Paper Citation Record · LEDGER

Experts Weights Averaging: A New General Training Scheme for Vision Transformers

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2308.06093.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2308.06093 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:44:35.606784Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-12T05:21:29.947991Z

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 110d3c23-bafd-4f95-be48-203e50c754b3 · inbound

A Survey on Inference Optimization Techniques for Mixture of Experts Models cites this paper.

A Survey on Inference Optimization Techniques for Mixture of Experts Models Experts Weights Averaging: A New General Training Scheme for Vision Transformers

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.606784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 383a6074-3c43-4d1b-ac2e-a89b037ae1e7 · inbound

Merging Models on the Fly Without Retraining: A Sequential Approach to Scalable Continual Model Merging cites this paper.

Merging Models on the Fly Without Retraining: A Sequential Approach to Scalable Continual Model Merging Experts Weights Averaging: A New General Training Scheme for Vision Transformers

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-10T20:03:11.088984Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78f4a3b7-a753-4059-98a5-2809dbed33f5 · inbound

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models cites this paper.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Experts Weights Averaging: A New General Training Scheme for Vision Transformers

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T19:05:55.169824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ef6419d-b080-423c-a3d5-3595d6c6baf5 · inbound

AGoQ: Activation and Gradient Quantization for Memory-Efficient Distributed Training of LLMs cites this paper.

AGoQ: Activation and Gradient Quantization for Memory-Efficient Distributed Training of LLMs Experts Weights Averaging: A New General Training Scheme for Vision Transformers

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:31:18.788502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e0ce55bd-4e81-4248-81a5-5902bed0a430 · inbound

AGoQ: Activation and Gradient Quantization for Memory-Efficient Distributed Training of LLMs cites this paper.

AGoQ: Activation and Gradient Quantization for Memory-Efficient Distributed Training of LLMs Experts Weights Averaging: A New General Training Scheme for Vision Transformers

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:21:29.997408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 16912c84-36d5-44bb-96f6-35527dfdbbde · inbound

Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach cites this paper.

Towards Privacy-Preserving Federated Prompt Tuning under Data Heterogeneity: A Subspace-Decomposed Expert Approach Experts Weights Averaging: A New General Training Scheme for Vision Transformers

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T07:33:05.152405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.