflective objects from multiview images,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.154738Z

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Unavailable: canonical work link unavailable.

Observation 830bd369-e7bd-4f70-b6c3-658650ede3a5 · outbound

This paper cites Uni-retrieval: A multi-style retrieval framework for stem’s education,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Uni-retrieval: A multi-style retrieval framework for stem’s education,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.157439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8db100f9-5090-4edb-bfff-60fe0583e92f · outbound

This paper cites From Query to Explanation: Uni-RAG for Multi-Modal Retrieval-Augmented Learning in STEM.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design From Query to Explanation: Uni-RAG for Multi-Modal Retrieval-Augmented Learning in STEM

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.159880Z

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Unavailable: canonical work link unavailable.

Observation b73ed423-ea98-4386-91eb-b0a554babb21 · outbound

This paper cites Towards affective evaluation of stem education: Leveraging mllms in project- based learning,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Towards affective evaluation of stem education: Leveraging mllms in project- based learning,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.162671Z

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Observation dfd9c4e9-93d2-450e-a438-7edccdc88337 · outbound

This paper cites Seeing sound, hearing sight: Uncovering modality bias and conflict of ai mod- els in sound localization,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Seeing sound, hearing sight: Uncovering modality bias and conflict of ai mod- els in sound localization,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.165160Z

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Unavailable: canonical work link unavailable.

Observation 7aa41146-f5ca-413b-a5e8-6b35eca25bbb · outbound

This paper cites Senticnet 9: Generative commonsense for emotion ai via conceptual primitive discovery and time shift mechanism,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Senticnet 9: Generative commonsense for emotion ai via conceptual primitive discovery and time shift mechanism,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.167530Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 961aab8f-4f2f-4ffa-bacd-77b9475bbdb8 · outbound

This paper cites Towards spatial reasoning and understanding via modeling modality conflict, bias and alignment,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Towards spatial reasoning and understanding via modeling modality conflict, bias and alignment,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.170275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6f452a0-3651-49f2-ab9c-b4fac617807a · outbound

This paper cites GLaM: Efficient scaling of language models with mixture-of- experts,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design GLaM: Efficient scaling of language models with mixture-of- experts,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.172753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 381e6964-a82c-47b5-bc75-bedf222f9f3c · outbound

This paper cites ST-MoE: Designing stable and transferable sparse expert models,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design ST-MoE: Designing stable and transferable sparse expert models,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.175261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1372507a-5e74-4141-8abe-57bd7d4b2326 · outbound

This paper cites DeepSeekMoE: Towards ultimate expert specialization in mixture-of-experts language models,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design DeepSeekMoE: Towards ultimate expert specialization in mixture-of-experts language models,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.177902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75244cc6-1314-41a9-8274-fb778fd41c48 · outbound

This paper cites Mixture-of-experts with expert choice routing,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Mixture-of-experts with expert choice routing,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.180539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07d32d52-388b-459f-8990-0cc9f0071b75 · outbound

This paper cites DeepSeek-V3 technical report,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design DeepSeek-V3 technical report,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.183092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a297ac64-6110-41e4-ac3e-078013def1cd · outbound

This paper cites Mixture-of-depths: Dynamically allocating compute in transformer-based language models,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Mixture-of-depths: Dynamically allocating compute in transformer-based language models,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.186205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4540b41f-4ab7-46b6-a406-24a783daa3a7 · outbound

This paper cites Scaling vision with sparse mixture of experts,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Scaling vision with sparse mixture of experts,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.189000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 145d86c5-5d8a-4e5f-acdb-e3410cdd4771 · outbound

This paper cites Attention residuals,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Attention residuals,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.191648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01859a1e-52bc-4c83-8e18-295f77c8d43e · outbound

This paper cites FORGE: Fused On-Register Gradient Elimination for Memory-Efficient LLM Training.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design FORGE: Fused On-Register Gradient Elimination for Memory-Efficient LLM Training

Reference 42

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.194536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85f63cb6-3783-4d42-a05e-331ddf3cce29 · outbound

This paper cites DiffMoE: Dynamic token selection for scalable diffusion transformers,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design DiffMoE: Dynamic token selection for scalable diffusion transformers,

Reference 43

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.197823Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8587c8a-ee5d-4b79-a9b2-fe1ae8a8ae4f · outbound

This paper cites Ai flow at the network edge,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Ai flow at the network edge,

Reference 44

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.200386Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 506bfa3f-155c-4cf6-b5dd-d59d0a057f70 · outbound

This paper cites Ai flow: Perspectives, scenarios, and approaches,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Ai flow: Perspectives, scenarios, and approaches,

Reference 45

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.203232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3b72938-9870-45c1-bc7f-37ee28770b40 · outbound

This paper cites Genera- tive transmission: Rethinking computation, bandwidth, and memory in communication,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Genera- tive transmission: Rethinking computation, bandwidth, and memory in communication,

Reference 46

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.205791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 093fc216-7f31-46bd-b1df-da4975419e66 · outbound

This paper cites ImageNet: A large-scale hierarchical image database,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design ImageNet: A large-scale hierarchical image database,

Reference 47

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.208310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d6c98ca-8365-4c08-a561-a9ee508b5473 · outbound

This paper cites GANs trained by a two time-scale update rule converge to a local nash equilibrium,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design GANs trained by a two time-scale update rule converge to a local nash equilibrium,

Reference 48

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.210728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07c601ca-f772-4402-b2bc-f28efc0d652b · outbound

This paper cites Classifier-free diffusion guidance,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Classifier-free diffusion guidance,

Reference 49

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.213462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24b07bd6-4884-4ad5-848d-10cdd554cf90 · outbound

This paper cites Dynamic diffusion transformer,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Dynamic diffusion transformer,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.216000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f5c1595-312f-42eb-b5d6-0e2b70350cc3 · outbound

This paper cites Back to basics: Let denoising generative models denoise,.

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design Back to basics: Let denoising generative models denoise,

Reference 51

Resolution: unresolved; no resolver link, observed 2026-07-31T08:31:50.218574Z

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Pith citing papers

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