Paper Citation Record · LEDGER

Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2407.00945.

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

pith.paper-citation-record.v1
2407.00945 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 2026-08-10T06:31:04.303077+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T18:56:37.579860Z

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

2
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 3a4d1b1f-30b1-43db-aec9-d07997c616df · inbound

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

Mixture of Experts (MoE): A Big Data Perspective Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs

Reference 111

Resolution: unresolved; no resolver link, observed 2026-08-10T18:56:37.579860Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7660d774-09ea-415c-9f92-5a4c5e66991e · inbound

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging cites this paper.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T21:52:06.051660Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b966d609-c9b1-400b-9d19-4d782f931642 · inbound

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs cites this paper.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T17:57:25.147648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b00ed5d-839b-4f7f-85aa-d9bdfebc41b8 · inbound

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

EvoESAP: Non-Uniform Expert Pruning for Sparse MoE Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-15T14:35:55.542805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 50b30048-2659-47a3-8189-2652e2a66b70 · inbound

Does a Global Perspective Help Prune Sparse MoEs Elegantly? cites this paper.

Does a Global Perspective Help Prune Sparse MoEs Elegantly? Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:30:50.688930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0930bd18-5d90-4894-a874-b0fdf08145f4 · inbound

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

Temporally Extended Mixture-of-Experts Models Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs

Reference 24

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T00:39:48.290062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 27d5642d-aa9e-4f3e-9fc4-2c17e568e113 · inbound

HodgeCover: Higher-Order Topological Coverage Drives Compression of Sparse Mixture-of-Experts cites this paper.

HodgeCover: Higher-Order Topological Coverage Drives Compression of Sparse Mixture-of-Experts Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-15T05:55:04.829046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 29d0b9e4-6f75-4189-add2-25b448d22c8f · inbound

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

Post-Trained MoE Can Skip Half Experts via Self-Distillation Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:03:15.243574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 489d6158-235b-4aa9-b203-60bb8436b6ef · inbound

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

Post-Trained MoE Can Skip Half Experts via Self-Distillation Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-06-30T18:25:00.060784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e62a1f1a-b2f7-4f84-b6dc-bdce1d046c27 · inbound

dMoE: dLLMs with Learnable Block Experts cites this paper.

dMoE: dLLMs with Learnable Block Experts Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-06-28T22:52:45.115735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 94868339-c023-4799-a1e7-cc7b5949fc0a · inbound

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

Less is MoE: Trimming Experts in Domain-Specialist Language Models Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs

Reference 50

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T11:36:55.540848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e61384f3-e1c4-4f9c-ab82-797e14b04bb5 · inbound

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

Attribution-Guided and Coverage-Maximized Pruning for Structural MoE Compression Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-06-27T02:10:21.751821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 331452d3-39fa-4727-a2c2-478da7597b75 · inbound

Director: Accelerating Distributed MoE Serving via Online Proactive Expert Placement cites this paper.

Director: Accelerating Distributed MoE Serving via Online Proactive Expert Placement Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-13T07:33:31.233659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ed985b9-0324-446d-8bd4-5eb1f49936ce · inbound

EdgeXpert: An Edge Device for Memory-Efficient LLM Inference with Mixture-of-Experts and Speculative Decoding cites this paper.

EdgeXpert: An Edge Device for Memory-Efficient LLM Inference with Mixture-of-Experts and Speculative Decoding Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-08T15:48:05.515013Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.