Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2404.05567.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T00:37:13.192754Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T19:08:50.558628Z
0 of 0 outbound references displayed
External citation measurements
No source-named external measurement is stored.
No outbound reference observations are available for this paper version.
Observation 17cd2fc4-25f7-46dc-b552-0baf645a894d · inbound
Monet: Mixture of Monosemantic Experts for Transformers Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models
Reference 21
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 731951e7-5796-4419-93dc-ecec60a589d5 · inbound
A Survey on Inference Optimization Techniques for Mixture of Experts Models Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models
Reference 130
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b8c98053-70df-430a-ae69-2e0d3592da0e · inbound
TouchASP: Elastic Automatic Speech Perception that Everyone Can Touch Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models
Reference 6
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 427b3fff-99d2-4f59-8d94-36d76c0383d9 · inbound
Soup-of-Experts: Pretraining Specialist Models via Parameters Averaging Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models
Reference 32
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation baacc1a6-b538-4d4e-bf01-7025b4327245 · inbound
Dynamic Chain-of-Thought: Towards Adaptive Deep Reasoning Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models
Reference 14
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c3f672a2-a990-4bce-a299-880423aa4676 · inbound
EfficientLLM: Efficiency in Large Language Models Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models
Reference 155
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 129e737e-87b6-446a-90f2-d248528f26ab · inbound
Industrial brain: a human-like autonomous neuro-symbolic cognitive decision-making system Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models
Reference 27
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 45314fb4-18bb-41b1-927d-d722a3d2ef11 · inbound
Universal Pansharpening Model Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models
Reference 32
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 307bd995-80ed-4b41-9a09-f9327d153578 · inbound
Does a Global Perspective Help Prune Sparse MoEs Elegantly? Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models
Reference 18
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 598b2a3e-c1b4-4a67-9eec-3ebfbaa60759 · inbound
MP-ISMoE: Mixed-Precision Interactive Side Mixture-of-Experts for Efficient Transfer Learning Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models
Reference 152
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 9939682b-2787-4f99-a1e4-0f849141381c · inbound
Routers Learn the Geometry of Their Experts: Geometric Coupling in Sparse Mixture-of-Experts Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models
Reference 26
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 764cd259-ac5d-41df-b6f7-18966e1e1d4f · inbound
SoftMoE: Soft Differentiable Routing for Mixture-of-Experts in LLMs Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models
Reference 24
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation f5c7008d-2b31-435d-bf08-e1f8ea2e907f · inbound
LoKiFormer: Locality-aware Attention with Decoupled Knowledge Memory for Efficient Large Language Model Pretraining Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models
Reference 21
Source-reported events for the cited work
Unavailable: canonical work link unavailable.