Paper Citation Record · LEDGER

Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism

As of 15 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2507.05753.

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

pith.paper-citation-record.v1
2507.05753 v1

Coverage vector

measured 48 of 48 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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External citation measurements

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Outbound references

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This paper cites an unresolved cited work.

Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Unresolved cited work

Reference 1

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Unresolved cited work

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This paper cites A Foundation Model for the Earth System.

Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism A Foundation Model for the Earth System

Reference 3

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Unresolved cited work

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This paper cites FuXi: A cascade machine learning forecasting system for 15-day global weather forecast.

Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism FuXi: A cascade machine learning forecasting system for 15-day global weather forecast

Reference 5

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This paper cites TSMixer: An All-MLP Architecture for Time Series Forecasting.

Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism TSMixer: An All-MLP Architecture for Time Series Forecasting

Reference 6

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Unresolved cited work

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Unresolved cited work

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Unresolved cited work

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 10

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Unresolved cited work

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Unresolved cited work

Reference 12

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Unresolved cited work

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Unresolved cited work

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Unresolved cited work

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Inness, M

Reference 16

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Observation bca503c4-a00d-4d58-bf4a-1a85e4efab74 · outbound

This paper cites Scaling Laws for Neural Language Models.

Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Scaling Laws for Neural Language Models

Reference 17

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Observation e8736da3-f236-4084-ad06-67affb612b14 · outbound

This paper cites Forecasting Global Weather with Graph Neural Networks.

Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Forecasting Global Weather with Graph Neural Networks

Reference 18

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This paper cites On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima.

Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima

Reference 19

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Unresolved cited work

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Unresolved cited work

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This paper cites Oriol Vinyals, Jacklynn Stott, Alexander Pritzel, Shakir Mohamed, and Pe- ter Battaglia.

Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Oriol Vinyals, Jacklynn Stott, Alexander Pritzel, Shakir Mohamed, and Pe- ter Battaglia

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism AIFS -- ECMWF's data-driven forecasting system

Reference 24

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Robertson, and José A

Reference 25

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning

Reference 26

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Unresolved cited work

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Gupta, and Aditya Grover

Reference 28

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This paper cites Scaling transformer neural networks for skillful and reliable medium-range weather forecasting.

Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Scaling transformer neural networks for skillful and reliable medium-range weather forecasting

Reference 29

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Regional data-driven weather modeling with a global stretched-grid

Reference 30

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Unresolved cited work

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Generalized Slow Roll for Tensors

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism WeatherBench 2: A benchmark for the next generation of data-driven global weather models

Reference 33

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Unresolved cited work

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Taylor, Ronald J

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Unresolved cited work

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Unresolved cited work

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism van de Geijn and Jerrell Watts

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Gomez, Łukasz Kaiser, and Illia Polosukhin

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Aji, Ming Fan, Wei Zhang, Junqi Yin, Moetasim Ashfaq, Dan Lu, and Prasanna Balaprakash

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Hamrud, Christian Kuehnlein, S

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Unresolved cited work

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism SIGARCH Comput

Reference 2017

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This paper cites Yet Another Accelerated SGD: ResNet-50 Training on ImageNet in 74.7 seconds.

Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Yet Another Accelerated SGD: ResNet-50 Training on ImageNet in 74.7 seconds

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Umweltbundesamt

Reference 2020

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This paper cites Nature Machine Intelligence 5, 11 (2023), 1176–1178.

Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Nature Machine Intelligence 5, 11 (2023), 1176–1178

Reference 2023

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Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism Hilfer fractional advection-diffusion equations with power-law initial condition; a Numerical study using variational iteration method

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