Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
MoESys: A Distributed and Efficient Mixture-of-Experts Training and Inference System for Internet Services
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2205.10034.
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 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T18:22:48.275391Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T21:52:09.794643Z
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 c16bf56f-ec91-4130-9987-ba68b2356f8e · inbound
Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation cites this paper.
Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation MoESys: A Distributed and Efficient Mixture-of-Experts Training and Inference System for Internet Services
Reference 50
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T14:24:00.456236Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 885bad79-21a6-472e-982e-8b158c7adc8c · inbound
A Survey on Inference Optimization Techniques for Mixture of Experts Models cites this paper.
A Survey on Inference Optimization Techniques for Mixture of Experts Models MoESys: A Distributed and Efficient Mixture-of-Experts Training and Inference System for Internet Services
Reference 157
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T12:44:35.947066Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5518b52d-870d-4a3e-a058-88f88bcafefb · inbound
MoE-GPS: Guidlines for Prediction Strategy for Dynamic Expert Duplication in MoE Load Balancing cites this paper.
MoE-GPS: Guidlines for Prediction Strategy for Dynamic Expert Duplication in MoE Load Balancing MoESys: A Distributed and Efficient Mixture-of-Experts Training and Inference System for Internet Services
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:41:39.474967Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bba7f7c2-dd04-4b92-8ba4-29e4791a7706 · inbound
Hecto: Modular Sparse Experts for Adaptive and Interpretable Reasoning cites this paper.
Hecto: Modular Sparse Experts for Adaptive and Interpretable Reasoning MoESys: A Distributed and Efficient Mixture-of-Experts Training and Inference System for Internet Services
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:59:51.427121Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9598610f-07a4-4393-9bf6-397d0a7b4a0d · inbound
Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging cites this paper.
Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging MoESys: A Distributed and Efficient Mixture-of-Experts Training and Inference System for Internet Services
Reference 34
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T21:52:09.856666Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 72f73e4b-e444-467b-a520-616311cadd47 · inbound
RailX: A Flexible, Scalable, and Low-Cost Network Architecture for Hyper-Scale LLM Training Systems cites this paper.
RailX: A Flexible, Scalable, and Low-Cost Network Architecture for Hyper-Scale LLM Training Systems MoESys: A Distributed and Efficient Mixture-of-Experts Training and Inference System for Internet Services
Reference 123
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T18:22:48.275391Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a6385045-49c8-440f-bfc2-9c8f96d4e492 · inbound
From Tensor Buffer to Distributed Memory Hierarchy: A Survey of KV Cache Management for LLM Serving cites this paper.
From Tensor Buffer to Distributed Memory Hierarchy: A Survey of KV Cache Management for LLM Serving MoESys: A Distributed and Efficient Mixture-of-Experts Training and Inference System for Internet Services
Reference 106
- Resolution
- unresolved
- no resolver link, observed 2026-07-12T09:50:23.266920Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 87c1e333-1cad-4113-9466-5f80f5e9e095 · inbound
Communication-Aware Placement and Pruning for Efficient Mixture-of-Experts Inference cites this paper.
Communication-Aware Placement and Pruning for Efficient Mixture-of-Experts Inference MoESys: A Distributed and Efficient Mixture-of-Experts Training and Inference System for Internet Services
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-07-11T08:35:22.347459Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5edc39aa-bea9-4096-b76d-7a91c6d1ebc6 · inbound
SpecPrefetch: Parameter-Efficient Expert Prefetching for Sparse MoE Foundation Models cites this paper.
SpecPrefetch: Parameter-Efficient Expert Prefetching for Sparse MoE Foundation Models MoESys: A Distributed and Efficient Mixture-of-Experts Training and Inference System for Internet Services
Reference 2017
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T10:18:55.642410Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.