Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-10T12:15:38.522144Z
Paper Citation Record · LEDGER
DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery
As of 14 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2607.08150.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
27 of 27 outbound references displayed
- verified exact2
- verified fuzzy24
- unresolved1
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation d8bf22d1-a85d-4fe6-bc25-f6d6b336c171 · outbound
This paper cites Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.Nature Machine Intelligence, 1(5):206–215.
DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.Nature Machine Intelligence, 1(5):206–215
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T12:17:04.189644Z
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 0db17f30-efff-462b-ac0c-eb494603471d · outbound
This paper cites A unified approach to interpreting model predictions.
DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery A unified approach to interpreting model predictions
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T12:17:04.155619Z
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 f52b3d41-7f88-4ce0-bf71-f78bd3c917a5 · outbound
This paper cites why should I trust you?.
DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery why should I trust you?
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T12:17:04.187349Z
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 8f529848-af3f-43ab-bde7-572d58bfb77f · outbound
This paper cites On the robustness of interpretability methods.
DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery On the robustness of interpretability methods
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T12:17:04.191219Z
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 b6e58d3a-5251-4101-9bd1-ddff2873b346 · outbound
This paper cites Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods.
DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T12:17:04.192995Z
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 511d287b-39ff-45af-86d4-9045efd3556b · outbound
This paper cites MIT Press, 1994.
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T12:17:04.194533Z
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 9fb51b5b-b6e1-4558-a8db-ecebd1adef2e · outbound
This paper cites Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl.
DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl
Reference 7
- Resolution
- verified exact
- local_arxiv, observed 2026-07-10T12:17:03.795007Z
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 4a43bdf8-c76a-4cbe-80bd-c59b26192ce6 · outbound
This paper cites AI Feynman: A physics-inspired method for symbolic regression.Science Advances, 6(16):eaay2631, 2020.
DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery AI Feynman: A physics-inspired method for symbolic regression.Science Advances, 6(16):eaay2631, 2020
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T12:17:04.199729Z
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 4853dc16-491c-443e-b549-d1489ed65af8 · outbound
This paper cites KAN: Kolmogorov-Arnold Networks.
DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery KAN: Kolmogorov-Arnold Networks
Reference 9
- Resolution
- verified exact
- local_arxiv, observed 2026-07-10T12:17:03.791614Z
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 4ff47b0c-950c-4d5a-9db2-e9d34609a849 · outbound
This paper cites an unresolved cited work.
DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery Unresolved cited work
Reference 10
- Resolution
- unresolved
- raw_fallback, observed 2026-07-10T12:17:04.181917Z
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 3c90ed26-70e5-4e1c-9169-e556f959b2f7 · outbound
This paper cites Interpretable policies for reinforcement learning by genetic programming.Engineering Applications of Artificial Intelligence, 76:158–169, 2018.
DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery Interpretable policies for reinforcement learning by genetic programming.Engineering Applications of Artificial Intelligence, 76:158–169, 2018
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T12:17:04.183694Z
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 285ffde8-a57a-411c-a630-25a7044f1582 · outbound
This paper cites Improving model-based genetic programming for symbolic regression of small expressions.Evolutionary Computation, 29(2):211–237, 2021.
DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery Improving model-based genetic programming for symbolic regression of small expressions.Evolutionary Computation, 29(2):211–237, 2021
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T12:17:04.176036Z
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 4eea89bb-786a-4ae9-9b6d-05b1c3112fb1 · outbound
This paper cites Integration of neural network-based symbolic regression in deep learning for scientific discovery.IEEE Transactions on Neural Networks and Learning Systems, 32(9): 4166–4177, 2020.
DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery Integration of neural network-based symbolic regression in deep learning for scientific discovery.IEEE Transactions on Neural Networks and Learning Systems, 32(9): 4166–4177, 2020
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T12:17:04.178071Z
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 740f873c-5acb-456e-9291-e88219af6521 · outbound
This paper cites Individual comparisons by ranking methods.Biometrics Bulletin, 1(6):80–83.
DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery Individual comparisons by ranking methods.Biometrics Bulletin, 1(6):80–83
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T12:17:04.180245Z
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 841e88a8-d478-4802-bc2c-89bddfe0caf6 · outbound
This paper cites Fitting percentage of body fat to simple body measurements.Journal of Statistics Education, 4(1), 1996.
DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery Fitting percentage of body fat to simple body measurements.Journal of Statistics Education, 4(1), 1996
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T12:17:04.196262Z
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 472718af-1427-4083-b3a7-9489b1ab67eb · outbound
This paper cites International application of a new probability algorithm for the diagnosis of coronary artery disease.American Journal of Cardiology, 64(5):304–310, 1989.
DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery International application of a new probability algorithm for the diagnosis of coronary artery disease.American Journal of Cardiology, 64(5):304–310, 1989
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T12:17:04.170520Z
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 21c5d6f5-0a33-4687-b9ca-74edcb02d1ef · outbound
This paper cites Cohort profile: The western australian pregnancy cohort (raine) study—generation 2.
DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery Cohort profile: The western australian pregnancy cohort (raine) study—generation 2
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T12:17:04.172254Z
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 56113d75-43e3-4ab1-b90f-2205d0a1409b · outbound
This paper cites Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors.Nature Genetics, 51(5):804–814, 2019.
DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors.Nature Genetics, 51(5):804–814, 2019
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T12:17:04.174194Z
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 10969d46-2bb5-4fd3-88b7-f4e75b0c7410 · outbound
This paper cites Prenatal stress, development, health and disease risk: A 2015 Delphi consensus and call for action.Psychoneuroendocrinology, 62: 366–375, 2015.
DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery Prenatal stress, development, health and disease risk: A 2015 Delphi consensus and call for action.Psychoneuroendocrinology, 62: 366–375, 2015
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T12:17:04.185460Z
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 4000776e-832d-49bc-bb0f-89783726abad · outbound
This paper cites Cohort profile: Pregnancy and childhood epigenetics (PACE) consortium.International Journal of Epidemiology, 47(1):22–23, 2018.
DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery Cohort profile: Pregnancy and childhood epigenetics (PACE) consortium.International Journal of Epidemiology, 47(1):22–23, 2018
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T12:17:04.165095Z
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 11ff041a-530d-4350-9519-d6c6bb3bc429 · outbound
This paper cites Fetal origins of coronary heart disease.BMJ, 311(6998):171–174, 1995.
DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery Fetal origins of coronary heart disease.BMJ, 311(6998):171–174, 1995
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T12:17:04.166752Z
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 0b65e79b-4980-439a-b28e-2da8ac6a1ea1 · outbound
This paper cites Modeling wine preferences by data mining from physicochemical properties.Decision Support Systems, 47(4):547–553, 2009.
DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery Modeling wine preferences by data mining from physicochemical properties.Decision Support Systems, 47(4):547–553, 2009
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T12:17:04.161434Z
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 3ba2a96e-3d35-47f6-a519-de961060f9ed · outbound
This paper cites Using data mining to predict secondary school student performance.
DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery Using data mining to predict secondary school student performance
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T12:17:04.198063Z
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 296c9da9-e986-406b-a414-bb0252a6f0d7 · outbound
This paper cites Behavioral risk factor surveillance system survey data.
DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery Behavioral risk factor surveillance system survey data
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T12:17:04.159703Z
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 9372c579-9a74-4726-98e3-3849b7b76046 · outbound
This paper cites XGBoost: A scalable tree boosting system.
DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery XGBoost: A scalable tree boosting system
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T12:17:04.163208Z
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 09d32eae-bee4-4cd7-a916-10a548ee8c86 · outbound
This paper cites Random forests.Machine Learning, 45:5–32.
DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery Random forests.Machine Learning, 45:5–32
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T12:17:04.168716Z
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 bb60130e-3ddd-4575-9b2f-902789b5a97f · outbound
This paper cites Extremely randomized trees.Machine Learning, 63:3–42, 2006.
DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery Extremely randomized trees.Machine Learning, 63:3–42, 2006
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T12:17:04.157860Z
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
No inbound Pith citation observations are available.