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Paper Citation Record · LEDGER

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models

As of 15 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2501.10714.

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

pith.paper-citation-record.v1
2501.10714 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T19:05:55.300522Z

measured 52 of 52 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

52 of 52 outbound references displayed

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  • verified fuzzy34
  • unresolved18
  • parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6eca6e43-5a8a-4632-be01-fc69e718477d · outbound

This paper cites https://developer.nvidia.com/blog/doubling-all2all- performance-with-nvidia-collective-communication-library-2-12/.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models https://developer.nvidia.com/blog/doubling-all2all- performance-with-nvidia-collective-communication-library-2-12/

Reference 1

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Observation 7ae7939d-1a8c-450f-ae8a-9912a56d556d · outbound

This paper cites Deepspeed-inference: enabling efficient infer- ence of transformer models at unprecedented scale.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Deepspeed-inference: enabling efficient infer- ence of transformer models at unprecedented scale

Reference 2

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Observation a8bf19b0-5037-4016-a15e-e11f7b484f37 · outbound

This paper cites Language models are few-shot learners.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Language models are few-shot learners

Reference 3

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Observation 0dfe1503-fe16-4ecd-9e8c-9c6b07ef8dd9 · outbound

This paper cites FLUX: Fast Software-based Communication Overlap On GPUs Through Kernel Fusion.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models FLUX: Fast Software-based Communication Overlap On GPUs Through Kernel Fusion

Reference 4

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Observation 07bbda01-9b0a-45de-a31f-80cd7be94982 · outbound

This paper cites Centauri: Enabling efficient sched- uling for communication-computation overlap in large model train- ing via communication partitioning.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Centauri: Enabling efficient sched- uling for communication-computation overlap in large model train- ing via communication partitioning

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 2c3471e6-22bb-4817-a501-5d26b5371634 · outbound

This paper cites On the representation collapse of sparse mixture of experts.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models On the representation collapse of sparse mixture of experts

Reference 6

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 91b8be49-ef8e-47b9-87da-b812022e2b72 · outbound

This paper cites Palm: Scaling language modeling with pathways.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Palm: Scaling language modeling with pathways

Reference 7

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Observation 96093689-98ff-4c7c-82c8-acc315e8ae4f · outbound

This paper cites Stablemoe: Stable routing strategy for mixture of experts.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Stablemoe: Stable routing strategy for mixture of experts

Reference 8

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation a9ed7357-2285-4a41-83fc-f6ff33610287 · outbound

This paper cites Large scale distributed deep networks.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Large scale distributed deep networks

Reference 9

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Observation 27b22620-c9b6-4b58-bc38-3cf0ad4ac1fd · outbound

This paper cites Deepseek-v2: A strong, economical, and efficient mixture-of-experts language model, 2024.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Deepseek-v2: A strong, economical, and efficient mixture-of-experts language model, 2024

Reference 10

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Observation b705c846-265e-4b57-8a8f-d58dbca2b99f · outbound

This paper cites PaLM-E: An Embodied Multimodal Language Model.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models PaLM-E: An Embodied Multimodal Language Model

Reference 11

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Observation f150b62a-8c17-4517-afe3-92c62b7d4306 · outbound

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

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity

Reference 12

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Observation e984d50e-df31-4780-aa12-a2adcb3a7427 · outbound

This paper cites FastMoE: A Fast Mixture-of-Expert Training System.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models FastMoE: A Fast Mixture-of-Expert Training System

Reference 13

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Observation 3f4daa76-9012-4571-9e41-e1ff91bc8ae1 · outbound

This paper cites FasterMoE: modeling and optimizing training of large-scale dynamic pre-trained models.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models FasterMoE: modeling and optimizing training of large-scale dynamic pre-trained models

Reference 14

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 18eda69a-b686-4d42-ba83-a3c27739cd61 · outbound

This paper cites Gpipe: Efficient training of giant neural networks using pipeline parallelism.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Gpipe: Efficient training of giant neural networks using pipeline parallelism

Reference 15

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Observation 78f4a3b7-a753-4059-98a5-2809dbed33f5 · outbound

This paper cites Experts Weights Averaging: A New General Training Scheme for Vision Transformers.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Experts Weights Averaging: A New General Training Scheme for Vision Transformers

