Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
SwapMoE: Serving Off-the-shelf MoE-based Large Language Models with Tunable Memory Budget
As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2308.15030.
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 2026-08-06T06:34:29.942622+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-04T21:51:05.092526Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T16:36:06.598162Z
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 b39b1e94-6c3d-4c17-9851-c95924dd86fc · inbound
MoE-Compression: How the Compression Error of Experts Affects the Inference Accuracy of MoE Model? cites this paper.
MoE-Compression: How the Compression Error of Experts Affects the Inference Accuracy of MoE Model? SwapMoE: Serving Off-the-shelf MoE-based Large Language Models with Tunable Memory Budget
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T21:51:05.092526Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bf7cb693-3216-476d-aae0-c18d55c558fc · inbound
A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs cites this paper.
A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs SwapMoE: Serving Off-the-shelf MoE-based Large Language Models with Tunable Memory Budget
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T21:32:45.891720Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b8bc58f2-7627-41e6-81e9-ee57e0289148 · inbound
VisMMOE: Exploiting Visual-Expert Affinity for Efficient Visual-Language MoE Offloading cites this paper.
VisMMOE: Exploiting Visual-Expert Affinity for Efficient Visual-Language MoE Offloading SwapMoE: Serving Off-the-shelf MoE-based Large Language Models with Tunable Memory Budget
Reference 22
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T16:36:06.601343Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.