Paper Citation Record · LEDGER

Sparse Mixture-of-Experts are Domain Generalizable Learners

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2206.04046.

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

pith.paper-citation-record.v1
2206.04046 v6

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 2026-08-13T06:32:02.005865+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:42:30.766350Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T23:07:26.960601Z

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 c8d54a78-966d-46ca-8c70-bf244f584773 · inbound

Generalizable Multispectral Land Cover Classification via Frequency-Aware Mixture of Low-Rank Token Experts cites this paper.

Generalizable Multispectral Land Cover Classification via Frequency-Aware Mixture of Low-Rank Token Experts Sparse Mixture-of-Experts are Domain Generalizable Learners

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T15:42:30.766350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27b704ef-3681-48e2-a71e-cc93800ac751 · inbound

LoRA-Mixer: Coordinate Modular LoRA Experts Through Serial Attention Routing cites this paper.

LoRA-Mixer: Coordinate Modular LoRA Experts Through Serial Attention Routing Sparse Mixture-of-Experts are Domain Generalizable Learners

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-19T09:07:14.559981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f4efbd08-6641-42ad-8a2c-e6749753fc33 · inbound

DEER: Disentangled Mixture of Experts with Instance-Adaptive Routing for Generalizable Machine-Generated Text Detection cites this paper.

DEER: Disentangled Mixture of Experts with Instance-Adaptive Routing for Generalizable Machine-Generated Text Detection Sparse Mixture-of-Experts are Domain Generalizable Learners

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T00:27:27.235376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78dc1451-c4bb-43a0-a4c2-f791f2d69b58 · inbound

CrossFlowDG: Bridging the Modality Gap with Cross-modal Flow Matching for Domain Generalization cites this paper.

CrossFlowDG: Bridging the Modality Gap with Cross-modal Flow Matching for Domain Generalization Sparse Mixture-of-Experts are Domain Generalizable Learners

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-10T07:52:13.790549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 43ca8e98-75e2-494b-a84b-c1dffe64ded9 · inbound

STAR: Rethinking MoE Routing as Structure-Aware Subspace Learning cites this paper.

STAR: Rethinking MoE Routing as Structure-Aware Subspace Learning Sparse Mixture-of-Experts are Domain Generalizable Learners

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-07-02T23:07:26.962049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3ddf15a1-dc8f-463b-9364-a29957b41ded · inbound

Can Experts Adapt Without Training? On Test-Time Modality Generalization in MVLMs cites this paper.

Can Experts Adapt Without Training? On Test-Time Modality Generalization in MVLMs Sparse Mixture-of-Experts are Domain Generalizable Learners

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T20:10:30.815191Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.