Paper Citation Record · LEDGER

The MoE-Empowered Edge LLMs Deployment: Architecture, Challenges, and Opportunities

As of 9 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 1 inbound Pith citation observation for arXiv:2502.08381.

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

pith.paper-citation-record.v1
2502.08381 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T05:19:35.760934Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

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

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:06:48.869855Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T15:06:49.316078Z

Reference resolution

14 of 14 outbound references displayed

verified exact0
verified fuzzy3
unresolved10
parse uncertain1
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3f7c392d-fe3e-4856-aee0-eaadf16f83d6 · outbound

This paper cites GPT-4 Technical Report.

The MoE-Empowered Edge LLMs Deployment: Architecture, Challenges, and Opportunities GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-08T05:19:35.704933Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4088149d-2f8c-44a6-9677-715e14901b31 · outbound

This paper cites The model compression and token compression are proposed in CoEL to address these issues.

The MoE-Empowered Edge LLMs Deployment: Architecture, Challenges, and Opportunities The model compression and token compression are proposed in CoEL to address these issues

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:19:36.001122Z

Source-reported events for the cited work

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

Observation 8cb37cd3-bd8d-4f60-8cb7-7db5ab29c444 · outbound

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

The MoE-Empowered Edge LLMs Deployment: Architecture, Challenges, and Opportunities LLaMA: Open and Efficient Foundation Language Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-08T05:19:35.709723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f2607d8-5096-4a59-a794-5d4f63846515 · outbound

This paper cites Mixtral of Experts.

The MoE-Empowered Edge LLMs Deployment: Architecture, Challenges, and Opportunities Mixtral of Experts

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-08T05:19:35.714657Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07bcb80e-f1ad-45ce-a049-237e122f5f79 · outbound

This paper cites EdgeShard: Efficient LLM Inference via Collaborative Edge Computing.

The MoE-Empowered Edge LLMs Deployment: Architecture, Challenges, and Opportunities EdgeShard: Efficient LLM Inference via Collaborative Edge Computing

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-08T05:19:35.719430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e5e6ffc-ef6f-41d2-bb08-42fafaa1df43 · outbound

This paper cites Galaxy: A Resource-Efficient Collaborative Edge AI System for In-situ Transformer Inference.

The MoE-Empowered Edge LLMs Deployment: Architecture, Challenges, and Opportunities Galaxy: A Resource-Efficient Collaborative Edge AI System for In-situ Transformer Inference

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-08T05:19:35.724310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bd45717-fdba-4e41-be04-28922b3ddacd · outbound

This paper cites On-Device Language Models: A Comprehensive Review.

The MoE-Empowered Edge LLMs Deployment: Architecture, Challenges, and Opportunities On-Device Language Models: A Comprehensive Review

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-08T05:19:35.729152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90e68a2b-1524-4d57-9d51-5548c3ad7208 · outbound

This paper cites EdgeMoE: Empowering Sparse Large Language Models on Mobile Devices.

The MoE-Empowered Edge LLMs Deployment: Architecture, Challenges, and Opportunities EdgeMoE: Empowering Sparse Large Language Models on Mobile Devices

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-08T05:19:35.734527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ede5f093-2544-45dd-841c-6737155b9164 · outbound

This paper cites WDMoE: Wireless Distributed Large Language Models with Mixture of Experts.

The MoE-Empowered Edge LLMs Deployment: Architecture, Challenges, and Opportunities WDMoE: Wireless Distributed Large Language Models with Mixture of Experts

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-08T05:19:35.739096Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9724153d-8c7a-4b1f-8e39-838775f6b7e3 · outbound

This paper cites High-speed data communication with advanced networks in large language model training,.

The MoE-Empowered Edge LLMs Deployment: Architecture, Challenges, and Opportunities High-speed data communication with advanced networks in large language model training,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:19:35.985080Z

Source-reported events for the cited work

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

Observation 66bd0a94-f79b-41ce-a6cf-45f3fb0c66f7 · outbound

This paper cites RDMA transports in datacenter networks: A survey,.

The MoE-Empowered Edge LLMs Deployment: Architecture, Challenges, and Opportunities RDMA transports in datacenter networks: A survey,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:19:35.969202Z

Source-reported events for the cited work

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

Observation b84fa4dd-a1af-40a9-a267-37d9d60a920a · outbound

This paper cites LLMCad: Fast and Scalable On-device Large Language Model Inference.

The MoE-Empowered Edge LLMs Deployment: Architecture, Challenges, and Opportunities LLMCad: Fast and Scalable On-device Large Language Model Inference

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-08T05:19:35.751961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f660f093-b257-448c-b549-4a27cb6e2981 · outbound

This paper cites Mobile Edge Intelligence for Large Language Models: A Contemporary Survey.

The MoE-Empowered Edge LLMs Deployment: Architecture, Challenges, and Opportunities Mobile Edge Intelligence for Large Language Models: A Contemporary Survey

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-08T05:19:35.760934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 942037e8-3f0c-4d36-9b30-957831e1ec5e · outbound

This paper cites an unresolved cited work.

The MoE-Empowered Edge LLMs Deployment: Architecture, Challenges, and Opportunities Unresolved cited work

Reference 2410

Resolution: parse uncertain; raw_fallback, observed 2026-08-08T05:19:35.950844Z

Source-reported events for the cited work

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

Pith citing papers

Observation f65f2a75-5228-4dc3-84b4-eca662a6016b · inbound

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges cites this paper.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges The MoE-Empowered Edge LLMs Deployment: Architecture, Challenges, and Opportunities

Reference 272

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:06:49.320471Z

Source-reported events for the cited work

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