Reference 16

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Observation 634bc3ae-50d3-473e-a721-10f5ad0a058d · outbound

This paper cites Tutel: Adaptive mixture-of-experts at scale.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Tutel: Adaptive mixture-of-experts at scale

Reference 17

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 83246816-fbbf-4c31-8cf4-f1b71791528f · outbound

This paper cites Breaking the computation and communication abstraction barrier in distributed machine learning workloads.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Breaking the computation and communication abstraction barrier in distributed machine learning workloads

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 7088d145-0197-4f83-ab97-0eee12afdced · outbound

This paper cites Highly scalable deep learning training system with mixed-precision: Training ImageNet in four minutes.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Highly scalable deep learning training system with mixed-precision: Training ImageNet in four minutes

Reference 19

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Observation e201b5e5-da61-4f10-9622-d1c5cf2c216a · outbound

This paper cites Mixtral of Experts.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Mixtral of Experts

Reference 20

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Observation fd46062d-05f2-4e73-b91b-3ebd3e54bb9d · outbound

This paper cites Lancet: Accelerating mixture-of-experts training by over- lapping weight gradient computation and all-to-all communication.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Lancet: Accelerating mixture-of-experts training by over- lapping weight gradient computation and all-to-all communication

Reference 21

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Observation 339ab4a9-001b-4c33-a978-aa7fe1876c84 · outbound

This paper cites Gshard: Scaling giant models with conditional compu- tation and automatic sharding.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Gshard: Scaling giant models with conditional compu- tation and automatic sharding

Reference 22

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation b95e5bc8-9bad-4eae-b600-eddb1a94b54b · outbound

This paper cites BASE layers: Simplifying training of large, sparse models.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models BASE layers: Simplifying training of large, sparse models

Reference 23

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Observation 8fb7b99b-61d0-48b7-882b-b28145780031 · outbound

This paper cites Acceler- ating distributed{MoE} training and inference with lina.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Acceler- ating distributed{MoE} training and inference with lina

Reference 24

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 71b22f36-7a78-4d02-b6df-3910757c8aae · outbound

This paper cites Janus: A unified dis- tributed training framework for sparse mixture-of-experts models.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Janus: A unified dis- tributed training framework for sparse mixture-of-experts models

Reference 25

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 36092380-1151-4b27-8fc5-ccf28957205b · outbound

This paper cites Gating dropout: Communication-efficient regularization for sparsely activated transformers.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Gating dropout: Communication-efficient regularization for sparsely activated transformers

Reference 26

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation a3148a43-8b3e-42db-a45f-4a910214841e · outbound

This paper cites Modeling task relationships in multi-task learning with multi- gate mixture-of-experts.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Modeling task relationships in multi-task learning with multi- gate mixture-of-experts

Reference 27

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation ee9cd83a-4fed-4246-ad23-f565fc339481 · outbound

This paper cites Bagualu: targeting brain scale pretrained models with over 37 million cores.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Bagualu: targeting brain scale pretrained models with over 37 million cores

Reference 28

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation f4051df7-4d54-4b72-a120-664d48e2a369 · outbound

This paper cites Efficient large- scale language model training on GPU clusters using Megatron-LM.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Efficient large- scale language model training on GPU clusters using Megatron-LM

Reference 29

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raw_fallback, observed 2026-08-10T19:05:55.664058Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation df6aacf6-b731-4fa8-a8e7-6483b5eca643 · outbound

This paper cites Flexmoe: Scaling large-scale sparse pre-trained model training via dynamic device placement.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Flexmoe: Scaling large-scale sparse pre-trained model training via dynamic device placement

Reference 30

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-10T19:05:55.222298Z digest=sha256:982cc6f2f407c6ecf0fbc649df03143cb1748b33379ffb9f959608e0f37ef58d

Observation 7a7c8003-d358-44a5-8c97-ee152cf73e38 · outbound

This paper cites HetuMoE: An Efficient Trillion-scale Mixture-of-Expert Distributed Training System.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models HetuMoE: An Efficient Trillion-scale Mixture-of-Expert Distributed Training System

Reference 31

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Observation 5e0c7257-5456-482f-b6bc-294482416b5e · outbound

