Paper Citation Record · LEDGER

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs

As of 10 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2602.19938.

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

pith.paper-citation-record.v1
2602.19938 v2

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T21:32:47.917300Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

31 of 31 outbound references displayed

verified exact0
verified fuzzy0
unresolved30
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

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Outbound references

Observation c5649613-9af0-490d-aee7-85a81c5d09e0 · outbound

This paper cites Deep Rewiring: Training very sparse deep networks.

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs Deep Rewiring: Training very sparse deep networks

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:44.948013Z

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Observation 26f304c9-6c42-4ac7-8b60-0fc3bfc08edf · outbound

This paper cites The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 6

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Observation 3fba3b89-ab0e-4b0e-a054-683de3c8271a · outbound

This paper cites Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference.

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:45.535224Z

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Observation be9d72b0-5d09-4624-a639-712d70c7d671 · outbound

This paper cites MathPrompter: Mathematical Reasoning using Large Language Models.

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs MathPrompter: Mathematical Reasoning using Large Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:45.586588Z

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Observation a16666b4-85b6-4f95-8b37-244edddc5327 · outbound

This paper cites Scaling Laws for Neural Language Models.

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs Scaling Laws for Neural Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:45.815295Z

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Observation bf7cb693-3216-476d-aae0-c18d55c558fc · outbound

This paper cites SwapMoE: Serving Off-the-shelf MoE-based Large Language Models with Tunable Memory Budget.

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs SwapMoE: Serving Off-the-shelf MoE-based Large Language Models with Tunable Memory Budget

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:45.891720Z

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Observation 17699da9-e3dd-44f2-b44e-2c75b053d9d0 · outbound

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

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:45.963798Z

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Observation ecd4fd35-43a1-4ae2-ad23-d9c4901028dd · outbound

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

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs TruthfulQA: Measuring How Models Mimic Human Falsehoods

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:46.028389Z

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Observation 1e87c76d-2f96-4b48-a16d-4167011f5afb · outbound

This paper cites AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models.

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:46.101296Z

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Observation 9e41dd5c-4aa4-40b2-b200-12d63ee3e27c · outbound

This paper cites LLM-Pruner: On the Structural Pruning of Large Language Models.

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs LLM-Pruner: On the Structural Pruning of Large Language Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:46.231928Z

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Observation 7a8a08df-5f80-47b7-9ffc-862559cd3f3f · outbound

This paper cites Pruning Convolutional Neural Networks for Resource Efficient Inference.

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs Pruning Convolutional Neural Networks for Resource Efficient Inference

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:46.323377Z

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Observation f3ec516e-e2c7-4ccf-849a-da6909f3db15 · outbound

This paper cites https://aclanthology.org/Q19-1016/.

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs https://aclanthology.org/Q19-1016/

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:46.565024Z

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Observation 0240ea56-1bcd-4876-9813-92b052dcdf30 · outbound

This paper cites Systematic Characterization of LLM Quantization: A Performance, Energy, and Quality Perspective.

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs Systematic Characterization of LLM Quantization: A Performance, Energy, and Quality Perspective

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:46.741030Z

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Observation 640d47b3-c827-4530-8609-4c1171b90e33 · outbound

This paper cites MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning.

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:46.952975Z

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Observation 5747fa34-0ae2-40c9-a46c-b9e4e25a1ec5 · outbound

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

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs Auxiliary-Loss-Free Load Balancing Strategy for Mixture-of-Experts

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:47.069093Z

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Observation dd601836-1c38-4c44-8c73-b5ac115df73c · outbound

This paper cites Scaling Relationship on Learning Mathematical Reasoning with Large Language Models.

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs Scaling Relationship on Learning Mathematical Reasoning with Large Language Models

Reference 26

Resolution: malformed identifier; no resolver link, observed 2026-08-02T21:32:47.190057Z

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Observation 1dab9dbf-2ff2-4733-a74f-14cdf17ce36f · outbound

This paper cites Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights.

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:47.700690Z

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Observation e0bd5800-dffa-442c-b380-5b2bef93cdc7 · outbound

This paper cites Exploring Sparse MoE in GANs for Text-conditioned Image Synthesis.

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs Exploring Sparse MoE in GANs for Text-conditioned Image Synthesis

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:47.822382Z

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Observation 72bf5afb-77d3-4ae0-af99-d779c209ab0a · outbound

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

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs ST-MoE: Designing Stable and Transferable Sparse Expert Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:47.917300Z

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Observation a40d2930-fdbf-445b-9d2c-2df26913bf5a · outbound

This paper cites Mixtral of Experts.

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs Mixtral of Experts

Reference 1991

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:45.765434Z

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Observation 6ba0ceff-4272-4239-832b-8fa7681de94f · outbound

This paper cites GRACE-MoE: Grouping and Replication with Locality-Aware Routing for Efficient Distributed MoE Inference.

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs GRACE-MoE: Grouping and Replication with Locality-Aware Routing for Efficient Distributed MoE Inference

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:45.476307Z

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Observation 2c82cef0-921c-4e14-b83f-4b266164cd0b · outbound

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

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:47.576363Z

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Observation 12f5d8b2-ac3f-44c6-8603-a738968efac1 · outbound

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

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs PIQA: Reasoning about Physical Commonsense in Natural Language

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:45.022568Z

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Observation 30dd69dd-16ed-488e-9087-118f33bdf256 · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:45.369082Z

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Observation a13b915e-d7a6-4cbf-8d5e-9333396d095c · outbound

This paper cites Load balancing mixture of experts with similarity preserving routers, 2025.https://arxiv.org/abs/2506.14038.

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs Load balancing mixture of experts with similarity preserving routers, 2025.https://arxiv.org/abs/2506.14038

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:46.495634Z

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Observation 38e2c223-ba81-467f-ae84-d88585e7db20 · outbound

This paper cites Sergey Zagoruyko and Nikos Komodakis.

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs Sergey Zagoruyko and Nikos Komodakis

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:47.342765Z

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Observation 48e7364a-526e-43d0-b9ca-bdd52b718f6d · outbound

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

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs WinoGrande: An Adversarial Winograd Schema Challenge at Scale

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:46.661601Z

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Observation 91ba37c3-0a0d-4940-9a6c-5cc4c030616b · outbound

This paper cites PaLM: Scaling Language Modeling with Pathways.

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs PaLM: Scaling Language Modeling with Pathways

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:45.108043Z

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Observation 60a34feb-a324-4079-a8c2-30d24acc0b46 · outbound

This paper cites Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference.

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:45.681616Z

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Observation aa8540e2-59da-45f2-89cc-8733b036db1e · outbound

This paper cites LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale.

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:45.167870Z

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Observation f5098165-2ac2-4d9e-aede-d79affeade7e · outbound

This paper cites Zachary Doucet, Rishi Sharma, Martijn de Vos, Rafael Pires, Anne-Marie Kermarrec, and Oana Balmau.

A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs Zachary Doucet, Rishi Sharma, Martijn de Vos, Rafael Pires, Anne-Marie Kermarrec, and Oana Balmau

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:45.229607Z

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Pith citing papers

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