Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
HetuMoE: An Efficient Trillion-scale Mixture-of-Expert Distributed Training System
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2203.14685.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T21:59:50.741606Z
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
11
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 cc7bfa8b-6dc4-47de-9f03-c8c844c7093f · inbound
Hecto: Modular Sparse Experts for Adaptive and Interpretable Reasoning cites this paper.
Hecto: Modular Sparse Experts for Adaptive and Interpretable Reasoning HetuMoE: An Efficient Trillion-scale Mixture-of-Expert Distributed Training System
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:59:50.741606Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fdbe7140-bb2b-40c8-b365-33682c0c10ea · inbound
AGoQ: Activation and Gradient Quantization for Memory-Efficient Distributed Training of LLMs cites this paper.
AGoQ: Activation and Gradient Quantization for Memory-Efficient Distributed Training of LLMs HetuMoE: An Efficient Trillion-scale Mixture-of-Expert Distributed Training System
Reference 18
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T15:31:17.580741Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 7bf4f180-ae02-4dfb-b571-5400bba21d81 · inbound
AGoQ: Activation and Gradient Quantization for Memory-Efficient Distributed Training of LLMs cites this paper.
AGoQ: Activation and Gradient Quantization for Memory-Efficient Distributed Training of LLMs HetuMoE: An Efficient Trillion-scale Mixture-of-Expert Distributed Training System
Reference 18
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T05:21:30.061160Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 57c4bd64-3ab0-423a-a8d5-5ccc39f8405e · inbound
Piper: Efficient Large-Scale MoE Training via Resource Modeling and Pipelined Hybrid Parallelism cites this paper.
Piper: Efficient Large-Scale MoE Training via Resource Modeling and Pipelined Hybrid Parallelism HetuMoE: An Efficient Trillion-scale Mixture-of-Expert Distributed Training System
Reference 36
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-08T17:13:38.771953Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation dd5fa225-7254-41be-acb6-3c24b9cf0e3d · inbound
Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs cites this paper.
Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs HetuMoE: An Efficient Trillion-scale Mixture-of-Expert Distributed Training System
Reference 29
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T21:36:17.246414Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation f41717dd-3c21-4f74-aba6-9b0211cc8618 · inbound
ReLibra: Routing-Replay-Guided Load Balancing for MoE Training in Reinforcement Learning cites this paper.
ReLibra: Routing-Replay-Guided Load Balancing for MoE Training in Reinforcement Learning HetuMoE: An Efficient Trillion-scale Mixture-of-Expert Distributed Training System
Reference 71
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-12T01:46:14.809929Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation a778404f-af0b-416c-a5a1-5df933cc78c9 · inbound
QLPO: Quadrant-weighted Sampling for Length-aware Policy Optimization cites this paper.
QLPO: Quadrant-weighted Sampling for Length-aware Policy Optimization HetuMoE: An Efficient Trillion-scale Mixture-of-Expert Distributed Training System
Reference 294
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T06:49:00.092774Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.