Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2405.14297.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T12:35:26.080156Z
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 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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+00:00.