Paper Citation Record · LEDGER

Theory of Mixture-of-Experts for Mobile Edge Computing

As of 15 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 1 inbound Pith citation observation for arXiv:2412.15690.

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

pith.paper-citation-record.v1
2412.15690 v2

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:16:24.903857Z

measured 34 of 34 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:13:14.831668Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T20:13:15.098982Z

Reference resolution

33 of 33 outbound references displayed

verified exact0
verified fuzzy26
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6dfa9a8a-043f-4b13-9bfe-dac832aaf5b2 · outbound

This paper cites Distributed machine learning for multiuser mobile edge computing systems,.

Theory of Mixture-of-Experts for Mobile Edge Computing Distributed machine learning for multiuser mobile edge computing systems,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:16:25.322189Z

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.

Observation 67fa123e-a250-4166-b01f-9c44dc30541a · outbound

This paper cites Federated learning for online resource allocation in mobile edge computing: A deep reinforcement learning approach,.

Theory of Mixture-of-Experts for Mobile Edge Computing Federated learning for online resource allocation in mobile edge computing: A deep reinforcement learning approach,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:16:25.310952Z

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.

Observation 77641dc9-414c-4c15-b27e-dc0bccbeb05d · outbound

This paper cites Follow me at the edge: Mobility- aware dynamic service placement for mobile edge computing,.

Theory of Mixture-of-Experts for Mobile Edge Computing Follow me at the edge: Mobility- aware dynamic service placement for mobile edge computing,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:16:25.299572Z

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.

Observation e9b05f66-7fc1-4d8d-9dc0-387d7b86645f · outbound

This paper cites A survey on the computation offloading approaches in mobile edge computing: A machine learning-based perspective,.

Theory of Mixture-of-Experts for Mobile Edge Computing A survey on the computation offloading approaches in mobile edge computing: A machine learning-based perspective,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:16:25.287421Z

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.

Observation 76dc1592-9edc-4d25-90b5-7bc94b7886af · outbound

This paper cites Winning at the starting line: Joint network selection and service placement for mobile edge computing,.

Theory of Mixture-of-Experts for Mobile Edge Computing Winning at the starting line: Joint network selection and service placement for mobile edge computing,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:16:25.273635Z

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.

Observation 878769d9-0234-44a9-b4c6-eef791bf61de · outbound

This paper cites Pricing-driven service caching and task offloading in mobile edge computing,.

Theory of Mixture-of-Experts for Mobile Edge Computing Pricing-driven service caching and task offloading in mobile edge computing,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:16:25.260973Z

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.

Observation de564afb-b100-4349-9707-484b5e1dd982 · outbound

This paper cites Catastrophic interference in connection- ist networks: The sequential learning problem,.

Theory of Mixture-of-Experts for Mobile Edge Computing Catastrophic interference in connection- ist networks: The sequential learning problem,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:16:25.247515Z

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.

Observation d5b24094-441d-4b90-8f3c-42f01e053d45 · outbound

This paper cites Overcoming catastrophic forgetting in neural networks,.

Theory of Mixture-of-Experts for Mobile Edge Computing Overcoming catastrophic forgetting in neural networks,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:16:25.232921Z

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.

Observation 10c54755-f96e-463c-ba93-f12181e59a2a · outbound

This paper cites Beyond not-forgetting: Con- tinual learning with backward knowledge transfer,.

Theory of Mixture-of-Experts for Mobile Edge Computing Beyond not-forgetting: Con- tinual learning with backward knowledge transfer,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:16:25.220927Z

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.

Observation 8a1aa3a5-0b95-4b8a-928d-0a0e12bb193b · outbound

This paper cites Inference for the generalization error,.

Theory of Mixture-of-Experts for Mobile Edge Computing Inference for the generalization error,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:16:25.208659Z

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.

Observation ca2535ac-7ce9-4780-a2a5-fec1538906ee · outbound

This paper cites Generalization error of ensemble estima- tors,.

Theory of Mixture-of-Experts for Mobile Edge Computing Generalization error of ensemble estima- tors,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:16:25.195355Z

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.

Observation 174a5004-b3af-41b1-80e5-0e8160bbdb27 · outbound

This paper cites Learning Factored Representations in a Deep Mixture of Experts.

Theory of Mixture-of-Experts for Mobile Edge Computing Learning Factored Representations in a Deep Mixture of Experts

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T11:16:24.822684Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62dfe6bd-d934-4545-bebd-456205564390 · outbound

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

Theory of Mixture-of-Experts for Mobile Edge Computing Outrageously large neural networks: The sparsely-gated mixture-of-experts layer,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:16:25.182826Z

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.

Observation a6c8f591-590b-4132-8d0e-1a9446f5248c · outbound

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

Theory of Mixture-of-Experts for Mobile Edge Computing Scaling vision with sparse mixture of experts,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:16:25.170740Z

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.

Observation d30974f0-4682-404c-b41d-ad1c688b424b · outbound

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

Theory of Mixture-of-Experts for Mobile Edge Computing Glam: Efficient scaling of language models with mixture-of-experts,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:16:25.157333Z

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.

Observation 729269b9-b813-405c-bdd6-3ae74edb0a3a · outbound

This paper cites Megablocks: Ef- ficient sparse training with mixture-of-experts,.

Theory of Mixture-of-Experts for Mobile Edge Computing Megablocks: Ef- ficient sparse training with mixture-of-experts,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:16:25.144923Z

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.

