Paper Citation Record · LEDGER

HeterMoE: Efficient Training of Mixture-of-Experts Models on Heterogeneous GPUs

As of 3 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2504.03871.

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

pith.paper-citation-record.v1
2504.03871 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 2026-08-03T06:30:56.289259+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T13:33:40.263979Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-10T12:15:01.137692Z

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 9bac683c-ab8e-428f-83f6-c22cf25fc492 · inbound

HetRL: Efficient Reinforcement Learning for LLMs in Heterogeneous Environments cites this paper.

HetRL: Efficient Reinforcement Learning for LLMs in Heterogeneous Environments HeterMoE: Efficient Training of Mixture-of-Experts Models on Heterogeneous GPUs

Reference 44

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T22:23:36.605292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 8d7c778e-2d3e-43e1-a02c-b76f054756b3 · inbound

UniEP: Unified Expert-Parallel MoE MegaKernel for LLM Training cites this paper.

UniEP: Unified Expert-Parallel MoE MegaKernel for LLM Training HeterMoE: Efficient Training of Mixture-of-Experts Models on Heterogeneous GPUs

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-10T02:22:20.798325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation f3d9f39d-42f0-4ce3-8b2b-310170deda5c · inbound

DisagMoE: Computation-Communication overlapped MoE Training via Disaggregated AF-Pipe Parallelism cites this paper.

DisagMoE: Computation-Communication overlapped MoE Training via Disaggregated AF-Pipe Parallelism HeterMoE: Efficient Training of Mixture-of-Experts Models on Heterogeneous GPUs

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-13T01:52:06.246426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation eb532d72-1c50-4332-b932-ad5748e901c8 · inbound

Diagnosing Overhead in Dispatch Operations: Cross-architecture Observatory cites this paper.

Diagnosing Overhead in Dispatch Operations: Cross-architecture Observatory HeterMoE: Efficient Training of Mixture-of-Experts Models on Heterogeneous GPUs

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-21T02:13:55.955085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 9d7712e8-5e1e-4855-b8d2-651549a2597e · inbound

Diagnosing Overhead in Dispatch Operations: Cross-architecture Observatory cites this paper.

Diagnosing Overhead in Dispatch Operations: Cross-architecture Observatory HeterMoE: Efficient Training of Mixture-of-Experts Models on Heterogeneous GPUs

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T13:33:40.263979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f86ca2d7-8180-4d8c-b3b9-67078792f97b · inbound

Simulating Unified Tensor Resharding in heterogeneous AI systems cites this paper.

Simulating Unified Tensor Resharding in heterogeneous AI systems HeterMoE: Efficient Training of Mixture-of-Experts Models on Heterogeneous GPUs

Reference 68

Resolution: verified exact; arxiv_id, observed 2026-07-04T14:19:54.701427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.