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Paper Citation Record · LEDGER

Foundation Models for Astrophysics

As of 19 August 2026, this Paper Citation Record lists 100 of 150 outbound references and 0 inbound Pith citation observations for arXiv:2608.02573.

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

pith.paper-citation-record.v1
2608.02573 v1

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measured 100 of 150 reference resolution

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100 of 150 outbound references displayed

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Outbound references

Observation 29ebe95e-d8b3-40f5-94af-b657ea3cf861 · outbound

This paper cites Flamingo: a Visual Language Model for Few-Shot Learning.

Foundation Models for Astrophysics Flamingo: a Visual Language Model for Few-Shot Learning

Reference 1

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Observation 26c6557c-e35d-4ade-9f5e-03615cab7379 · outbound

This paper cites Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture.

Foundation Models for Astrophysics Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture

Reference 2

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This paper cites V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning.

Foundation Models for Astrophysics V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

Reference 3

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Observation 540b1abd-29b5-476d-91b6-d4839e5bd9e4 · outbound

This paper cites Foundational Models Defining a New Era in Vision: A Survey and Outlook.

Foundation Models for Astrophysics Foundational Models Defining a New Era in Vision: A Survey and Outlook

Reference 4

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Observation b3b542ca-2185-477d-a495-257f197e1f0f · outbound

This paper cites Neural Machine Translation by Jointly Learning to Align and Translate.

Foundation Models for Astrophysics Neural Machine Translation by Jointly Learning to Align and Translate

Reference 5

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Observation 7e61598e-ad19-4fb8-9d3d-0db53db384ee · outbound

This paper cites Neural networks and principal component analysis: Learning from examples without local minima.Neural Networks, 2(1):53–58, 1989.

Foundation Models for Astrophysics Neural networks and principal component analysis: Learning from examples without local minima.Neural Networks, 2(1):53–58, 1989

Reference 6

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This paper cites LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics.

Foundation Models for Astrophysics LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics

Reference 7

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Observation 1d9a1eda-d738-426b-b628-3bad9c4ca388 · outbound

This paper cites Multimodal Machine Learning: A Survey and Taxonomy.

Foundation Models for Astrophysics Multimodal Machine Learning: A Survey and Taxonomy

Reference 8

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Observation f90b8607-da84-44a7-ae78-bc3bf6561d10 · outbound

This paper cites John Wiley & Sons, 2011.

Foundation Models for Astrophysics John Wiley & Sons, 2011

Reference 9

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Observation 4c6541f0-5963-4a80-8946-49e433a25fed · outbound

This paper cites Exhaustive Symbolic Regression.

Foundation Models for Astrophysics Exhaustive Symbolic Regression

Reference 10

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Observation 54574ada-f10a-4e79-9eba-03ed86e7fec5 · outbound

This paper cites Relational inductive biases, deep learning, and graph networks.

Foundation Models for Astrophysics Relational inductive biases, deep learning, and graph networks

Reference 11

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Observation 58d5bacc-76fa-4730-a1a7-5d8add574b94 · outbound

This paper cites Representation learning: A review and new perspectives.IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8):1798–1828, August 2013.

Foundation Models for Astrophysics Representation learning: A review and new perspectives.IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8):1798–1828, August 2013

Reference 12

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Observation df09dbf6-8269-4215-b4e7-b45dd2f8685a · outbound

This paper cites Bishop.Pattern Recognition and Machine Learning.

Foundation Models for Astrophysics Bishop.Pattern Recognition and Machine Learning

Reference 13

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Observation 43f03da9-4d75-49d0-9c5b-ccebcd205334 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Foundation Models for Astrophysics On the Opportunities and Risks of Foundation Models

Reference 14

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This paper cites RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control.

Foundation Models for Astrophysics RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

Reference 15

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Observation 6fae5a61-a1cc-4a03-b280-032e492ed839 · outbound

This paper cites Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges.

Foundation Models for Astrophysics Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

Reference 16

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Observation e5b491c9-e60b-4f2b-89a9-435012adad48 · outbound

This paper cites Language Models are Few-Shot Learners.

Foundation Models for Astrophysics Language Models are Few-Shot Learners

Reference 17

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Observation d866e8f4-6c34-43cb-8c90-45ee39748da4 · outbound

This paper cites Deep Multimodal Representation Learning for Stellar Spectra.

Foundation Models for Astrophysics Deep Multimodal Representation Learning for Stellar Spectra

Reference 18

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Observation f1d662f4-04ef-4cb1-ba19-bfe11f0a2657 · outbound

This paper cites Emerging Properties in Self-Supervised Vision Transformers.

