Paper Citation Record · LEDGER

Fast Symbolic Regression Benchmarking

As of 14 August 2026, this Paper Citation Record lists 10 of 10 outbound references and 0 inbound Pith citation observations for arXiv:2508.14481.

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

pith.paper-citation-record.v1
2508.14481 v1

Coverage vector

measured 10 of 10 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T18:37:58.157921Z

measured 10 of 10 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

10 of 10 outbound references displayed

verified exact2
verified fuzzy3
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 700d78cc-a1a6-4b23-ab60-97ddbb9370a3 · outbound

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

Fast Symbolic Regression Benchmarking Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T18:37:57.256258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36bd77cb-c8db-4e7f-b07a-81fea35a342c · outbound

This paper cites Contemporary Symbolic Regression Methods and their Relative Performance.

Fast Symbolic Regression Benchmarking Contemporary Symbolic Regression Methods and their Relative Performance

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T18:37:57.349266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b35e988-b03f-4aa4-a8f4-60d030819d0e · outbound

This paper cites Introducing Thermodynamics-Informed Symbolic Regression -- A Tool for Thermodynamic Equations of State Development.

Fast Symbolic Regression Benchmarking Introducing Thermodynamics-Informed Symbolic Regression -- A Tool for Thermodynamic Equations of State Development

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-08-05T18:37:58.511476Z

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 643ad786-23d2-4369-a601-ef0b5b0ba3a8 · outbound

This paper cites Thermodynamics-informed Symbolic Regression - A Tool for the Thermodynamic Equation of State De- velopment 2025.

Fast Symbolic Regression Benchmarking Thermodynamics-informed Symbolic Regression - A Tool for the Thermodynamic Equation of State De- velopment 2025

Reference 4

Resolution: verified exact; doi, observed 2026-08-05T18:37:58.319584Z

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 0c28c193-5ca0-47e8-9e19-11113c7949f6 · outbound

This paper cites Rethinking Symbolic Re- gression Datasets and Benchmarks for Scientific Discovery.Journal of Data- centric Machine Learning Research.https :/ /openreview.

Fast Symbolic Regression Benchmarking Rethinking Symbolic Re- gression Datasets and Benchmarks for Scientific Discovery.Journal of Data- centric Machine Learning Research.https :/ /openreview

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:37:59.190767Z

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 3bbedaff-1587-4677-9cca-d9fe560219aa · outbound

This paper cites P.; Paprocki, M.; Čertík, O.; Kirpichev, S.

Fast Symbolic Regression Benchmarking P.; Paprocki, M.; Čertík, O.; Kirpichev, S

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T18:37:57.741810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dcf22f04-fbd8-4eb9-9cbe-74f0aa25f740 · outbound

This paper cites GNU Parallel 2018 doi:10.

Fast Symbolic Regression Benchmarking GNU Parallel 2018 doi:10

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:37:59.045054Z

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 faec013b-9e28-4abd-8703-05bb77f23df2 · outbound

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

Fast Symbolic Regression Benchmarking AI Feynman: A physics-inspired method for symbolic regression

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:37:58.887838Z

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 ff0f3397-053f-4889-ae9b-71ca31a87504 · outbound

This paper cites an unresolved cited work.

Fast Symbolic Regression Benchmarking Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-05T18:37:58.694434Z

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 c3a67289-215a-4c18-a192-d0696aa68806 · outbound

This paper cites Symbolic Regression is NP-hard.

Fast Symbolic Regression Benchmarking Symbolic Regression is NP-hard

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T18:37:58.157921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.