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Paper Citation Record · LEDGER

A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2405.16646.

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

pith.paper-citation-record.v1
2405.16646 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T17:57:25.024965Z

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 937ebf75-9781-49e1-bfa1-9f6ce591e767 · 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 A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-04T17:57:25.024965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:57:25.024965Z digest=sha256:6a866139c34cad326926c0cbfcda7e006ce3ab40507486da6a915598c1410250

Observation d6e82fef-548f-4019-a623-0cce6f6c968a · inbound

How Can Mamba Learn In Context with Outliers and Generalize Provably? cites this paper.

How Can Mamba Learn In Context with Outliers and Generalize Provably? A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-04T13:29:29.934738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T13:29:29.934738Z digest=sha256:3eb07197be21b0e6e2f242d9a7f50b46b84005759aba5d0b31b0616d4dbf94af

Observation 30e99c09-9094-4aff-a492-71706c79fae8 · inbound

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

FluxMoE: Decoupling Expert Residency for High-Performance MoE Serving A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-13T20:18:13.323445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-13T20:16:16.466375Z digest=sha256:5621f48c8e35e12f439246fd62d425818dcae4ba3c8f6ecd598db3b513c6c3be

Observation c9fc6343-9ead-4f99-9632-abb0e3d61ffc · inbound

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

Does a Global Perspective Help Prune Sparse MoEs Elegantly? A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-10T23:30:50.727393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=arxiv_source observed=2026-05-10T19:06:25.626026Z digest=sha256:f69058b7c594b1372d64dc6221c6f96d99dd3f69d4e9a1236e0ae9ee0720a4a1

Observation 3bc4c394-8b2b-4471-9367-b887d33043e3 · inbound

dMoE: dLLMs with Learnable Block Experts cites this paper.

dMoE: dLLMs with Learnable Block Experts A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-06-28T22:52:45.130360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-06-28T22:50:51.900169Z digest=sha256:68f9ecf7e150ea1de632b290e7f764f67627e086e6d8af2b7518200502f3e561

Observation 92d8cf16-0219-4059-809a-acae09d3989c · inbound

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

Attribution-Guided and Coverage-Maximized Pruning for Structural MoE Compression A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts

Reference 4

Resolution
metadata mismatch
arxiv_id, observed 2026-06-27T02:10:21.746719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-06-27T02:07:44.237002Z digest=sha256:f3bf72515742c9288c0034435da240ec3d0db2f2188a363a30e392c077e3170a

Observation 164b1113-6ea3-45ad-ac30-944bf5ec555a · 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 A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts

Reference 42

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-11T08:35:22.347459Z digest=sha256:e445d613225509c5479e38aaab51261a78a82432a42820eb3619238e13bb4223