This paper cites Springer, 1999.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Springer, 1999

Reference 32

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source=pdf_text observed=2026-08-10T19:05:55.229870Z digest=sha256:2219173e8a68c6dd2f63df8c1d7a04d29f2163b928101d566078e40c0a0726b0

Observation 1b9e643d-5a85-4770-8283-463e0c0ee288 · outbound

This paper cites Parm: Efficient training of large sparsely-activated models with dedicated schedules.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Parm: Efficient training of large sparsely-activated models with dedicated schedules

Reference 33

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raw_fallback, observed 2026-08-10T19:05:55.623764Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-10T19:05:55.233478Z digest=sha256:fcfa80f71f06920e7f9456075142c71df1be8c78a80ec02074578df7cc5d6516

Observation 637c6623-802f-4d7d-94e8-908e7294bf6b · outbound

This paper cites Sinclair.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Sinclair

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:05:55.610245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-10T19:05:55.236995Z digest=sha256:aaf23f1c6bd92d1420c63e25b5d1c45607ed195fa0a030a8c42b5764a4f479e1

Observation dcf067a7-1b92-4851-b2f1-48df2593038a · outbound

This paper cites Differential evolution.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Differential evolution

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:05:55.597154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-10T19:05:55.240768Z digest=sha256:5bdba6cdd2ef153d533c7d293e4943fa34fcd5e23b3e827d91fcf0e2c8be4aab

Observation 97fbd66a-aa5f-4700-a6e9-6a3ee8637a87 · outbound

This paper cites From Sparse to Soft Mixtures of Experts.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models From Sparse to Soft Mixtures of Experts

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-10T19:05:55.244443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:05:55.244443Z digest=sha256:bc793606a6c37e015c9e55563f6823747f043d5299cf70bcc122ff90e7aade12

Observation 9527e6fe-90cd-4b97-be65-8a0bddcc8609 · outbound

This paper cites Beckmann.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Beckmann

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:05:55.583586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-10T19:05:55.248468Z digest=sha256:465df8a987a5eecd7e51eed75573db593a6f2aacf22d0bf787f26e132ee1ae9e

Observation 63a85fec-f237-48cf-a2aa-62c79a08a610 · outbound

This paper cites Language models are unsupervised multitask learners.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Language models are unsupervised multitask learners

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-10T19:05:55.252114Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:05:55.252114Z digest=sha256:e523e761276137e1a998c4a7ba8c2f1483db7fd136159fd2b2d51b4355cc68da

Observation 936a2d2d-802f-491f-bae0-7c05ef6573c6 · outbound

This paper cites Deepspeed-moe: Advancing mixture-of-experts inference and training to power next-generation ai scale.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Deepspeed-moe: Advancing mixture-of-experts inference and training to power next-generation ai scale

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:05:55.560292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-10T19:05:55.255661Z digest=sha256:8874a42f3c0de3d63fa9d04bd332acc027ae691244fb13611c9ef742f946ed71

Observation bb688046-91ee-43d3-96bf-356c9dabf994 · outbound

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

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Outrageously large neural networks: The sparsely-gated mixture-of-experts layer

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:05:55.546293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-10T19:05:55.259055Z digest=sha256:333019bf63534a5c185e391b4f199b1e865299fe88970cf9c337548341d758cb

Observation 8829b396-86f2-4407-b2a7-edefdf954dd6 · outbound

This paper cites Exploiting simultaneous communications to accelerate data parallel distributed deep learning.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Exploiting simultaneous communications to accelerate data parallel distributed deep learning

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:05:55.533093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-10T19:05:55.263102Z digest=sha256:09800837251ec419fb3c7a311c78cf54f86bffc81cd9c306d96e5e8daf25e610

Observation 6cb210e4-3597-4aea-b8ef-2c3ed4f88006 · outbound

This paper cites PipeMoE: Ac- celerating mixture-of-experts through adaptive pipelining.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models PipeMoE: Ac- celerating mixture-of-experts through adaptive pipelining

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:05:55.518217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-10T19:05:55.266430Z digest=sha256:8ded3c00c696b3810a2250526c4eb6d21984f5e2191670826a01a13910c9a295

