Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models
As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2407.01906.
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 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T21:56:03.410343Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T06:39:37.866861Z
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 87a78449-95f5-43e8-88a7-3132c14b2201 · inbound
PERFT: Parameter-Efficient Routed Fine-Tuning for Mixture-of-Expert Model cites this paper.
PERFT: Parameter-Efficient Routed Fine-Tuning for Mixture-of-Expert Model Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models
Reference 57
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T21:56:03.410343Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e68e4604-d2a0-4bc0-b13c-8ab0500e4274 · inbound
SEUF: Is Unlearning One Expert Enough for Mixture-of-Experts LLMs? cites this paper.
SEUF: Is Unlearning One Expert Enough for Mixture-of-Experts LLMs? Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models
Reference 54
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T10:57:22.930781Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 02a3d0b2-6fe4-4e83-a979-08ec8dc5c359 · inbound
Chain-of-Experts: Unlocking the Communication Power of Mixture-of-Experts Models cites this paper.
Chain-of-Experts: Unlocking the Communication Power of Mixture-of-Experts Models Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:28:36.692380Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4abd988f-1cb5-424c-8604-4afec38bd550 · inbound
Unveiling Language Routing Isolation in Multilingual MoE Models for Interpretable Subnetwork Adaptation cites this paper.
Unveiling Language Routing Isolation in Multilingual MoE Models for Interpretable Subnetwork Adaptation Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T12:54:50.789596Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 546d09ba-25e6-4a5e-a42f-76b7b06136df · inbound
Application-Driven Pedagogical Knowledge Optimization of Open-Source LLMs via Reinforcement Learning and Supervised Fine-Tuning cites this paper.
Application-Driven Pedagogical Knowledge Optimization of Open-Source LLMs via Reinforcement Learning and Supervised Fine-Tuning Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T22:20:49.416094Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 3b20f752-752a-4433-8275-4ad8d29bfade · inbound
Preserving Long-Tailed Expert Information in Mixture-of-Experts Tuning cites this paper.
Preserving Long-Tailed Expert Information in Mixture-of-Experts Tuning Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models
Reference 30
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T19:21:09.710901Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 8beeb8a8-83fc-494b-9a1f-63e0ff3abc30 · inbound
Behavioral and Representational Evidence of Binomial Ordering Preferences in Large Language Models cites this paper.
Behavioral and Representational Evidence of Binomial Ordering Preferences in Large Language Models Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models
Reference 40
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-04T06:39:37.868624Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 5de70fa0-5c5f-4d65-b8d4-2f76d9d1f776 · inbound
MoE$^2$-LoRA: When MoE Models Meet MoE-style Low-Rank Adaptation cites this paper.
MoE$^2$-LoRA: When MoE Models Meet MoE-style Low-Rank Adaptation Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T06:18:16.165867Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.