Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:2211.15841.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T11:20:23.608575Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-30T15:24:49.924239Z
0 of 0 outbound references displayed
External citation measurements
No source-named external measurement is stored.
No outbound reference observations are available for this paper version.
Observation 405b044b-1f8b-4e02-ab51-dfb4f02a968f · inbound
Mixtral of Experts MegaBlocks: Efficient Sparse Training with Mixture-of-Experts
Reference 13
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 107736f1-d318-43c3-ba93-bbc2de294613 · inbound
Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models MegaBlocks: Efficient Sparse Training with Mixture-of-Experts
Reference 12
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 99384180-b8a9-4ab8-a0b7-1a9605377599 · inbound
Training Sparse Mixture Of Experts Text Embedding Models MegaBlocks: Efficient Sparse Training with Mixture-of-Experts
Reference 4
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 55dafae2-588d-4e18-b4ee-64b9a1a0224f · inbound
Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights MegaBlocks: Efficient Sparse Training with Mixture-of-Experts
Reference 10
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation afe6b634-b2a1-4f08-ba14-063b67a85fe5 · inbound
Apple Intelligence Foundation Language Models: Tech Report 2025 MegaBlocks: Efficient Sparse Training with Mixture-of-Experts
Reference 6
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9eb5650d-b3eb-45be-924d-73e7de6ca05d · inbound
Maximum Score Routing For Mixture-of-Experts MegaBlocks: Efficient Sparse Training with Mixture-of-Experts
Reference 16
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 479a75b0-ca0a-40cb-aac4-948b76d48d76 · inbound
When Does Sparsity Mitigate the Curse of Depth in LLMs MegaBlocks: Efficient Sparse Training with Mixture-of-Experts
Reference 12
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c71d9f5f-e52f-4b4b-924c-2810a0ffde9c · inbound
Scaling Multi-Node Mixture-of-Experts Inference Using Expert Activation Patterns MegaBlocks: Efficient Sparse Training with Mixture-of-Experts
Reference 7
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 52548489-bca6-4a80-a3ec-91609d656bed · inbound
TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training MegaBlocks: Efficient Sparse Training with Mixture-of-Experts
Reference 19
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 4171cd97-1054-4062-be7b-654dd935ea7f · inbound
RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts MegaBlocks: Efficient Sparse Training with Mixture-of-Experts
Reference 10
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 9575abc1-549e-4c21-b324-4d33f24d60a2 · inbound
Surviving Partial Rank Failures in Wide Expert-Parallel MoE Inference MegaBlocks: Efficient Sparse Training with Mixture-of-Experts
Reference 7
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 1235cce1-ca82-4a06-910f-d8a7456eb1cb · inbound
Safety-Oriented Routing Analysis of Mixtral MoE Under Benign and Harmful Prompts MegaBlocks: Efficient Sparse Training with Mixture-of-Experts
Reference 8
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 ba00ad91-0f43-49e1-944c-510d54818d01 · inbound
Communication-Aware Placement and Pruning for Efficient Mixture-of-Experts Inference MegaBlocks: Efficient Sparse Training with Mixture-of-Experts
Reference 27
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fb0b9cf3-8307-4248-9806-c49d1a1b584e · inbound
A Training-Memory Regression in MLA Sequence Parallelism: Why Megatron-Core Forbids Absorption, and LAGA -- a Communication-Efficient Fix MegaBlocks: Efficient Sparse Training with Mixture-of-Experts
Reference 6
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ce1b3d73-d720-4578-998b-fb5c930107d3 · inbound
Route-Block Membership Selects Packed-AWQ Arithmetic: A Controlled Single-Fixture Mechanism Study MegaBlocks: Efficient Sparse Training with Mixture-of-Experts
Reference 4
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation abfb2628-53e8-4d81-a6a7-fcac483bb81d · inbound
Relax Within, Balance Across: Geometry-Guided Load Balancing for Vision-Language Mixture-of-Experts MegaBlocks: Efficient Sparse Training with Mixture-of-Experts
Reference 2022
Source-reported events for the cited work
Unavailable: canonical work link unavailable.