Observation 5ade87aa-a439-476f-97d5-c54b1ed5f19d · outbound

This paper cites Schemoe: An ex- tensible mixture-of-experts distributed training system with tasks scheduling.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Schemoe: An ex- tensible mixture-of-experts distributed training system with tasks scheduling

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:05:55.504495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-10T19:05:55.269557Z digest=sha256:e83190c3d274725db36b0c7a53671cbee48f6a4c5de7cd0a194626f688461167

Observation 3fa568d8-b289-4def-bee2-4f77473dc4e9 · outbound

This paper cites A hybrid tensor-expert- data parallelism approach to optimize mixture-of-experts training.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models A hybrid tensor-expert- data parallelism approach to optimize mixture-of-experts training

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:05:55.485399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-10T19:05:55.273159Z digest=sha256:19c820b19a02b551db5c1c26865eee96d6012623278de7b1a80082a79d6e7478

Observation e9e45f2c-f3cd-4126-b2c9-30360a6bdf08 · outbound

This paper cites Attention is all you need.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Attention is all you need

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-10T19:05:55.276366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:05:55.276366Z digest=sha256:e5ffcdc80492ddeb34df768b8c638baa9cf535ec81d2371a2ac3cee903be6516

Observation 2b2112e4-8c85-4dc1-85ab-63011375483b · outbound

This paper cites Overlap communication with dependent compu- tation via decomposition in large deep learning models.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Overlap communication with dependent compu- tation via decomposition in large deep learning models

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:05:55.468097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-10T19:05:55.279486Z digest=sha256:a6760557a012560040d35b834aaf586d7947b97179935646e3a5815c0d16e4e4

Observation d8952115-49a6-4eb3-8d5f-a75eb3d24060 · outbound

This paper cites Large batch optimization for deep learning: Training BERT in 76 minutes.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Large batch optimization for deep learning: Training BERT in 76 minutes

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:05:55.457249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-10T19:05:55.282689Z digest=sha256:b3ff63a1efc18ae76c2c5219c0cfea54733af6ef62f9206cc23e74ff54359787

Observation 3c0f0013-3795-4faa-acdd-7e56b87e99e7 · outbound

This paper cites SpeechMoE: Scaling to Large Acoustic Models with Dynamic Routing Mixture of Experts.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models SpeechMoE: Scaling to Large Acoustic Models with Dynamic Routing Mixture of Experts

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-10T19:05:55.285944Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:05:55.285944Z digest=sha256:3423e84361317ea97eda3f59c83a43c7e943514632a107cb5db3abf566c89bd1

Observation 9c5b9b8b-b453-497a-906e-b1a03ab170a3 · outbound

This paper cites SmartMoE: Efficiently training Sparsely-Activated mod- els through combining offline and online parallelization.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models SmartMoE: Efficiently training Sparsely-Activated mod- els through combining offline and online parallelization

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:05:55.446707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-10T19:05:55.289368Z digest=sha256:dcb40d207ce32d261fb01484cbc4b71ec1f0e2c943fba21412cc3c5a27ff26cc

Observation d9c12ac6-24df-4160-81c0-2d54e0dd9b73 · outbound

This paper cites Pit: Optimization of dynamic sparse deep learning models via permutation invariant transformation.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Pit: Optimization of dynamic sparse deep learning models via permutation invariant transformation

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:05:55.435346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-10T19:05:55.292629Z digest=sha256:9b8fae4870fda4a712b0775eef47e3d672bc847e7a6916220fcfebc2d4f1f567

Observation 487fa7b9-974a-45bc-9f95-830f85be3280 · outbound

This paper cites Mixture-of-Experts with Expert Choice Routing.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Mixture-of-Experts with Expert Choice Routing

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-10T19:05:55.296446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:05:55.296446Z digest=sha256:0370adc367fb3400476ec4b9962ddddc2d6350b60cb5f0446121145ccbddfdfa

Observation f4b27ede-16fc-4098-bbbe-a828271fcf63 · outbound

This paper cites Taming sparsely activated transformer with stochastic experts.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Taming sparsely activated transformer with stochastic experts

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:05:55.423266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-10T19:05:55.300522Z digest=sha256:7411266aa5880a400894db2addcd42dd6a0de96cf76353c09be2111c7e1ac401

Pith citing papers

No inbound Pith citation observations are available.