Paper Citation Record · LEDGER

The power of fine-grained experts: Granularity boosts expressivity in Mixture of Experts

As of 19 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 1 inbound Pith citation observation for arXiv:2505.06839.

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

pith.paper-citation-record.v1
2505.06839 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:46:44.611831Z

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-15T04:45:20.091598Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T04:49:44.902632Z

Reference resolution

12 of 12 outbound references displayed

verified exact1
verified fuzzy0
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation acb9d570-262b-4ae3-b445-d43529f4ac24 · outbound

This paper cites Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models.

The power of fine-grained experts: Granularity boosts expressivity in Mixture of Experts Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T22:46:44.551914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3fd8071f-9a3c-41f4-8380-989a19baa200 · outbound

This paper cites Learning Factored Representations in a Deep Mixture of Experts.

The power of fine-grained experts: Granularity boosts expressivity in Mixture of Experts Learning Factored Representations in a Deep Mixture of Experts

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T22:46:44.562756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a372c26e-c605-497b-a2c7-f733c157d218 · outbound

This paper cites Mixture of A Million Experts.

The power of fine-grained experts: Granularity boosts expressivity in Mixture of Experts Mixture of A Million Experts

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T22:46:44.567757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83593f7e-03f4-4f89-a214-4fc73c2f7b6e · outbound

This paper cites Scaling Laws for Neural Language Models.

The power of fine-grained experts: Granularity boosts expressivity in Mixture of Experts Scaling Laws for Neural Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T22:46:44.593031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8f91383-0e61-458f-851a-3e6491fc3c03 · outbound

This paper cites Toward Inference-optimal Mixture-of-Expert Large Language Models.

The power of fine-grained experts: Granularity boosts expressivity in Mixture of Experts Toward Inference-optimal Mixture-of-Expert Large Language Models

Reference 1937

Resolution: unresolved; no resolver link, observed 2026-08-15T22:46:44.611831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51fcda39-e0ee-4794-ac27-0da723f76ce2 · outbound

This paper cites Mixture of Parrots: Experts improve memorization more than reasoning.

The power of fine-grained experts: Granularity boosts expressivity in Mixture of Experts Mixture of Parrots: Experts improve memorization more than reasoning

Reference 1991

Resolution: unresolved; no resolver link, observed 2026-08-15T22:46:44.572961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19f5b74c-cf85-4805-8ab5-0415fdb0cfbb · outbound

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

The power of fine-grained experts: Granularity boosts expressivity in Mixture of Experts Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 2006

Resolution: unresolved; no resolver link, observed 2026-08-15T22:46:44.606998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 853018b4-d263-488b-a4ad-4f2f03a88d57 · outbound

This paper cites Towards A Unified View of Sparse Feed-Forward Network in Pretraining Large Language Model.

The power of fine-grained experts: Granularity boosts expressivity in Mixture of Experts Towards A Unified View of Sparse Feed-Forward Network in Pretraining Large Language Model

Reference 2020

Resolution: verified exact; local_arxiv, observed 2026-08-15T22:46:44.710594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7ce640f6-c721-4693-89fe-c8983745d0dc · outbound

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

The power of fine-grained experts: Granularity boosts expressivity in Mixture of Experts DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-15T22:46:44.557649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af40d205-c36f-4223-a4d9-fe8c05e1f3ea · outbound

This paper cites DeepSeek-V3 Technical Report.

The power of fine-grained experts: Granularity boosts expressivity in Mixture of Experts DeepSeek-V3 Technical Report

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-15T22:46:44.602434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a1667a6-f474-45fc-89d6-c299ce6083d5 · outbound

This paper cites Mixtral of Experts.

The power of fine-grained experts: Granularity boosts expressivity in Mixture of Experts Mixtral of Experts

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T22:46:44.578388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb3e4a9c-6470-4949-a038-3f529b28ba10 · outbound

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

The power of fine-grained experts: Granularity boosts expressivity in Mixture of Experts Scaling Laws for Fine-Grained Mixture of Experts

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-15T22:46:44.587867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 78d5c2fa-e4b1-4a29-a7e1-4096c62be107 · inbound

How to Scale Mixture-of-Experts: From muP to the Maximally Scale-Stable Parameterization cites this paper.

How to Scale Mixture-of-Experts: From muP to the Maximally Scale-Stable Parameterization The power of fine-grained experts: Granularity boosts expressivity in Mixture of Experts

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-15T04:49:44.905137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.