Paper Citation Record · LEDGER

Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 27 inbound Pith citation observations for arXiv:2402.14800.

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

pith.paper-citation-record.v1
2402.14800 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 27 of 27 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T21:51:05.721834Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T19:08:50.012079Z

Reference resolution

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verified exact0
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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 be36b2e7-608d-4942-b512-7d9e33db634f · inbound

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

A Survey on Efficient Inference for Large Language Models Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 183

Resolution: verified exact; arxiv_id, observed 2026-05-15T02:39:33.169470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3cce7e1d-3d45-4a35-92ce-7874c9a6a402 · 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 Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 15

Resolution: metadata mismatch; arxiv_id, observed 2026-05-23T17:05:42.981077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 04cc05ad-d4f1-40d2-a50f-9be1250037d4 · 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 Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-23T17:13:14.061601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1e936820-78cb-4947-8138-fb9c67f9fa18 · inbound

Analytical FFN-to-MoE Restructuring via Activation Pattern Analysis cites this paper.

Analytical FFN-to-MoE Restructuring via Activation Pattern Analysis Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-23T04:12:31.024128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 822ff542-9253-4c11-9d77-6273aba3864c · inbound

MoE-Compression: How the Compression Error of Experts Affects the Inference Accuracy of MoE Model? cites this paper.

MoE-Compression: How the Compression Error of Experts Affects the Inference Accuracy of MoE Model? Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T21:51:05.721834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e503e305-eb19-4b77-b620-c66d5a4e5d81 · inbound

Unified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation cites this paper.

Unified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:19.418335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0283ff76-3549-4e48-8be1-a5c9d2fb79ea · 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 Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T17:57:25.160350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d35c89df-12f2-4f4e-85cb-b42da094b9be · 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 Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-18T12:41:22.803615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1cd25960-a6da-476c-8de5-adbda4992407 · inbound

PuzzleMoE: Efficient Compression of Large Mixture-of-Experts Models via Sparse Expert Merging and Bit-packed inference cites this paper.

PuzzleMoE: Efficient Compression of Large Mixture-of-Experts Models via Sparse Expert Merging and Bit-packed inference Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T23:41:52.898841Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 601b09f5-644d-4f07-a968-e1c972f721ed · inbound

Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models cites this paper.

Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 199

Resolution: verified exact; arxiv_id, observed 2026-05-16T12:40:54.829536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a8dc26dd-950c-417a-9d1e-cd7f1aa050c4 · inbound

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

EvoESAP: Non-Uniform Expert Pruning for Sparse MoE Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-15T14:35:55.505976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b3989a0d-4e18-4db4-8d27-4aa2352b11a4 · inbound

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

FluxMoE: Decoupling Expert Residency for High-Performance MoE Serving Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-13T20:18:13.308753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 46f94e26-bb0d-4f72-a72a-8bc6e83b628f · inbound

Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts cites this paper.

Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-10T03:29:21.482695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation cac8bb70-77c3-4759-b6f5-0b739b619197 · inbound

Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts cites this paper.

Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:06:15.362277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 94f2ab8f-a75a-49cb-acad-49e2270e6646 · inbound

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

Temporally Extended Mixture-of-Experts Models Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-10T00:39:48.314205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c9372f65-d6c0-4c07-addc-dcaba4a9384f · inbound

Preserving Long-Tailed Expert Information in Mixture-of-Experts Tuning cites this paper.

Preserving Long-Tailed Expert Information in Mixture-of-Experts Tuning Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:21:09.646812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 042d1a9f-7455-48ff-93a5-397f2be0765d · inbound

MACS: Modality-Aware Capacity Scaling for Efficient Multimodal MoE Inference cites this paper.

MACS: Modality-Aware Capacity Scaling for Efficient Multimodal MoE Inference Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T06:51:46.405151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a72b52a1-6a79-4870-9cc3-a46ce8901e92 · inbound

MACS: Modality-Aware Capacity Scaling for Efficient Multimodal MoE Inference cites this paper.

MACS: Modality-Aware Capacity Scaling for Efficient Multimodal MoE Inference Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-11T01:45:52.053900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8b1db52f-d37f-4c5f-951d-fda88f01870b · inbound

SlimQwen: Exploring the Pruning and Distillation in Large MoE Model Pre-training cites this paper.

SlimQwen: Exploring the Pruning and Distillation in Large MoE Model Pre-training Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:16:28.485473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8b1d5189-8889-42d4-8edc-44ec47436a21 · inbound

SlimQwen: Exploring the Pruning and Distillation in Large MoE Model Pre-training cites this paper.

SlimQwen: Exploring the Pruning and Distillation in Large MoE Model Pre-training Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-20T23:23:51.219912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a8f8732b-b5f8-480f-a672-2627725cc172 · inbound

When Does Sparse MoE Help in Vision? The Role of Backbone Compute Leverage in Sparse Routing cites this paper.

When Does Sparse MoE Help in Vision? The Role of Backbone Compute Leverage in Sparse Routing Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-19T16:22:39.608550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8fe14920-7977-42ae-b05a-7b40f3205cab · inbound

GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs cites this paper.

GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-25T05:36:39.868047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4122790f-c394-4632-9378-fc589a2f737f · inbound

BitsMoE: Efficient Spectral Energy-Guided Bit Allocation for MoE LLM Quantization cites this paper.

BitsMoE: Efficient Spectral Energy-Guided Bit Allocation for MoE LLM Quantization Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-06-30T15:44:48.441458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation cd5e468b-b1a5-4354-b53c-5e4c6d067459 · inbound

Expert-Aware Refusal Steering cites this paper.

Expert-Aware Refusal Steering Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-07-02T03:26:29.122065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4959bbd2-0486-4e39-af8f-e7e1a649a509 · inbound

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

Attribution-Guided and Coverage-Maximized Pruning for Structural MoE Compression Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 13

Resolution: malformed identifier; arxiv_id, observed 2026-07-03T19:08:50.013861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 39e3a7b9-a421-459d-a781-95c484fb4e55 · 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 Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-06-30T17:14:57.435789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c6e63b0b-f3f9-4024-b036-bbd514680b1d · inbound

Communication-Aware Placement and Pruning for Efficient Mixture-of-Experts Inference cites this paper.

Communication-Aware Placement and Pruning for Efficient Mixture-of-Experts Inference Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-11T08:35:22.347459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.