Foundation Models for Astrophysics Emerging Properties in Self-Supervised Vision Transformers

Reference 19

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Observation 5595abc8-73bc-49db-98d0-a2b76278f7f1 · outbound

This paper cites Multitask learning.Machine learning, 28(1):41–75, 1997.

Foundation Models for Astrophysics Multitask learning.Machine learning, 28(1):41–75, 1997

Reference 20

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This paper cites Chameleon: Mixed-Modal Early-Fusion Foundation Models.

Foundation Models for Astrophysics Chameleon: Mixed-Modal Early-Fusion Foundation Models

Reference 21

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Observation ac2feeda-5869-4304-bda3-2c46e6f41ebc · outbound

This paper cites A Simple Framework for Contrastive Learning of Visual Representations.

Foundation Models for Astrophysics A Simple Framework for Contrastive Learning of Visual Representations

Reference 22

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This paper cites Group equivariant convolutional networks.

Foundation Models for Astrophysics Group equivariant convolutional networks

Reference 23

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This paper cites The frontier of simulation-based infer- ence.Proceedings of the National Academy of Science, 117(48):30055–30062, December 2020.

Foundation Models for Astrophysics The frontier of simulation-based infer- ence.Proceedings of the National Academy of Science, 117(48):30055–30062, December 2020

Reference 24

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This paper cites Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl.

Foundation Models for Astrophysics Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl

Reference 25

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This paper cites Discovering Symbolic Models from Deep Learning with Inductive Biases.

Foundation Models for Astrophysics Discovering Symbolic Models from Deep Learning with Inductive Biases

Reference 26

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This paper cites Sparse Autoencoders Find Highly Interpretable Features in Language Models.

Foundation Models for Astrophysics Sparse Autoencoders Find Highly Interpretable Features in Language Models

Reference 27

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Foundation Models for Astrophysics Unresolved cited work

Reference 28

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This paper cites Identifiability Results for Multimodal Contrastive Learning.

Foundation Models for Astrophysics Identifiability Results for Multimodal Contrastive Learning

Reference 29

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Foundation Models for Astrophysics Zico Kolter

Reference 30

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Foundation Models for Astrophysics BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 31

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Foundation Models for Astrophysics Donoso-Oliva, I

Reference 32

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

Foundation Models for Astrophysics An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 33

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This paper cites Understanding Emergent Abilities of Language Models from the Loss Perspective.

Foundation Models for Astrophysics Understanding Emergent Abilities of Language Models from the Loss Perspective

Reference 34

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This paper cites Scalable Pre-training of Large Autoregressive Image Models.

Foundation Models for Astrophysics Scalable Pre-training of Large Autoregressive Image Models

Reference 35

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This paper cites Why does unsupervised pre-training help deep learning?Journal of Machine Learning Research, 11(19):625–660, 2010.

Foundation Models for Astrophysics Why does unsupervised pre-training help deep learning?Journal of Machine Learning Research, 11(19):625–660, 2010

Reference 36

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This paper cites Mellier, Abdurro’uf, J.

Foundation Models for Astrophysics Mellier, Abdurro’uf, J

Reference 37

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This paper cites Neocognitron: A self-organizing neural network model for a mech- anism of pattern recognition unaffected by shift in position.Biological cybernetics, 36(4):193–202, 1980.

Foundation Models for Astrophysics Neocognitron: A self-organizing neural network model for a mech- anism of pattern recognition unaffected by shift in position.Biological cybernetics, 36(4):193–202, 1980

Reference 38

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This paper cites Garc ´ıa P´erez, Carlos Allende Prieto, Jon A.

Foundation Models for Astrophysics Garc ´ıa P´erez, Carlos Allende Prieto, Jon A

Reference 39

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Observation b891bf2b-2d80-47d1-9f25-35c690c1dca1 · outbound

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Observation 394c04b6-06bd-4075-9dd4-a17d91bec131 · outbound

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Observation 40eaaf64-e3b0-4b98-8702-183b562bce2b · outbound

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Observation 4be58a86-b7f1-46b5-ac18-b33a65f327cf · outbound

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Observation 426750ab-d39d-4547-bc2c-4ab873832546 · outbound

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Observation af3c124b-aab0-4bbc-bc97-fdd1ad98defc · outbound

This paper cites an unresolved cited work.