Observation 9ecd8a49-e847-40df-b748-c7862c71c3e2 · outbound

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

Theory of Mixture-of-Experts for Mobile Edge Computing Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T11:16:24.842356Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ad49c4c-8a35-484f-a83d-12596e44cf0c · outbound

This paper cites Towards understanding the mixture-of-experts layer in deep learning,.

Theory of Mixture-of-Experts for Mobile Edge Computing Towards understanding the mixture-of-experts layer in deep learning,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:16:25.125519Z

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.

Observation 81d0465a-942d-4576-9832-4300b860c67e · outbound

This paper cites Theory on Mixture-of-Experts in Continual Learning.

Theory of Mixture-of-Experts for Mobile Edge Computing Theory on Mixture-of-Experts in Continual Learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T11:16:24.849303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1419baa-ce73-4483-8eb7-7adcff519976 · outbound

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

Theory of Mixture-of-Experts for Mobile Edge Computing Deepspeed-moe: Advancing mixture-of-experts inference and training to power next-generation ai scale,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:16:25.112255Z

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.

Observation 81105a87-c8d4-47a7-b6bd-c067772bccd5 · outbound

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

Theory of Mixture-of-Experts for Mobile Edge Computing A hybrid tensor-expert-data parallelism approach to optimize mixture- of-experts training,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:16:25.099022Z

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.

Observation 4c58dc49-8bae-4c80-a123-87bee4bd5391 · outbound

This paper cites Toward Scalable Generative AI via Mixture of Experts in Mobile Edge Networks.

Theory of Mixture-of-Experts for Mobile Edge Computing Toward Scalable Generative AI via Mixture of Experts in Mobile Edge Networks

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T11:16:24.861773Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c84c2c69-f272-4ad2-87a7-52e857b47d8d · outbound

This paper cites Moesys: A distributed and efficient mixture-of-experts training and inference system for internet services,.

Theory of Mixture-of-Experts for Mobile Edge Computing Moesys: A distributed and efficient mixture-of-experts training and inference system for internet services,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:16:25.087031Z

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.

Observation ee94e83d-cc4d-46ba-95ee-2a9b6066bf27 · outbound

This paper cites Coscl: Cooperation of small continual learners is stronger than a big one,.

Theory of Mixture-of-Experts for Mobile Edge Computing Coscl: Cooperation of small continual learners is stronger than a big one,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T11:16:24.869580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 928c7ab5-b968-49fd-8271-e11401bda78c · outbound

This paper cites Joint planning of mec and fiber deployment in sparsely populated areas,.

Theory of Mixture-of-Experts for Mobile Edge Computing Joint planning of mec and fiber deployment in sparsely populated areas,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:16:25.064305Z

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.

Observation e64d3368-6676-44e7-bdcc-3de188b4db26 · outbound

This paper cites LocMoE: A Low-Overhead MoE for Large Language Model Training.

Theory of Mixture-of-Experts for Mobile Edge Computing LocMoE: A Low-Overhead MoE for Large Language Model Training

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T11:16:24.876900Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3e602c2-5ff7-47d0-873c-280f808f6f0c · outbound

This paper cites A theoretical understanding of shallow vision transformers: Learning, generalization, and sample complexity,.

Theory of Mixture-of-Experts for Mobile Edge Computing A theoretical understanding of shallow vision transformers: Learning, generalization, and sample complexity,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:16:25.049992Z

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.

Observation ee484205-a928-4e4f-9972-8d6b02cdc5ae · outbound

This paper cites How catastrophic can catastrophic forgetting be in linear regression?.

Theory of Mixture-of-Experts for Mobile Edge Computing How catastrophic can catastrophic forgetting be in linear regression?

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:16:25.035143Z

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.

Observation 9a98f142-8c95-4890-9d3e-1d3be86cc60f · outbound

This paper cites Theory on forgetting and generalization of continual learning,.

Theory of Mixture-of-Experts for Mobile Edge Computing Theory on forgetting and generalization of continual learning,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:16:25.022800Z

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.

Observation 879a4152-0dea-4395-8bb4-fa9713c5b0b4 · outbound

This paper cites Characterizing implicit bias in terms of optimization geometry,.

Theory of Mixture-of-Experts for Mobile Edge Computing Characterizing implicit bias in terms of optimization geometry,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:16:25.010589Z

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.

Observation 728a8e8c-c811-4287-830d-d54060d724ec · outbound

This paper cites Efficient lifelong learning with a-gem,.

Theory of Mixture-of-Experts for Mobile Edge Computing Efficient lifelong learning with a-gem,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:16:24.998644Z

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.

Observation 7f4a60fe-89f3-4cb9-a57c-eadaa98d61f8 · outbound

This paper cites A theoretical analysis of catastrophic forgetting through the ntk overlap ma- trix,.

Theory of Mixture-of-Experts for Mobile Edge Computing A theoretical analysis of catastrophic forgetting through the ntk overlap ma- trix,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:16:24.986116Z

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.

Observation 2897b1a5-fb48-4633-a0f1-6264a8551be2 · outbound

This paper cites Handwritten digit recognition with a back-propagation network,.

Theory of Mixture-of-Experts for Mobile Edge Computing Handwritten digit recognition with a back-propagation network,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T11:16:24.903857Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation c233a7bb-6a5a-4f5d-924e-b347de4f1b2a · inbound

MoE$^2$: Optimizing Collaborative Inference for Edge Large Language Models cites this paper.

MoE$^2$: Optimizing Collaborative Inference for Edge Large Language Models Theory of Mixture-of-Experts for Mobile Edge Computing

Reference 80

Resolution: verified exact; local_arxiv, observed 2026-08-10T20:13:15.106170Z

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.