Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
EdgeMoE: Empowering Sparse Large Language Models on Mobile Devices
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2308.14352.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T05:19:35.734527Z
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 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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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.