Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2405.16646.
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 2026-08-06T06:34:29.942622+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-04T17:57:25.024965Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
0 of 0 outbound references displayed
External citation measurements
0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
No outbound reference observations are available for this paper version.
Observation 937ebf75-9781-49e1-bfa1-9f6ce591e767 · inbound
Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts
Reference 8
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d6e82fef-548f-4019-a623-0cce6f6c968a · inbound
How Can Mamba Learn In Context with Outliers and Generalize Provably? A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts
Reference 9
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 30e99c09-9094-4aff-a492-71706c79fae8 · inbound
FluxMoE: Decoupling Expert Residency for High-Performance MoE Serving A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts
Reference 11
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation c9fc6343-9ead-4f99-9632-abb0e3d61ffc · inbound
Does a Global Perspective Help Prune Sparse MoEs Elegantly? A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts
Reference 7
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 3bc4c394-8b2b-4471-9367-b887d33043e3 · inbound
dMoE: dLLMs with Learnable Block Experts A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts
Reference 19
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 92d8cf16-0219-4059-809a-acae09d3989c · inbound
Attribution-Guided and Coverage-Maximized Pruning for Structural MoE Compression A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts
Reference 4
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 164b1113-6ea3-45ad-ac30-944bf5ec555a · inbound
Communication-Aware Placement and Pruning for Efficient Mixture-of-Experts Inference A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts
Reference 42
Source-reported events for the cited work
Unavailable: canonical work link unavailable.