Paper Citation Record · LEDGER

Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:2405.14297.

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

pith.paper-citation-record.v1
2405.14297 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 15 of 15 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:44:35.464375Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T15:39:56.514258Z

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 b0867ddf-9f69-4902-9cce-0fc23997bd68 · 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 Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.464375Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d3edc89-f958-4f09-93f0-f6c147fc8bcb · inbound

Mixture of Experts (MoE): A Big Data Perspective cites this paper.

Mixture of Experts (MoE): A Big Data Perspective Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-10T18:56:37.338376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53a8e46f-fa72-428e-8e43-79292a7c7001 · inbound

Tight Clusters Make Specialized Experts cites this paper.

Tight Clusters Make Specialized Experts Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:55:19.530584Z

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 e484b78a-4242-4364-b61b-b39c2e9edfe2 · inbound

Decoding Knowledge Attribution in Mixture-of-Experts: A Framework of Basic-Refinement Collaboration and Efficiency Analysis cites this paper.

Decoding Knowledge Attribution in Mixture-of-Experts: A Framework of Basic-Refinement Collaboration and Efficiency Analysis Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T12:35:26.080156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c45b934-2f91-40d8-bfcb-6552f3c52bc9 · inbound

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts cites this paper.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:08.825228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90aa6063-8ba9-41d1-9f56-773a39372f21 · inbound

LayerScope: Predictive Cross-Layer Scheduling for Efficient Multi-Batch MoE Inference on Legacy Servers cites this paper.

LayerScope: Predictive Cross-Layer Scheduling for Efficient Multi-Batch MoE Inference on Legacy Servers Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-18T12:41:22.750752Z

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 3009b571-ce6c-4eb5-acbd-07ee44fce063 · inbound

A global log for medical AI cites this paper.

A global log for medical AI Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-04T11:34:09.076920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24f1c759-c0b0-4caf-abfb-b9dbef9e1008 · inbound

DTop-p MoE: Sparsity-Controlled Dynamic Top-p MoE for Foundation Model Pre-training cites this paper.

DTop-p MoE: Sparsity-Controlled Dynamic Top-p MoE for Foundation Model Pre-training Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T16:21:31.008497Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5591dc7-841e-4d24-905f-cb3dfbe8cbcf · inbound

BEAM: Binary Expert Activation Masking for Dynamic Routing in MoE cites this paper.

BEAM: Binary Expert Activation Masking for Dynamic Routing in MoE Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-15T01:59:38.695894Z

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 451e832b-0d19-4404-9e54-ec838ce2b527 · inbound

Post-Trained MoE Can Skip Half Experts via Self-Distillation cites this paper.

Post-Trained MoE Can Skip Half Experts via Self-Distillation Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:03:15.193659Z

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 35d2ee69-2aeb-438f-abf0-96af6b602ae0 · inbound

Post-Trained MoE Can Skip Half Experts via Self-Distillation cites this paper.

Post-Trained MoE Can Skip Half Experts via Self-Distillation Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-06-30T18:25:00.018283Z

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 355a096d-004d-45e3-880c-423195d5ea50 · inbound

PithTrain: A Compact and Agent-Native MoE Training System cites this paper.

PithTrain: A Compact and Agent-Native MoE Training System Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-06-28T23:12:46.518740Z

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 de322e12-ec2c-4faf-9858-b1716e0c215d · inbound

STAR: Rethinking MoE Routing as Structure-Aware Subspace Learning cites this paper.

STAR: Rethinking MoE Routing as Structure-Aware Subspace Learning Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-02T23:07:26.926103Z

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 ed922655-18c0-4e7f-b4c7-e735a63860d8 · inbound

GeMoE: Gating Entropy is All You Need for Uncertainty-aware Adaptive Routing in MoE-based Large Vision-Language Models cites this paper.

GeMoE: Gating Entropy is All You Need for Uncertainty-aware Adaptive Routing in MoE-based Large Vision-Language Models Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models

Reference 24

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T15:39:56.515476Z

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 20490b7b-c4cc-4db1-a652-fca9da3ff9df · inbound

Beyond Uniform Experts: Cost-Aware Expert Execution for Efficient Multi-Device MoE Inference cites this paper.

Beyond Uniform Experts: Cost-Aware Expert Execution for Efficient Multi-Device MoE Inference Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-06-30T17:14:57.472706Z

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.