Paper Citation Record · LEDGER

EdgeMoE: Empowering Sparse Large Language Models on Mobile Devices

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2308.14352.

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

pith.paper-citation-record.v1
2308.14352 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 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 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T21:41:15.815919Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T03:00:16.068728Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 9a453015-3fef-46f6-962f-c31f4d3d8a21 · inbound

Every Software as an Agent: Blueprint and Case Study cites this paper.

Every Software as an Agent: Blueprint and Case Study EdgeMoE: Empowering Sparse Large Language Models on Mobile Devices

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-08T21:41:15.815919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

The MoE-Empowered Edge LLMs Deployment: Architecture, Challenges, and Opportunities cites this paper.

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 a81f09ad-9963-49d8-b0d8-12bf53f0c2e2 · inbound

MoE-GPS: Guidlines for Prediction Strategy for Dynamic Expert Duplication in MoE Load Balancing cites this paper.

MoE-GPS: Guidlines for Prediction Strategy for Dynamic Expert Duplication in MoE Load Balancing EdgeMoE: Empowering Sparse Large Language Models on Mobile Devices

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:39.496695Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e1b5cc7-964e-4d72-a0c7-1ca0760e394f · inbound

Dissecting the Impact of Mobile DVFS Governors on LLM Inference Performance and Energy Efficiency cites this paper.

Dissecting the Impact of Mobile DVFS Governors on LLM Inference Performance and Energy Efficiency EdgeMoE: Empowering Sparse Large Language Models on Mobile Devices

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T20:44:17.905201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c498b3c-d2ee-4048-8edc-ecf2cb5ded22 · 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 EdgeMoE: Empowering Sparse Large Language Models on Mobile Devices

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:48.006502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8181ca5-3127-45e8-8eeb-d2c31f7b72f3 · inbound

Accelerating Edge Inference for Distributed MoE Models with Latency-Optimized Expert Placement cites this paper.

Accelerating Edge Inference for Distributed MoE Models with Latency-Optimized Expert Placement EdgeMoE: Empowering Sparse Large Language Models on Mobile Devices

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-18T22:52:51.931759Z

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 22b6556a-3882-4961-b80b-6450e186c147 · inbound

DuoServe-MoE: Dual-Phase Expert Prefetch and Caching for LLM Inference QoS Assurance cites this paper.

DuoServe-MoE: Dual-Phase Expert Prefetch and Caching for LLM Inference QoS Assurance EdgeMoE: Empowering Sparse Large Language Models on Mobile Devices

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-18T18:36:44.243794Z

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 29848277-505c-41b3-a764-c93f9547ee3c · inbound

NASiC: 3D NAND-based CAM-Selected Multibit CIM Architecture for Efficient On-Device Mixture-of-Experts LLM Inference cites this paper.

NASiC: 3D NAND-based CAM-Selected Multibit CIM Architecture for Efficient On-Device Mixture-of-Experts LLM Inference EdgeMoE: Empowering Sparse Large Language Models on Mobile Devices

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-25T03:00:16.071560Z

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 225048d6-3b1f-4300-819f-cff86c782f07 · inbound

AirMoE: Statistic-Augmented Over-the-Air MoE for Collaborative Intelligence cites this paper.

AirMoE: Statistic-Augmented Over-the-Air MoE for Collaborative Intelligence EdgeMoE: Empowering Sparse Large Language Models on Mobile Devices

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T20:38:29.015403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2b4e76c-62a7-4897-8fb0-bee4e0ae9eed · inbound

DraftExpert: Expansion-Aware Self-Speculative Decoding for End-Device MoE Inference cites this paper.

DraftExpert: Expansion-Aware Self-Speculative Decoding for End-Device MoE Inference EdgeMoE: Empowering Sparse Large Language Models on Mobile Devices

Reference 28

Resolution: unresolved; no resolver link, observed 2026-07-31T15:02:37.590572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.