Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Galaxy: A Resource-Efficient Collaborative Edge AI System for In-situ Transformer Inference
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2405.17245.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T05:19:35.724310Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-23T01:05:16.383592Z
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 2e5e6ffc-ef6f-41d2-bb08-42fafaa1df43 · inbound
The MoE-Empowered Edge LLMs Deployment: Architecture, Challenges, and Opportunities cites this paper.
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 3b8c494d-9f11-4bc9-be15-6a21ced81128 · inbound
Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices cites this paper.
Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Galaxy: A Resource-Efficient Collaborative Edge AI System for In-situ Transformer Inference
Reference 78
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T01:05:16.386504Z
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 cefeb681-f9e5-4b0f-bc37-fde6ef58283c · inbound
Ghidorah: Fast LLM Inference on Edge with Speculative Decoding and Hetero-Core Parallelism cites this paper.
Ghidorah: Fast LLM Inference on Edge with Speculative Decoding and Hetero-Core Parallelism Galaxy: A Resource-Efficient Collaborative Edge AI System for In-situ Transformer Inference
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:02:07.770435Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.