Paper Citation Record · LEDGER

Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2404.05567.

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

pith.paper-citation-record.v1
2404.05567 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:31:50.478054Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T19:08:50.558628Z

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 129e737e-87b6-446a-90f2-d248528f26ab · inbound

Industrial brain: a human-like autonomous neuro-symbolic cognitive decision-making system cites this paper.

Industrial brain: a human-like autonomous neuro-symbolic cognitive decision-making system Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T21:31:50.478054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45314fb4-18bb-41b1-927d-d722a3d2ef11 · inbound

Universal Pansharpening Model cites this paper.

Universal Pansharpening Model Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-02T19:05:48.549327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 307bd995-80ed-4b41-9a09-f9327d153578 · inbound

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

Does a Global Perspective Help Prune Sparse MoEs Elegantly? Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:30:50.751478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 598b2a3e-c1b4-4a67-9eec-3ebfbaa60759 · inbound

MP-ISMoE: Mixed-Precision Interactive Side Mixture-of-Experts for Efficient Transfer Learning cites this paper.

MP-ISMoE: Mixed-Precision Interactive Side Mixture-of-Experts for Efficient Transfer Learning Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models

Reference 152

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T07:01:10.646889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9939682b-2787-4f99-a1e4-0f849141381c · inbound

Routers Learn the Geometry of Their Experts: Geometric Coupling in Sparse Mixture-of-Experts cites this paper.

Routers Learn the Geometry of Their Experts: Geometric Coupling in Sparse Mixture-of-Experts Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:27:18.855594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 764cd259-ac5d-41df-b6f7-18966e1e1d4f · inbound

SoftMoE: Soft Differentiable Routing for Mixture-of-Experts in LLMs cites this paper.

SoftMoE: Soft Differentiable Routing for Mixture-of-Experts in LLMs Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models

Reference 24

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T19:08:50.560200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.