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Observation 27a28394-5f76-417f-b8c1-eec5351c1f94 · outbound

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Observation 6ec5f658-4e81-4b97-8f5f-8ad0d27559ff · outbound

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Observation f1b471da-91b3-469d-8972-235a283f7cbf · outbound

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Observation 73c26f2a-b514-4766-b00d-e4670ddd5811 · outbound

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Observation 16b35c02-518b-4f4a-938f-297f7416eddc · outbound

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Observation c0f679cd-2451-47c2-9f58-7159085805c9 · outbound

This paper cites an unresolved cited work.

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Observation 01060522-262a-4b6e-83fe-d3d9a266de77 · outbound

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Observation 18a07232-92c9-4259-842a-41594899ff97 · outbound

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Observation 68736bbc-827c-40bd-a8ff-ed26e491dcb8 · outbound

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Observation 05adb0eb-4d8e-4a70-8413-907cdaf72e99 · outbound

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Observation 7b38f41f-cb35-4238-9cc3-b8cde1df56c1 · outbound

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Observation c135ba4e-94fb-4030-bc3b-4208c9952154 · outbound

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Observation dd9d3942-773c-4d65-80d4-9a5e61066310 · outbound

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Observation 2a365e9c-86af-433d-822d-0529135b98bb · outbound

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Observation cba61947-d68b-4220-a752-e3329e6c3233 · outbound

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Foundation Models for Astrophysics Unresolved cited work

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Observation 2f801d4f-eabb-4675-a5cf-048cc2532aae · outbound

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Observation 8b2bce69-b7b1-417b-85e3-17f37f0ab3a4 · outbound

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Observation 49f88f68-c3af-4640-afb6-64d04471b7df · outbound

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Observation a550a367-da32-4d9f-9895-a1ab3e196b32 · outbound

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Observation b37fab68-c866-48ff-a20d-f6c0a56a159f · outbound

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Observation 99ba5a28-ab60-4970-900c-aaa0332ed1d5 · outbound

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Observation f18683bd-80b7-4033-b85f-8be36d39d43a · outbound

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Observation 596901ed-c63b-4915-be60-7e6437c9d31b · outbound

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Observation e08595c9-1e24-460e-9ce6-82009d59b6fe · outbound

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Observation a6f0b268-5eb5-4390-b9ca-0832e2da8d67 · outbound

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Observation 5ee6aca0-e7b6-426b-9255-1c30354ce12b · outbound

This paper cites an unresolved cited work.

Foundation Models for Astrophysics Unresolved cited work

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Observation f2ada00a-0f0c-4e02-8e71-b17e195f60df · outbound

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Observation 7b3f0ba9-e346-41b5-b733-c9262f0f0966 · outbound

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Observation 115e4a2d-a94e-4cfc-94e9-40f40a07e8a8 · outbound

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Observation 2f40a7ea-ed27-42e5-b89d-70cc0916dbbe · outbound

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Observation 3b8ae88d-db21-461c-b028-9d99dd564923 · outbound

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Observation bc770726-aa42-4b84-b6e9-f3f92988238a · outbound

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Observation 2cdff265-c62d-49c0-ae0a-4a29e2b26521 · outbound

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Observation 247b0f9c-a48b-48c9-8544-5d7520d188ce · outbound

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Foundation Models for Astrophysics Differentiable Stellar Atmospheres with Physics-Informed Neural Networks

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Observation fdc3b59e-ffdc-4230-b0ab-23375b4f97d9 · outbound

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Foundation Models for Astrophysics Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning

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Observation 930ec956-6457-4a34-a7ec-c412c68d049a · outbound

This paper cites Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations.

Foundation Models for Astrophysics Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

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Observation b7ff1658-0b03-4b91-baeb-86010f2c7efe · outbound

This paper cites Fama—a scalable foundational astronomical masked autoencoder for astronomical image analysis.The Astrophysical Journal Supplement Series, 283(2):49, mar 2026.

Foundation Models for Astrophysics Fama—a scalable foundational astronomical masked autoencoder for astronomical image analysis.The Astrophysical Journal Supplement Series, 283(2):49, mar 2026

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Observation a6235e8a-ef07-4e0f-82f5-589fe9d6b0fb · outbound

This paper cites Masters, Chris J.

Foundation Models for Astrophysics Masters, Chris J

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Observation cde19b91-3b63-4354-99b1-90fb92c9da15 · outbound

This paper cites Walrus: A Cross-Domain Foundation Model for Continuum Dynamics.

