Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Drop-Upcycling: Training Sparse Mixture of Experts with Partial Re-initialization
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2502.19261.
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 2026-08-05T21:55:11.541674Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T16:07:09.402252Z
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 c7dd9abf-8bae-4531-9603-310de87de6fc · inbound
Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts cites this paper.
Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts Drop-Upcycling: Training Sparse Mixture of Experts with Partial Re-initialization
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T21:55:11.541674Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6ac13a9c-27df-4bb6-a7ae-77fa632cc8a8 · inbound
Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts cites this paper.
Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts Drop-Upcycling: Training Sparse Mixture of Experts with Partial Re-initialization
Reference 39
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T03:29:21.470138Z
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 c75a0b0a-7d71-4908-9189-c273fea7e002 · inbound
Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts cites this paper.
Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts Drop-Upcycling: Training Sparse Mixture of Experts with Partial Re-initialization
Reference 39
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T02:06:15.376410Z
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 58c46432-2673-491c-ba33-0793fea73ed6 · inbound
Mamoda2.5: Enhancing Unified Multimodal Model with DiT-MoE cites this paper.
Mamoda2.5: Enhancing Unified Multimodal Model with DiT-MoE Drop-Upcycling: Training Sparse Mixture of Experts with Partial Re-initialization
Reference 84
- Resolution
- verified exact
- arxiv_id, observed 2026-05-09T06:25:47.875649Z
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 1a7e647e-92fc-496a-9230-d6566a62b7f6 · inbound
Reversible Foundations: Training a 120B Sparse MoE through State-Preserving Scaling cites this paper.
Reversible Foundations: Training a 120B Sparse MoE through State-Preserving Scaling Drop-Upcycling: Training Sparse Mixture of Experts with Partial Re-initialization
Reference 53
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T16:07:09.403688Z
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.