Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Finding Fantastic Experts in MoEs: A Unified Study for Expert Dropping Strategies and Observations
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2504.05586.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T01:03:27.841154Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T21:18:58.232790Z
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 813489f6-ebea-4a19-bc62-b51c3ccebd77 · inbound
EvoESAP: Non-Uniform Expert Pruning for Sparse MoE cites this paper.
EvoESAP: Non-Uniform Expert Pruning for Sparse MoE Finding Fantastic Experts in MoEs: A Unified Study for Expert Dropping Strategies and Observations
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T14:35:55.603004Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 970eee36-c626-44a0-bb56-e2c9c656aef0 · inbound
SlimQwen: Exploring the Pruning and Distillation in Large MoE Model Pre-training cites this paper.
SlimQwen: Exploring the Pruning and Distillation in Large MoE Model Pre-training Finding Fantastic Experts in MoEs: A Unified Study for Expert Dropping Strategies and Observations
Reference 35
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T07:16:28.518356Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation fefe6a5a-aa90-4e54-9246-4429f9e650b7 · inbound
SlimQwen: Exploring the Pruning and Distillation in Large MoE Model Pre-training cites this paper.
SlimQwen: Exploring the Pruning and Distillation in Large MoE Model Pre-training Finding Fantastic Experts in MoEs: A Unified Study for Expert Dropping Strategies and Observations
Reference 35
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T23:23:51.313057Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 574049e8-6324-4b02-af9c-ea34c3e61a62 · inbound
On the Utility and Factual Reliability of Pruned Mixture-of-Experts Models in the Biomedical Domain cites this paper.
On the Utility and Factual Reliability of Pruned Mixture-of-Experts Models in the Biomedical Domain Finding Fantastic Experts in MoEs: A Unified Study for Expert Dropping Strategies and Observations
Reference 8
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-03T21:18:58.234527Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation b3112eee-2100-43cb-a294-48fa13e75535 · inbound
When Compression Scores Cannot Decide: Information Boundaries for Group-Robust LLM Pruning cites this paper.
When Compression Scores Cannot Decide: Information Boundaries for Group-Robust LLM Pruning Finding Fantastic Experts in MoEs: A Unified Study for Expert Dropping Strategies and Observations
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T01:03:27.841154Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.