Paper Citation Record · LEDGER

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective

As of 14 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2507.19540.

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

pith.paper-citation-record.v1
2507.19540 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:08:27.155691Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

30 of 30 outbound references displayed

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External citation measurements

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Outbound references

Observation 796019d4-3544-456d-a903-a2f394813022 · outbound

This paper cites 2023 Scientific discovery in the age of artificial intelligence.

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective 2023 Scientific discovery in the age of artificial intelligence

Reference 1

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Observation c259fd2e-6e93-4267-8faa-16c0db864df8 · outbound

This paper cites 2023 Combining data and theory for derivable scientific discovery with AI-Descartes.

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective 2023 Combining data and theory for derivable scientific discovery with AI-Descartes

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:08:34.078091Z

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Observation 3221c3ab-f449-442a-9003-5924b04b362c · outbound

This paper cites 1987 Scientific Discovery: Computational Explorations of the Creative Processes.

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective 1987 Scientific Discovery: Computational Explorations of the Creative Processes

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:08:33.858412Z

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Observation f79c170f-d520-4017-8113-6d383edba191 · outbound

This paper cites 2007 Computational Discovery of Scientific Knowledge.

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective 2007 Computational Discovery of Scientific Knowledge

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:08:33.577919Z

Source-reported events for the cited work

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Observation 92773c9a-850f-4d1c-a77c-3e03b6de46f4 · outbound

This paper cites 1992 Genetic programming: On the programming of computers by means of natural selection.

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective 1992 Genetic programming: On the programming of computers by means of natural selection

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:08:33.386685Z

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Observation 56f3399f-e6bf-43a1-8ef8-19410d5c4151 · outbound

This paper cites 2009 Distilling free-form natural laws from experimental data.

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective 2009 Distilling free-form natural laws from experimental data

Reference 6

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Source-reported events for the cited work

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Observation 61c00904-596c-4316-87d2-8d9cca0048bc · outbound

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

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T15:08:24.390500Z

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Observation 79b05de8-c553-4966-b6bc-e94a2915db88 · outbound

This paper cites 2016 Discovering governing equations from data by sparse identification of nonlinear dynamical systems.

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective 2016 Discovering governing equations from data by sparse identification of nonlinear dynamical systems

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:08:33.088016Z

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Observation 3cca19f9-799a-4a9e-a008-f20f0ceee156 · outbound

This paper cites 2021 Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients.

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective 2021 Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T15:08:24.679184Z

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Observation 787ab6cb-6d7b-46d4-b86b-f1586297becb · outbound

This paper cites 2023 Efficient generator of mathematical expressions for symbolic regression.

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective 2023 Efficient generator of mathematical expressions for symbolic regression

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:08:32.758086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b7a0e365-6515-491e-94ac-73b8d0ced907 · outbound

This paper cites 2020 AI Feynman: A physics-inspired method for symbolic regression.

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective 2020 AI Feynman: A physics-inspired method for symbolic regression

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:08:32.572646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 42960114-fbda-4e13-accd-3c23b5127825 · outbound

This paper cites 2021 Contemporary symbolic regression methods and their relative performance.

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective 2021 Contemporary symbolic regression methods and their relative performance

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:08:32.275279Z

Source-reported events for the cited work

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Observation 9e61c963-ab3f-45e8-b07d-66af983a30b3 · outbound

This paper cites 2024 Interpretable scientific discovery with symbolic regression: a review.

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective 2024 Interpretable scientific discovery with symbolic regression: a review

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T15:08:25.251422Z

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Observation a159a430-bfc5-427b-976c-498703e85275 · outbound

This paper cites 2020 A Bayesian machine scientist to aid in the solution of challenging scientific problems.

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective 2020 A Bayesian machine scientist to aid in the solution of challenging scientific problems

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:08:31.962196Z

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Observation ddb7c585-b952-4143-88bb-20693d248ffe · outbound

This paper cites 2020 Bayesian machine scientist to compare data collapses for the Nikuradse dataset.

