Paper Citation Record · LEDGER

Scaling Laws for Fine-Grained Mixture of Experts

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 49 inbound Pith citation observations for arXiv:2402.07871.

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

pith.paper-citation-record.v1
2402.07871 v1

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measured 49 of 49 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 1 of 1 external citation measurements

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

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External citation measurements

3
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 55220a9d-b5c6-428f-b64d-973a86cb10ba · inbound

Ultra-Sparse Memory Network cites this paper.

Ultra-Sparse Memory Network Scaling Laws for Fine-Grained Mixture of Experts

Reference 24

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ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing cites this paper.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Scaling Laws for Fine-Grained Mixture of Experts

Reference 26

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Observation 743d0cea-2e11-4eb2-99ef-552c3de26c33 · inbound

Scaling Inference-Efficient Language Models cites this paper.

Scaling Inference-Efficient Language Models Scaling Laws for Fine-Grained Mixture of Experts

Reference 23

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Observation 2745ea1f-7427-405e-851a-79a3be989fa8 · inbound

Soup-of-Experts: Pretraining Specialist Models via Parameters Averaging cites this paper.

Soup-of-Experts: Pretraining Specialist Models via Parameters Averaging Scaling Laws for Fine-Grained Mixture of Experts

Reference 28

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Observation d2574811-c62c-4e89-af7d-ca2f6aad0326 · inbound

Scaling Laws for Upcycling Mixture-of-Experts Language Models cites this paper.

Scaling Laws for Upcycling Mixture-of-Experts Language Models Scaling Laws for Fine-Grained Mixture of Experts

Reference 35

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Observation 55cd9535-3e4b-4db5-aaa9-f9f4f0b56bfc · inbound

Training Sparse Mixture Of Experts Text Embedding Models cites this paper.

Training Sparse Mixture Of Experts Text Embedding Models Scaling Laws for Fine-Grained Mixture of Experts

Reference 9

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Observation dfb99475-540a-47e6-87b4-efed5b4f22c7 · inbound

Revisiting Transformers through the Lens of Low Entropy and Dynamic Sparsity cites this paper.

Revisiting Transformers through the Lens of Low Entropy and Dynamic Sparsity Scaling Laws for Fine-Grained Mixture of Experts

Reference 22

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Observation 9fbe071d-ce53-49c7-a60f-8c837cfbaf15 · inbound

Position: Enough of Scaling LLMs! Lets Focus on Downscaling cites this paper.

Position: Enough of Scaling LLMs! Lets Focus on Downscaling Scaling Laws for Fine-Grained Mixture of Experts

Reference 16

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

The power of fine-grained experts: Granularity boosts expressivity in Mixture of Experts cites this paper.

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

Reference 2025

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$\mu$-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts cites this paper.

$\mu$-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts Scaling Laws for Fine-Grained Mixture of Experts

Reference 30

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Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights cites this paper.

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

Reference 16

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Observation 98c88a95-962e-476d-851c-d2b5d70c0932 · inbound

A Theory of Inference Compute Scaling: Reasoning through Directed Stochastic Skill Search cites this paper.

A Theory of Inference Compute Scaling: Reasoning through Directed Stochastic Skill Search Scaling Laws for Fine-Grained Mixture of Experts

Reference 102

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Observation 400a9e8f-9b73-45ca-8fbd-4df819028559 · inbound

LoRA-Mixer: Coordinate Modular LoRA Experts Through Serial Attention Routing cites this paper.

LoRA-Mixer: Coordinate Modular LoRA Experts Through Serial Attention Routing Scaling Laws for Fine-Grained Mixture of Experts

Reference 52

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Observation 7060e794-53d6-4614-9403-522dba46b907 · inbound

BlockFFN: Towards End-Side Acceleration-Friendly Mixture-of-Experts with Chunk-Level Activation Sparsity cites this paper.

BlockFFN: Towards End-Side Acceleration-Friendly Mixture-of-Experts with Chunk-Level Activation Sparsity Scaling Laws for Fine-Grained Mixture of Experts

Reference 20

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Observation 7cb64e95-77f8-483e-8777-90264f295ad8 · inbound

Maximum Score Routing For Mixture-of-Experts cites this paper.

