Paper Citation Record · LEDGER

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights

As of 16 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2506.02890.

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

pith.paper-citation-record.v1
2506.02890 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:19:02.406641Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

40 of 40 outbound references displayed

verified exact1
verified fuzzy0
unresolved38
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 23d42a33-69a1-42d0-9044-f9b7cb5f1412 · outbound

This paper cites PIQA: Reasoning about Physical Commonsense in Natural Language.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights PIQA: Reasoning about Physical Commonsense in Natural Language

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T11:18:57.605069Z

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Observation 6c5c04c0-114f-4b1d-b2e7-e13f50b7573b · outbound

This paper cites Unified Scaling Laws for Routed Language Models.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights Unified Scaling Laws for Routed Language Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T11:18:57.728870Z

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Observation 490955d4-d1fa-4eae-925c-960bfbf0357b · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T11:18:57.847592Z

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Observation cf6215c0-8604-43ad-88a1-1ade11843c52 · outbound

This paper cites DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T11:18:57.953340Z

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Observation 7e5135d2-3a1a-4c4a-ab67-87ee6e3f6d41 · outbound

This paper cites Sparse Upcycling: Inference Inefficient Finetuning.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights Sparse Upcycling: Inference Inefficient Finetuning

Reference 5

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T11:19:03.596040Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ac690ccf-7837-4ab3-86b4-91ad969c3ef0 · outbound

This paper cites GLaM: Efficient Scaling of Language Models with Mixture-of-Experts.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights GLaM: Efficient Scaling of Language Models with Mixture-of-Experts

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T11:18:58.233961Z

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Observation cbcf660c-bf90-4ac9-9fdc-e8586bda8110 · outbound

This paper cites The Llama 3 Herd of Models.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights The Llama 3 Herd of Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T11:18:58.354384Z

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Observation bfaf3d68-9e6d-41b0-9a53-a672eee2a5ab · outbound

This paper cites LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T11:18:58.457714Z

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Observation 07219901-bb44-4405-a9b3-810c78fad12b · outbound

This paper cites Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T11:18:58.610575Z

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Observation 55dafae2-588d-4e18-b4ee-64b9a1a0224f · outbound

This paper cites MegaBlocks: Efficient Sparse Training with Mixture-of-Experts.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights MegaBlocks: Efficient Sparse Training with Mixture-of-Experts

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T11:18:58.768850Z

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Observation 429f91ef-ec6b-419b-85c8-820860df9c47 · outbound

This paper cites Upcycling Large Language Models into Mixture of Experts.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights Upcycling Large Language Models into Mixture of Experts

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T11:18:58.896820Z

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Observation 64cd1e7f-79d9-4754-8811-bcb8c29a1841 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights Measuring Massive Multitask Language Understanding

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T11:18:59.031902Z

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Observation f5bb8d47-9b68-4ab0-8272-085aaed78c67 · outbound

This paper cites Training Compute-Optimal Large Language Models.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights Training Compute-Optimal Large Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T11:18:59.176409Z

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Observation 3cc6b99b-984c-4575-914f-684b0cc97be5 · outbound

This paper cites Mixtral of Experts.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights Mixtral of Experts

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T11:18:59.337088Z

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Observation 25d45c05-03e1-421c-92cb-edc25657d8e0 · outbound

This paper cites Scaling Laws for Neural Language Models.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights Scaling Laws for Neural Language Models

Reference 15

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Observation 49c2378d-5ed7-4d2f-a1f9-9e08474a9558 · outbound

This paper cites Scaling Laws for Fine-Grained Mixture of Experts.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights Scaling Laws for Fine-Grained Mixture of Experts

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T11:18:59.617737Z

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Observation c31a50cf-a3bb-49af-ae73-2a2b94cca90a · outbound

This paper cites RACE: Large-scale ReAding Comprehension Dataset From Examinations.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights RACE: Large-scale ReAding Comprehension Dataset From Examinations

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T11:18:59.705154Z

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Observation efaed30b-9103-4d35-8666-f46d2d19fbf2 · outbound

This paper cites GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

Reference 18

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Observation 8af221aa-1615-432b-8715-36b997b7a23a · outbound

