Paper Citation Record · LEDGER

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression

As of 7 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2506.08267.

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

pith.paper-citation-record.v1
2506.08267 v2

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:23:09.215595Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

32 of 32 outbound references displayed

verified exact0
verified fuzzy23
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0dfb89b7-c4cf-487d-bd54-e97f156218ed · outbound

This paper cites A computational framework for physics-informed symbolic regression with straightforward integra- tion of domain knowledge,.

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression A computational framework for physics-informed symbolic regression with straightforward integra- tion of domain knowledge,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:23:09.600982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 66c2c078-61e3-46ee-875e-32e514dea55e · outbound

This paper cites Deep learning and symbolic regression for discovering parametric equations,.

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression Deep learning and symbolic regression for discovering parametric equations,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:23:09.588409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f29d9873-5e19-48df-8b73-e15f1f76922b · outbound

This paper cites Deep symbolic regression for physics guided by units constraints: toward the automated discovery of physical laws,.

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression Deep symbolic regression for physics guided by units constraints: toward the automated discovery of physical laws,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:23:09.575697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f9e9d9e2-2704-4c23-b662-4be86233fe3d · outbound

This paper cites Data-driven discovery of partial differential equations,.

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression Data-driven discovery of partial differential equations,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:23:09.560565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 75b5f27e-1419-4da1-982c-b16991462cec · outbound

This paper cites Symbolic regression in materials science,.

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression Symbolic regression in materials science,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:23:09.542095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5603ffd8-11bb-4135-8dfc-7141eae646be · outbound

This paper cites Discovering symbolic models from deep learn- ing with inductive biases,.

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression Discovering symbolic models from deep learn- ing with inductive biases,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:23:09.530136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9446b293-fa9d-4465-a58b-66fc67de2cd2 · outbound

This paper cites Ai feynman: A physics-inspired method for symbolic regression,.

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression Ai feynman: A physics-inspired method for symbolic regression,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:23:09.518935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 385b48f6-8785-44d7-ae51-601d98360ce0 · outbound

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

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression Contemporary symbolic regression methods and their relative performance,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:23:09.506363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5dcb6d78-a2dc-4b2d-b6f3-7ce3c861ebb5 · outbound

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

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression Distilling free-form natural laws from experimental data,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T05:23:09.124668Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 302727f1-fa40-451c-a2a7-c33d8545953c · outbound

This paper cites Taylor genetic pro- gramming for symbolic regression,.

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression Taylor genetic pro- gramming for symbolic regression,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:23:09.486265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 24be1fcc-b94d-4f15-b1ed-5ec37e9e13c0 · outbound

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

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T05:23:09.132410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42353a13-9902-48f9-90db-1d0f0125c155 · outbound

This paper cites Transformer- based planning for symbolic regression,.

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression Transformer- based planning for symbolic regression,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:23:09.474236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6b3e5860-4c8f-409e-9f49-5ebfb3de884a · outbound

This paper cites Sym- former: End-to-end symbolic regression using transformer-based archi- tecture,.

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression Sym- former: End-to-end symbolic regression using transformer-based archi- tecture,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:23:09.462363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 58cc4036-4a5b-462c-93dd-d0c7f70ae490 · outbound

This paper cites Deep gener- ative symbolic regression with monte-carlo-tree-search,.

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression Deep gener- ative symbolic regression with monte-carlo-tree-search,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:23:09.450335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c1a52998-c6f7-476c-9576-604cfa09f963 · outbound

This paper cites Genetic programming as a means for programming comput- ers by natural selection,.

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression Genetic programming as a means for programming comput- ers by natural selection,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:23:09.437376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation da84cbc3-ac91-4d77-9d24-7baddf5d766c · outbound

This paper cites On improving genetic programming for symbolic regression,.

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression On improving genetic programming for symbolic regression,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:23:09.425749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 94736e25-0921-4b9f-8d24-aa8040015130 · outbound

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

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T05:23:09.155921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1ddb413-2617-4afd-a69c-aff9a6590153 · outbound

This paper cites A seq2seq approach to symbolic regression,.

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression A seq2seq approach to symbolic regression,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:23:09.413929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8ef43189-5e3c-4ca8-9857-771b870c3ff3 · outbound

This paper cites Gaussian processes for machine learning.,(mit press: Cambridge, ma),.

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression Gaussian processes for machine learning.,(mit press: Cambridge, ma),

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:23:09.402209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 784ba4d1-7adb-4ba2-809c-a5f3052875ff · outbound

This paper cites Scientific machine learning through physics–informed neural networks: Where we are and what’s next,.

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression Scientific machine learning through physics–informed neural networks: Where we are and what’s next,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T05:23:09.168508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 724df5e6-5816-4bf4-8c02-6fef9868ae51 · outbound

This paper cites Learning equations for extrap- olation and control,.

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression Learning equations for extrap- olation and control,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:23:09.382861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 75052c60-86d3-4f74-bd46-ba33bfbf2d7a · outbound

This paper cites Integration of neural network-based symbolic regression in deep learning for scientific discovery,.

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression Integration of neural network-based symbolic regression in deep learning for scientific discovery,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:23:09.369763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c933a812-040e-4023-97fe-b22d11277b93 · outbound

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

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression Discovering governing equations from data by sparse identification of nonlinear dynamical systems,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T05:23:09.180213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5eb34f52-7249-400e-aa45-953e0a1ac196 · outbound

This paper cites Efficient symbolic policy learning with differentiable symbolic expression,.

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression Efficient symbolic policy learning with differentiable symbolic expression,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:23:09.357188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 787f3bc8-b358-4fe1-b371-8d399b57017e · outbound

This paper cites Noise-resilient symbolic regression with dynamic gating reinforcement learning,.

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression Noise-resilient symbolic regression with dynamic gating reinforcement learning,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:23:09.344614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8b742912-3837-44e2-99b3-2cf0052f84ae · outbound

This paper cites A systematic dnn weight pruning framework using alternating direction method of multipliers,.

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression A systematic dnn weight pruning framework using alternating direction method of multipliers,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:23:09.332578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b906df22-07cd-4f91-b8cf-f9a626d11faf · outbound

This paper cites Distributed optimization and statistical learning via the alternating direction method of multipliers,.

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression Distributed optimization and statistical learning via the alternating direction method of multipliers,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T05:23:09.196073Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8fb440ad-f1c0-4ead-9bac-f3d2ff05bcb5 · outbound

This paper cites Pruning Convolutional Neural Networks for Resource Efficient Inference.

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression Pruning Convolutional Neural Networks for Resource Efficient Inference

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T05:23:09.200100Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49a4d659-7703-4ca7-8ec2-bbe7f9851d36 · outbound

This paper cites Optimal brain damage,.

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression Optimal brain damage,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T05:23:09.204209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8c4d063-f911-4820-ad6e-4fcae4c86911 · outbound

This paper cites Interactive symbolic regression with co-design mechanism through offline reinforcement learning,.

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression Interactive symbolic regression with co-design mechanism through offline reinforcement learning,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:23:09.306339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b0949632-2ee5-4e4a-8a7d-de6998527ef7 · outbound

This paper cites Enhancing sparsity by reweightedℓ 1 minimization,.

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression Enhancing sparsity by reweightedℓ 1 minimization,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:23:09.293825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d0719613-6a7e-4219-87b6-5eb56a90e57a · outbound

This paper cites DARTS: Differentiable Architecture Search.

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression DARTS: Differentiable Architecture Search

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T05:23:09.215595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.