Paper Citation Record · LEDGER

SEER-MoE: Sparse Expert Efficiency through Regularization for Mixture-of-Experts

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2404.05089.

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

pith.paper-citation-record.v1
2404.05089 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T00:49:01.961675Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation e513c6f3-da10-40da-bc09-08cf589196e2 · inbound

A Survey on Efficient Inference for Large Language Models cites this paper.

A Survey on Efficient Inference for Large Language Models SEER-MoE: Sparse Expert Efficiency through Regularization for Mixture-of-Experts

Reference 184

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T02:39:33.172544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c5939d3e-644c-4877-997a-02adddc7084c · inbound

Lynx: Enabling Efficient MoE Inference through Dynamic Batch-Aware Expert Selection cites this paper.

Lynx: Enabling Efficient MoE Inference through Dynamic Batch-Aware Expert Selection SEER-MoE: Sparse Expert Efficiency through Regularization for Mixture-of-Experts

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-23T17:05:42.974851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a89d51e7-941f-408c-b4ca-ff6c2c19a190 · inbound

Condense, Don't Just Prune: Enhancing Efficiency and Performance in MoE Layer Pruning cites this paper.

Condense, Don't Just Prune: Enhancing Efficiency and Performance in MoE Layer Pruning SEER-MoE: Sparse Expert Efficiency through Regularization for Mixture-of-Experts

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-23T17:13:14.044860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5dba707c-f1aa-43c6-84da-bdc42cd319a5 · 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 SEER-MoE: Sparse Expert Efficiency through Regularization for Mixture-of-Experts

Reference 120

Resolution: unresolved; no resolver link, observed 2026-08-11T12:44:35.812514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 861bc117-a4c7-4512-aa1c-5b7327645a46 · inbound

EvoESAP: Non-Uniform Expert Pruning for Sparse MoE cites this paper.

EvoESAP: Non-Uniform Expert Pruning for Sparse MoE SEER-MoE: Sparse Expert Efficiency through Regularization for Mixture-of-Experts

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-15T14:35:55.630815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b4805d89-44b9-4685-b984-4574c6d1a333 · inbound

FluxMoE: Decoupling Expert Residency for High-Performance MoE Serving cites this paper.

FluxMoE: Decoupling Expert Residency for High-Performance MoE Serving SEER-MoE: Sparse Expert Efficiency through Regularization for Mixture-of-Experts

Reference 38

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T20:18:13.315696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 07acc7fa-6274-4c40-bc75-77f735f304a0 · inbound

REAM: Merging Improves Pruning of Experts in LLMs cites this paper.

REAM: Merging Improves Pruning of Experts in LLMs SEER-MoE: Sparse Expert Efficiency through Regularization for Mixture-of-Experts

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T22:20:49.455786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7ff85418-8b1f-4104-88f9-143722c4ce51 · inbound

Alloc-MoE: Budget-Aware Expert Activation Allocation for Efficient Mixture-of-Experts Inference cites this paper.

Alloc-MoE: Budget-Aware Expert Activation Allocation for Efficient Mixture-of-Experts Inference SEER-MoE: Sparse Expert Efficiency through Regularization for Mixture-of-Experts

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T07:30:57.607074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ee8514e3-f1c6-4114-8b8e-1febbacf5516 · inbound

Temporally Extended Mixture-of-Experts Models cites this paper.

Temporally Extended Mixture-of-Experts Models SEER-MoE: Sparse Expert Efficiency through Regularization for Mixture-of-Experts

Reference 28

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T00:39:48.308169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d5b70125-52c6-4d55-91b9-e24d818b4205 · inbound

Fast MoE Inference via Predictive Prefetching and Expert Replication cites this paper.

Fast MoE Inference via Predictive Prefetching and Expert Replication SEER-MoE: Sparse Expert Efficiency through Regularization for Mixture-of-Experts

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-13T02:27:06.980895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 83f9de9c-db81-4a2b-b19c-ddcb39e44b50 · inbound

Less is MoE: Trimming Experts in Domain-Specialist Language Models cites this paper.

Less is MoE: Trimming Experts in Domain-Specialist Language Models SEER-MoE: Sparse Expert Efficiency through Regularization for Mixture-of-Experts

Reference 37

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T11:36:55.521337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 62e25931-7f66-40cc-836d-4c62876699a3 · inbound

Attribution-Guided and Coverage-Maximized Pruning for Structural MoE Compression cites this paper.

Attribution-Guided and Coverage-Maximized Pruning for Structural MoE Compression SEER-MoE: Sparse Expert Efficiency through Regularization for Mixture-of-Experts

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-06-27T02:10:21.740597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 37c26ce4-16ee-4383-8fe3-93f9bc83f13f · inbound

On the Utility and Factual Reliability of Pruned Mixture-of-Experts Models in the Biomedical Domain cites this paper.

On the Utility and Factual Reliability of Pruned Mixture-of-Experts Models in the Biomedical Domain SEER-MoE: Sparse Expert Efficiency through Regularization for Mixture-of-Experts

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-07-03T21:18:58.240923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9e90403c-22a1-48db-9172-f5027fa6feb6 · inbound

Router Sensitivity Under Lightweight Fine-Tuning Identifies Prunable Experts in Mixture-of-Experts Models cites this paper.

Router Sensitivity Under Lightweight Fine-Tuning Identifies Prunable Experts in Mixture-of-Experts Models SEER-MoE: Sparse Expert Efficiency through Regularization for Mixture-of-Experts

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-12T00:49:01.961675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.