This paper cites TruthfulQA: Measuring How Models Mimic Human Falsehoods.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights TruthfulQA: Measuring How Models Mimic Human Falsehoods

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T11:18:59.970756Z

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Observation 6cda30c3-4cf6-4df1-84aa-8c83da93f4b6 · outbound

This paper cites Decoupled Weight Decay Regularization.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights Decoupled Weight Decay Regularization

Reference 20

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Observation 440298cf-05a8-4c27-9f66-92bad5874d99 · outbound

This paper cites Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:00.187903Z

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Observation 09650bda-ab84-413a-ae5a-07c044a036b9 · outbound

This paper cites OLMoE: Open Mixture-of-Experts Language Models.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights OLMoE: Open Mixture-of-Experts Language Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:00.317285Z

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Observation 8b7bce40-c419-4b8b-8405-e67e9b3de5c6 · outbound

This paper cites Nemotron-4 15B Technical Report.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights Nemotron-4 15B Technical Report

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:00.444605Z

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Observation 93099af7-621f-4c4d-adcb-63637862ad55 · outbound

This paper cites Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:00.551984Z

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Observation 10dac4b0-507a-471a-bde5-a808b18e13b8 · outbound

This paper cites WinoGrande: An Adversarial Winograd Schema Challenge at Scale.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights WinoGrande: An Adversarial Winograd Schema Challenge at Scale

Reference 25

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Observation 203ac900-8af4-4aaf-a6bd-317fba9b1f18 · outbound

This paper cites SocialIQA: Commonsense Reasoning about Social Interactions.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights SocialIQA: Commonsense Reasoning about Social Interactions

Reference 26

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Observation 4f5a9f30-f350-446d-8f03-53eb866f7a82 · outbound

This paper cites GLU Variants Improve Transformer.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights GLU Variants Improve Transformer

Reference 27

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Observation 5e5fe9c0-cf6c-4447-83a7-26d75f2bbccd · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 28

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Observation 50cdbd4e-1a64-417c-8797-e52818ce1cc7 · outbound

This paper cites Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:01.053899Z

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Observation 9df312bc-e8dc-4c7b-b8f8-e54611c79b15 · outbound

This paper cites CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:01.172988Z

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Observation 78708258-bb64-4aec-a720-da550fee9539 · outbound

This paper cites Scattered Mixture-of-Experts Implementation.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights Scattered Mixture-of-Experts Implementation

Reference 31

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Observation 8bbe1ebc-a25d-4c7d-b1eb-2a4650f36c6d · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights Gemini: A Family of Highly Capable Multimodal Models

Reference 32

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Observation 880eabab-d2fe-4fb5-93c1-b37eec1aab11 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights LLaMA: Open and Efficient Foundation Language Models

Reference 33

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Observation a206d79e-3f9a-4d15-a308-51b65b513df5 · outbound

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Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 34

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Observation 38a7c2ea-f04f-4928-9c37-c87943b8b149 · outbound

This paper cites Attention Is All You Need.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights Attention Is All You Need

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:01.798731Z

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Observation d5f06785-f366-42a5-b461-a7885a63d744 · outbound

This paper cites Llama 3 Meets MoE: Efficient Upcycling.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights Llama 3 Meets MoE: Efficient Upcycling

Reference 36

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5a460349-b136-4297-89aa-e38da0ad669f · outbound

This paper cites Auxiliary-Loss-Free Load Balancing Strategy for Mixture-of-Experts.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights Auxiliary-Loss-Free Load Balancing Strategy for Mixture-of-Experts

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:02.046781Z

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Observation b2b7a143-d814-4e89-a644-396b9c2b1c69 · outbound

This paper cites OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:02.147861Z

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Observation d1927ad0-145f-4822-88e1-84c942b07e31 · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:02.275720Z

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Observation eeecb256-2e9e-4c02-8725-7200254ac798 · outbound

This paper cites ST-MoE: Designing Stable and Transferable Sparse Expert Models.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights ST-MoE: Designing Stable and Transferable Sparse Expert Models

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:02.406641Z

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Pith citing papers

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