Maximum Score Routing For Mixture-of-Experts Scaling Laws for Fine-Grained Mixture of Experts

Reference 21

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Observation e766e8a8-41d6-4b09-a7bd-4f0b18b05e96 · inbound

UltraMemV2: Memory Networks Scaling to 120B Parameters with Superior Long-Context Learning cites this paper.

UltraMemV2: Memory Networks Scaling to 120B Parameters with Superior Long-Context Learning Scaling Laws for Fine-Grained Mixture of Experts

Reference 25

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Observation 9b19e677-ad68-4a5e-a034-715ac56ca56c · inbound

ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution cites this paper.

ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution Scaling Laws for Fine-Grained Mixture of Experts

Reference 163

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Observation 8e35aa42-8db1-4f09-a7d3-54e90d3459ab · inbound

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs cites this paper.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Scaling Laws for Fine-Grained Mixture of Experts

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-18T05:30:55.127020Z

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Observation 575042b2-3f11-4599-9691-8941c8874a45 · inbound

Grouter: Decoupling Routing from Representation for Accelerated MoE Training cites this paper.

Grouter: Decoupling Routing from Representation for Accelerated MoE Training Scaling Laws for Fine-Grained Mixture of Experts

Reference 10

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Generalization and Scaling Laws for Mixture-of-Experts Transformers cites this paper.

Generalization and Scaling Laws for Mixture-of-Experts Transformers Scaling Laws for Fine-Grained Mixture of Experts

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-10T20:25:46.779709Z

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Train Separately, Merge Together: Modular Post-Training with Mixture-of-Experts cites this paper.

Train Separately, Merge Together: Modular Post-Training with Mixture-of-Experts Scaling Laws for Fine-Grained Mixture of Experts

Reference 20

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Observation d8efa6c9-f131-4c80-aa40-b8c5b9c1904b · inbound

Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts cites this paper.

Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts Scaling Laws for Fine-Grained Mixture of Experts

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-10T03:29:21.467504Z

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Observation 7b0baec4-ce32-48b3-bd14-064caa41a3b2 · inbound

Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts cites this paper.

Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts Scaling Laws for Fine-Grained Mixture of Experts

Reference 26

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Observation 45123722-e5eb-4cf8-af05-7f7c9e722391 · inbound

There Will Be a Scientific Theory of Deep Learning cites this paper.

There Will Be a Scientific Theory of Deep Learning Scaling Laws for Fine-Grained Mixture of Experts

Reference 125

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A Limit Theory of Foundation Models: A Mathematical Approach to Understanding Emergent Intelligence and Scaling Laws cites this paper.

A Limit Theory of Foundation Models: A Mathematical Approach to Understanding Emergent Intelligence and Scaling Laws Scaling Laws for Fine-Grained Mixture of Experts

Reference 69

Resolution: verified exact; arxiv_id, observed 2026-05-13T07:27:28.919417Z

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A Limit Theory of Foundation Models: A Mathematical Approach to Understanding Emergent Intelligence and Scaling Laws cites this paper.

A Limit Theory of Foundation Models: A Mathematical Approach to Understanding Emergent Intelligence and Scaling Laws Scaling Laws for Fine-Grained Mixture of Experts

Reference 70

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Observation 1e64b776-ec3c-4bba-9cdc-53598699f42b · inbound

UniPool: A Globally Shared Expert Pool for Mixture-of-Experts cites this paper.

UniPool: A Globally Shared Expert Pool for Mixture-of-Experts Scaling Laws for Fine-Grained Mixture of Experts

Reference 25

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DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices cites this paper.

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices Scaling Laws for Fine-Grained Mixture of Experts

Reference 129

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:36:20.061044Z

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Observation 820d46e4-9493-4e7b-9dee-e5422aa56357 · inbound

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices cites this paper.

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices Scaling Laws for Fine-Grained Mixture of Experts

Reference 129

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DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices cites this paper.

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices Scaling Laws for Fine-Grained Mixture of Experts

Reference 129

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Scaling Laws for Mixture Pretraining Under Data Constraints cites this paper.

Scaling Laws for Mixture Pretraining Under Data Constraints Scaling Laws for Fine-Grained Mixture of Experts

Reference 4

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Observation 5159d113-4454-4e88-8f60-7fdc40fdf00a · inbound

Scaling Laws for Mixture Pretraining Under Data Constraints cites this paper.