Foundation Models for Astrophysics Walrus: A Cross-Domain Foundation Model for Continuum Dynamics

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Observation fca2db98-1d86-4e25-84b3-9c883a55a259 · outbound

This paper cites Multiple Physics Pretraining for Physical Surrogate Models.

Foundation Models for Astrophysics Multiple Physics Pretraining for Physical Surrogate Models

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Observation 0906e878-bdba-4d25-93f8-c0729021c264 · outbound

This paper cites A logical calculus of the ideas immanent in nervous activity.The bulletin of mathematical biophysics, 5(4):115–133, 1943.

Foundation Models for Astrophysics A logical calculus of the ideas immanent in nervous activity.The bulletin of mathematical biophysics, 5(4):115–133, 1943

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Observation 5fb55caa-3c81-4369-bb08-35965813de2c · outbound

This paper cites Efficient Estimation of Word Representations in Vector Space.

Foundation Models for Astrophysics Efficient Estimation of Word Representations in Vector Space

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Observation 3d2794b5-44e8-4841-80fc-704e99b11205 · outbound

This paper cites Re- current neural network based language model.

Foundation Models for Astrophysics Re- current neural network based language model

Reference 88

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Observation 151eddc3-4447-448c-82fe-e0dbb8a2f70f · outbound

This paper cites 4M: Massively Multimodal Masked Modeling.

Foundation Models for Astrophysics 4M: Massively Multimodal Masked Modeling

Reference 89

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Observation 007279fc-4bf4-4056-bff2-a5048711e3d7 · outbound

This paper cites Progress measures for grokking via mechanistic interpretability.

Foundation Models for Astrophysics Progress measures for grokking via mechanistic interpretability

Reference 90

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Observation 1aa1b4fd-fc57-4320-86f3-1fbd43208e4e · outbound

This paper cites Yi, Kim Venn, and Spencer Bialek.

Foundation Models for Astrophysics Yi, Kim Venn, and Spencer Bialek

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Observation 50ac3627-0ac4-4d5b-9ae7-cd8b88575cd2 · outbound

This paper cites Emergence of simple-cell receptive field properties by learning a sparse code for natural images.Nature, 381(6583):607–609, 1996.

Foundation Models for Astrophysics Emergence of simple-cell receptive field properties by learning a sparse code for natural images.Nature, 381(6583):607–609, 1996

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Observation 7b04085a-4413-468f-9f1f-6bf52cc980ad · outbound

This paper cites Debiasing with Diffusion: Probabilistic Reconstruction of Dark Matter Fields from Galaxies with CAMELS.ApJ, 970(2):174, August 2024.

Foundation Models for Astrophysics Debiasing with Diffusion: Probabilistic Reconstruction of Dark Matter Fields from Galaxies with CAMELS.ApJ, 970(2):174, August 2024

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Observation 5890d641-9805-4d6c-bf86-f284c18aa992 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Foundation Models for Astrophysics DINOv2: Learning Robust Visual Features without Supervision

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Observation eeccf5d1-d2e4-4b67-8ec9-61a26cc0afc1 · outbound

This paper cites SKATR: A self-supervised summary transformer for SKA.SciPost Physics, 18(5):155, May 2025.

Foundation Models for Astrophysics SKATR: A self-supervised summary transformer for SKA.SciPost Physics, 18(5):155, May 2025

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Observation 8565998e-1619-4771-85cb-a9d5985ebb7b · outbound

This paper cites Training language models to follow instructions with human feedback.

Foundation Models for Astrophysics Training language models to follow instructions with human feedback

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Observation 33d73756-5cbc-4597-b745-0806cca3f902 · outbound

This paper cites The Scaling Law in Stellar Light Curves.

Foundation Models for Astrophysics The Scaling Law in Stellar Light Curves

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Observation 02d8042a-f960-4d15-9c6c-41bf4defbd12 · outbound

This paper cites A survey on transfer learning.IEEE Trans.

Foundation Models for Astrophysics A survey on transfer learning.IEEE Trans

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Observation 46ca1b79-241a-43c6-9090-2c2a9fb5fa83 · outbound

This paper cites Zhao & Y .-S.

Foundation Models for Astrophysics Zhao & Y .-S

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Observation f4507b4c-24e5-421d-b939-914e66180738 · outbound

This paper cites AION-1: Omnimodal Foundation Model for Astronomical Sciences.arXiv e-prints, page arXiv:2510.17960, October 2025.

Foundation Models for Astrophysics AION-1: Omnimodal Foundation Model for Astronomical Sciences.arXiv e-prints, page arXiv:2510.17960, October 2025

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