Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
MoE-CT: A Novel Approach For Large Language Models Training With Resistance To Catastrophic Forgetting
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2407.00875.
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 2026-08-07T13:12:00.608250Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T10:34:47.657717Z
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 8603a8cf-aecc-461e-9a56-4ff666cfde78 · inbound
Less, but Better: Efficient Multilingual Expansion for LLMs via Layer-wise Mixture-of-Experts cites this paper.
Less, but Better: Efficient Multilingual Expansion for LLMs via Layer-wise Mixture-of-Experts MoE-CT: A Novel Approach For Large Language Models Training With Resistance To Catastrophic Forgetting
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:12:00.608250Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8b988dd8-3d24-409f-a1dd-154c193bb153 · inbound
Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead cites this paper.
Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead MoE-CT: A Novel Approach For Large Language Models Training With Resistance To Catastrophic Forgetting
Reference 197
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:36:37.442462Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7bcad476-5289-4f5b-b3a7-79c03883aa84 · inbound
SelfAug: Mitigating Catastrophic Forgetting in Retrieval-Augmented Generation via Distribution Self-Alignment cites this paper.
SelfAug: Mitigating Catastrophic Forgetting in Retrieval-Augmented Generation via Distribution Self-Alignment MoE-CT: A Novel Approach For Large Language Models Training With Resistance To Catastrophic Forgetting
Reference 43
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T10:34:47.661876Z
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.