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Paper Citation Record · LEDGER

Contemporary Symbolic Regression Methods and their Relative Performance

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 22 inbound Pith citation observations for arXiv:2107.14351.

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

pith.paper-citation-record.v1
2107.14351 v1

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measured 0 of 0 reference resolution

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measured 22 of 22 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 22 of 22 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:27:34.556220Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-03T02:37:34.637254Z

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Outbound references

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Pith citing papers

Observation 1cbd9ef7-a159-4cd2-8d5e-d8b2c2c1c640 · inbound

SymbolFit: Automatic Parametric Modeling with Symbolic Regression cites this paper.

SymbolFit: Automatic Parametric Modeling with Symbolic Regression Contemporary Symbolic Regression Methods and their Relative Performance

Reference 4

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Observation 74635ebf-3dea-4955-9ca9-d741bbdc3ae8 · inbound

Knowledge-based model validation using a custom metric cites this paper.

Knowledge-based model validation using a custom metric Contemporary Symbolic Regression Methods and their Relative Performance

Reference 47

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Observation fe5af8a5-bd09-49ee-85be-d0c413dfdb45 · inbound

AutoSciLab: A Self-Driving Laboratory For Interpretable Scientific Discovery cites this paper.

AutoSciLab: A Self-Driving Laboratory For Interpretable Scientific Discovery Contemporary Symbolic Regression Methods and their Relative Performance

Reference 24

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Machine Learning-Based Analytical Expressions for Gray-Body Factors and Application to Primordial Black Holes cites this paper.

Machine Learning-Based Analytical Expressions for Gray-Body Factors and Application to Primordial Black Holes Contemporary Symbolic Regression Methods and their Relative Performance

Reference 14

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Observation 9d7546fb-d007-4bcb-94f9-75cdeb533036 · inbound

Thinking Outside the Template with Modular GP-GOMEA cites this paper.

Thinking Outside the Template with Modular GP-GOMEA Contemporary Symbolic Regression Methods and their Relative Performance

Reference 13

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Observation 96fa6361-94b7-4c9f-bf3f-46046ddb1996 · inbound

Symbolic Regression with Multimodal Large Language Models and Kolmogorov Arnold Networks cites this paper.

Symbolic Regression with Multimodal Large Language Models and Kolmogorov Arnold Networks Contemporary Symbolic Regression Methods and their Relative Performance

Reference 10

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Observation a94de04e-2f10-49c1-a792-4af181a83b0f · inbound

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback cites this paper.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback Contemporary Symbolic Regression Methods and their Relative Performance

Reference 8

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Observation 24a08719-8017-44c8-9391-92a9b1453b5b · inbound

Diffusion-Based Symbolic Regression cites this paper.

Diffusion-Based Symbolic Regression Contemporary Symbolic Regression Methods and their Relative Performance

Reference 13

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Observation 05ccaf6b-e6ae-47aa-85d0-da1b3692cd02 · inbound

(Exhaustive) Symbolic Regression and model selection by minimum description length cites this paper.

(Exhaustive) Symbolic Regression and model selection by minimum description length Contemporary Symbolic Regression Methods and their Relative Performance

Reference 12

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

Fast Symbolic Regression Benchmarking cites this paper.

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

Reference 2

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Observation 48e44be6-77c2-4257-b296-17e62f1f090e · inbound

Data-Efficient Symbolic Regression via Foundation Model Distillation cites this paper.

Data-Efficient Symbolic Regression via Foundation Model Distillation Contemporary Symbolic Regression Methods and their Relative Performance

Reference 8

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Observation fd57189e-5b38-4d44-8463-827cb3701542 · inbound

Exploring Multi-view Symbolic Regression methods in physical sciences cites this paper.

Exploring Multi-view Symbolic Regression methods in physical sciences Contemporary Symbolic Regression Methods and their Relative Performance

Reference 17

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Observation 16841cff-9f5b-423c-a51f-290a3fd72049 · inbound

In-Context Symbolic Regression for Robustness-Improved Kolmogorov-Arnold Networks cites this paper.

In-Context Symbolic Regression for Robustness-Improved Kolmogorov-Arnold Networks Contemporary Symbolic Regression Methods and their Relative Performance

Reference 14

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arxiv_id, observed 2026-05-15T10:15:26.746699Z

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Observation e1be0c5d-e5f9-4da5-bfc1-1a18d516c932 · inbound

Programmatic Context Augmentation for LLM-based Symbolic Regression cites this paper.

Programmatic Context Augmentation for LLM-based Symbolic Regression Contemporary Symbolic Regression Methods and their Relative Performance

Reference 31

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arxiv_id, observed 2026-05-09T06:25:48.473754Z

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Observation c8d3d54c-ff74-4138-8244-72e10654267f · inbound

Symbolic Classification-Enabled LHC Limits Online BSM Global Fits cites this paper.

Symbolic Classification-Enabled LHC Limits Online BSM Global Fits Contemporary Symbolic Regression Methods and their Relative Performance

Reference 40

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 9bb3abf6-9375-41a9-b0ae-e3e6c0d8f21b · inbound

GP-GOMEA with GPU-Based Fitness Evaluations: Design and Performance Analysis cites this paper.

GP-GOMEA with GPU-Based Fitness Evaluations: Design and Performance Analysis Contemporary Symbolic Regression Methods and their Relative Performance

Reference 4

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arxiv_id, observed 2026-06-28T20:22:37.111515Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation e15e3e86-4784-4594-96ae-4781c2c21084 · inbound

FunctionEvolve: Structure-Guided Symbolic Regression with LLMs cites this paper.

FunctionEvolve: Structure-Guided Symbolic Regression with LLMs Contemporary Symbolic Regression Methods and their Relative Performance

Reference 37

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arxiv_id, observed 2026-07-02T16:17:09.468394Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation b524a645-ba45-4a23-bd07-4bcea6c65f8e · inbound

CMBolic: Symbolic emulators for the Cosmic Microwave Background. I. Lensing cites this paper.

CMBolic: Symbolic emulators for the Cosmic Microwave Background. I. Lensing Contemporary Symbolic Regression Methods and their Relative Performance

Reference 23

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Learning the Universe: The Structure of Dust Attenuation Curves in Galaxy Simulations cites this paper.

Learning the Universe: The Structure of Dust Attenuation Curves in Galaxy Simulations Contemporary Symbolic Regression Methods and their Relative Performance

Reference 216

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Observation 05305c75-ce9e-4197-8a1b-80b3d92c0d1d · inbound

Sample Complexity of Scientific Discovery: PAC Learnability of Compositional Function Trees cites this paper.

Sample Complexity of Scientific Discovery: PAC Learnability of Compositional Function Trees Contemporary Symbolic Regression Methods and their Relative Performance

Reference 55

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 0b6b780d-7197-4f63-83f1-5225471f9882 · inbound

A-SR: Self-Evolving Agentic LLMs for Symbolic Regression via Hierarchical Coordination cites this paper.

A-SR: Self-Evolving Agentic LLMs for Symbolic Regression via Hierarchical Coordination Contemporary Symbolic Regression Methods and their Relative Performance

Reference 25

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NestyNet. I. Physics Functions Are Hard to Fit with Neural Networks: A Framework for Accurate Surrogates and Analytic Derivatives cites this paper.

NestyNet. I. Physics Functions Are Hard to Fit with Neural Networks: A Framework for Accurate Surrogates and Analytic Derivatives Contemporary Symbolic Regression Methods and their Relative Performance

Reference 26

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