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective 2020 Bayesian machine scientist to compare data collapses for the Nikuradse dataset

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:08:31.670183Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e72493ae-8c63-4076-ac0a-ab80ee42a89e · outbound

This paper cites 2023 Fundamental limits to learning closed-form mathematical models from data.

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective 2023 Fundamental limits to learning closed-form mathematical models from data

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:08:31.292576Z

Source-reported events for the cited work

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Observation 5802d3b2-4b6d-4355-bc49-2dd70a79e783 · outbound

This paper cites 1978 Estimating the dimension of a model.

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective 1978 Estimating the dimension of a model

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:08:31.088497Z

Source-reported events for the cited work

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Observation c77640eb-d166-43ae-aa81-2006ff8564d7 · outbound

This paper cites 2010 Bayesian model selection and statistical modeling.

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective 2010 Bayesian model selection and statistical modeling

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:08:30.846892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 604cd066-c859-488c-a3ce-d734fe571362 · outbound

This paper cites 2024 Exhaustive Symbolic Regression.

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective 2024 Exhaustive Symbolic Regression

Reference 19

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Observation c60159ca-32a1-4e34-8002-2239e3198f47 · outbound

This paper cites 2007 The Minimum Description Length Principle.

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective 2007 The Minimum Description Length Principle

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:08:30.488202Z

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Observation 0400f026-8b39-4d62-91a4-0064a14742b4 · outbound

This paper cites 2019 Information theory for fields.

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective 2019 Information theory for fields

Reference 21

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Source-reported events for the cited work

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Observation 497c1551-9896-470d-8744-c8f2fc8f216e · outbound

This paper cites 1946 Probability, frequency and reasonable expectation.

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective 1946 Probability, frequency and reasonable expectation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:08:30.109832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8b1fee9f-aa8d-4853-b9d6-32f09c858f89 · outbound

This paper cites 2003 Probability Theory: The Logic of Science.

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective 2003 Probability Theory: The Logic of Science

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:08:29.898171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0d7e46af-51db-4a84-a32e-95a024c0b549 · outbound

This paper cites 2022 Dutch Book Arguments.

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective 2022 Dutch Book Arguments

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:08:29.586269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d9c5500c-931b-4313-80e8-6e7c979709bb · outbound

This paper cites 2023 Priors for symbolic regression.

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective 2023 Priors for symbolic regression

Reference 25

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T15:08:27.903676Z

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Observation 1b4d8ecf-4fc4-4c4b-8d45-1751aff33d2b · outbound

This paper cites 2024 Incorporating background knowledge in symbolic regression using a computer algebra system.Machine Learning: Science and Technology 5, 025057.

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective 2024 Incorporating background knowledge in symbolic regression using a computer algebra system.Machine Learning: Science and Technology 5, 025057

Reference 26

Resolution: verified exact; doi, observed 2026-08-06T15:08:27.435707Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a5acacf1-4811-41d8-95d3-c12b68a40fce · outbound

This paper cites 1999 Bayesian model averaging: A tutorial.

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective 1999 Bayesian model averaging: A tutorial

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:08:29.350175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 656f97ea-9e3d-4680-8d37-580a9b169026 · outbound

This paper cites 2019 Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective 2019 Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:08:28.980777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ad10ec96-0c9c-4e53-802c-1b6b718602d4 · outbound

This paper cites 2025 Human mobility is well described by closed-form gravity-like models learned automatically from data.Nat.

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective 2025 Human mobility is well described by closed-form gravity-like models learned automatically from data.Nat

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:08:28.767152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8856f7ac-c0c3-46d5-bcd8-69cccc39b13b · outbound

This paper cites 2024 Artificial intelligence needs a scientific method-driven reset.

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective 2024 Artificial intelligence needs a scientific method-driven reset

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:08:28.447958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

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