Scaling Laws for Mixture Pretraining Under Data Constraints Scaling Laws for Fine-Grained Mixture of Experts

Reference 22

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Observation 6bf453b0-12b7-4e14-b8f2-f9317e9ebfba · inbound

Dense vs Sparse Pretraining at Tiny Scale: Active-Parameter vs Total-Parameter Matching cites this paper.

Dense vs Sparse Pretraining at Tiny Scale: Active-Parameter vs Total-Parameter Matching Scaling Laws for Fine-Grained Mixture of Experts

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-14T19:19:23.630198Z

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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 Scaling Laws for Fine-Grained Mixture of Experts

Reference 5

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Geometric Asymmetry in MoE Specialization: Functional Decorrelation and Representational Overlap cites this paper.

Geometric Asymmetry in MoE Specialization: Functional Decorrelation and Representational Overlap Scaling Laws for Fine-Grained Mixture of Experts

Reference 4

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MobileMoE: Scaling On-Device Mixture of Experts cites this paper.

MobileMoE: Scaling On-Device Mixture of Experts Scaling Laws for Fine-Grained Mixture of Experts

Reference 30

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Observation d0415849-9705-47e4-b36e-c54fdcd1b953 · inbound

NUCLEUS-MoE: Unified Model of Pool Boiling for Liquid Cooling cites this paper.

NUCLEUS-MoE: Unified Model of Pool Boiling for Liquid Cooling Scaling Laws for Fine-Grained Mixture of Experts

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-06-29T18:33:50.529632Z

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Observation a8bf0ac9-82c1-4dc2-b71c-6541060d964b · inbound

Mellum2 Technical Report cites this paper.

Mellum2 Technical Report Scaling Laws for Fine-Grained Mixture of Experts

Reference 35

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Sparsely gated tiny linear experts cites this paper.

Sparsely gated tiny linear experts Scaling Laws for Fine-Grained Mixture of Experts

Reference 2

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Observation bcf148dd-48f2-4345-a0c2-b446fb0ff84c · inbound

Sakana Fugu Technical Report cites this paper.

Sakana Fugu Technical Report Scaling Laws for Fine-Grained Mixture of Experts

Reference 197

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Observation 7468852c-78b1-402e-be66-805467163583 · inbound

Mixture-of-Control: State-Aware Fine-Tuning for Transformer-based Models cites this paper.

Mixture-of-Control: State-Aware Fine-Tuning for Transformer-based Models Scaling Laws for Fine-Grained Mixture of Experts

Reference 98

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Observation 144fa094-ef37-4303-9c48-495b7e44c7f4 · inbound

A Sovereign, Open-Source Foundation Model for German and English cites this paper.

A Sovereign, Open-Source Foundation Model for German and English Scaling Laws for Fine-Grained Mixture of Experts

Reference 41

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A Sovereign, Open-Source Foundation Model for German and English cites this paper.

A Sovereign, Open-Source Foundation Model for German and English Scaling Laws for Fine-Grained Mixture of Experts

Reference 41

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A Sovereign, Open-Source Foundation Model for German and English cites this paper.

A Sovereign, Open-Source Foundation Model for German and English Scaling Laws for Fine-Grained Mixture of Experts

Reference 41

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SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD cites this paper.

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD Scaling Laws for Fine-Grained Mixture of Experts

Reference 121

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Is MoE Routing a Huffman Code? Discovering the Frequency-Diversity Law in Chain-of-Thought cites this paper.

Is MoE Routing a Huffman Code? Discovering the Frequency-Diversity Law in Chain-of-Thought Scaling Laws for Fine-Grained Mixture of Experts

Reference 31

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Observation 80569cfd-2968-4e13-864e-cd06e2a97b27 · inbound

Scale Weight Decay and Train Better cites this paper.

Scale Weight Decay and Train Better Scaling Laws for Fine-Grained Mixture of Experts

Reference 61

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LLaDA MoE v2: Scaling Mixture-of-Experts Diffusion Language Models cites this paper.

LLaDA MoE v2: Scaling Mixture-of-Experts Diffusion Language Models Scaling Laws for Fine-Grained Mixture of Experts

Reference 67

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Compute-Optimal Is Not Cluster-Optimal: Systems-Aware Scaling for Sparse Mixture-of-Experts cites this paper.

Compute-Optimal Is Not Cluster-Optimal: Systems-Aware Scaling for Sparse Mixture-of-Experts Scaling Laws for Fine-Grained Mixture of Experts

Reference 5

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