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Paper Citation Record · LEDGER

SlimMoE: Structured Compression of Large MoE Models via Expert Slimming and Distillation

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2506.18349.

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

pith.paper-citation-record.v1
2506.18349 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T12:39:25.535897Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T12:43:25.580785Z

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 4d4a5acc-75a2-4225-bddb-637d91878c92 · 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 SlimMoE: Structured Compression of Large MoE Models via Expert Slimming and Distillation

Reference 31

Resolution
verified exact
arxiv_id, observed 2026-05-10T03:29:21.477510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-10T03:29:16.555166Z digest=sha256:44a19bbac258386cf4ecd242054dde7ffe6627e8bc360f3e6be46d8c8c882505

Observation f3091ad0-4e77-452b-8eb4-44f7fd349f6f · 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 SlimMoE: Structured Compression of Large MoE Models via Expert Slimming and Distillation

Reference 31

Resolution
verified exact
arxiv_id, observed 2026-05-12T02:06:15.342964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-12T02:03:02.654035Z digest=sha256:94c3fe4d2ee4a7600c3870740afe493a3934a50f79728f1ba3defdad0b5f78c5

Observation 9eb82aee-218f-43e1-ac45-286752f3db72 · 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 SlimMoE: Structured Compression of Large MoE Models via Expert Slimming and Distillation

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-05-12T07:16:28.297698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-05-12T03:34:10.370956Z digest=sha256:dafd98e557125888978f0f8a8d70be296046eb7192914bc17ff499c9aba2e8ef

Observation 140c4f58-c62c-4443-b1e7-bc6a9b4b382f · 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 SlimMoE: Structured Compression of Large MoE Models via Expert Slimming and Distillation

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-05-20T23:23:51.279419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-05-20T23:22:51.808346Z digest=sha256:d52ccc8b67f83429e6a59267b8bb74946135c71d70e12480b3ab5c5aa0db4ec0

Observation 68144bee-6c3d-43a6-8339-6be28052966a · inbound

Pruning and Distilling Mixture-of-Experts into Dense Language Models cites this paper.

Pruning and Distilling Mixture-of-Experts into Dense Language Models SlimMoE: Structured Compression of Large MoE Models via Expert Slimming and Distillation

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-06-29T12:43:25.582189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-06-29T12:39:25.535897Z digest=sha256:9857a05983b7796daf8743731d30ee1d9c132023576efdeead1bfa245d9dbca1