Paper Citation Record · LEDGER

Computational Math with Neural Networks is Hard

As of 8 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 1 inbound Pith citation observation for arXiv:2505.17751.

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

pith.paper-citation-record.v1
2505.17751 v1

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:51:24.498177Z

measured 55 of 55 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-09T19:35:31.236089Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T15:36:08.139823Z

Reference resolution

54 of 54 outbound references displayed

verified exact11
verified fuzzy21
unresolved19
parse uncertain0
malformed identifier2
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 69b391a1-4646-4e13-b9c2-e66c0c843cc8 · outbound

This paper cites Neural Operators for Accelerating Scientific Simulations and Design.

Computational Math with Neural Networks is Hard Neural Operators for Accelerating Scientific Simulations and Design

Reference 1

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Source-reported events for the cited work

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Observation c50a3091-b8ba-4be2-966b-fefc2f229d89 · outbound

This paper cites Volumes of sections of cubes and related problems.

Computational Math with Neural Networks is Hard Volumes of sections of cubes and related problems

Reference 2

Resolution: verified exact; doi, observed 2026-08-07T14:51:27.416253Z

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 9d1343d5-0777-4225-941d-874970fc2cda · outbound

This paper cites Neural networks as smooth priors for inverse problems for PDEs.

Computational Math with Neural Networks is Hard Neural networks as smooth priors for inverse problems for PDEs

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:33.427328Z

Source-reported events for the cited work

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Observation ae75b1e7-c6e4-4845-80db-46e18b076d8a · outbound

This paper cites Some remarkable properties of sinc and related integrals.

Computational Math with Neural Networks is Hard Some remarkable properties of sinc and related integrals

Reference 4

Resolution: verified exact; doi, observed 2026-08-07T14:51:27.177124Z

Source-reported events for the cited work

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Observation 0c88e1ec-562f-4ae2-90b5-a012e66d00e4 · outbound

This paper cites The complexity of satisfiability of small depth circuits.

Computational Math with Neural Networks is Hard The complexity of satisfiability of small depth circuits

Reference 5

Resolution: verified exact; doi, observed 2026-08-07T14:51:26.879732Z

Source-reported events for the cited work

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Observation 9f519fe7-a29f-448f-86e7-74102bfb6dce · outbound

This paper cites Solving the quantum many-body problem with artificial neural networks.

Computational Math with Neural Networks is Hard Solving the quantum many-body problem with artificial neural networks

Reference 6

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Source-reported events for the cited work

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Observation f06426b8-ebb8-4d73-8338-6c388d0bc7d7 · outbound

This paper cites an unresolved cited work.

Computational Math with Neural Networks is Hard Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:51:33.136400Z

Source-reported events for the cited work

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Observation 7c88b814-5e69-40a2-bba8-032c684073db · outbound

This paper cites Approximation algorithms for training one-node ReLU neural networks.

Computational Math with Neural Networks is Hard Approximation algorithms for training one-node ReLU neural networks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T14:51:20.729038Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54455d0a-3a66-44d3-b060-d3c984658103 · outbound

This paper cites High-dimensional integration: The quasi-Monte Carlo way.

Computational Math with Neural Networks is Hard High-dimensional integration: The quasi-Monte Carlo way

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:32.858327Z

Source-reported events for the cited work

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Observation 82dc33c1-b458-44e3-bfa4-bbe71a2975db · outbound

This paper cites Expression of Fractals Through Neural Network Functions.

Computational Math with Neural Networks is Hard Expression of Fractals Through Neural Network Functions

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:32.576739Z

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 0cfba83c-1fe8-406e-af44-9bbaaa6bd9fe · outbound

This paper cites Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning.

Computational Math with Neural Networks is Hard Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:32.343175Z

Source-reported events for the cited work

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Observation 5dfd182e-2090-48f8-aa13-56240e4ccfa6 · outbound

This paper cites The Deep Ritz Method: A Deep Learning-Based Numerical Algorithm for Solving Variational Problems.

Computational Math with Neural Networks is Hard The Deep Ritz Method: A Deep Learning-Based Numerical Algorithm for Solving Variational Problems

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T14:51:21.054908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e2a0f69-d819-4870-a359-e46108422383 · outbound

This paper cites The deep Ritz method: a deep learning-based numerical algorithm for solving variational problems.

Computational Math with Neural Networks is Hard The deep Ritz method: a deep learning-based numerical algorithm for solving variational problems

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:32.116424Z

Source-reported events for the cited work

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Observation 9c081924-db4b-4489-9d75-410ad89deb60 · outbound

This paper cites Feischl, A.

Computational Math with Neural Networks is Hard Feischl, A

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:31.849699Z

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 863b5661-cfac-47e5-b7b5-e318fdcad6fa · outbound

This paper cites Deep Neural Networks and Adaptive Quadrature for Solving Variational Problems.

Computational Math with Neural Networks is Hard Deep Neural Networks and Adaptive Quadrature for Solving Variational Problems

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:31.559458Z

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 16c6368a-bc1d-474b-872e-7e7b968c979c · outbound

This paper cites Hyperplane sections of the n-dimensional cube.

Computational Math with Neural Networks is Hard Hyperplane sections of the n-dimensional cube

Reference 16

Resolution: verified exact; doi, observed 2026-08-07T14:51:26.719376Z

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 33885e21-5e72-4a4e-8a46-ed6931ec7dde · outbound

This paper cites Training Neural Networks is NP-Hard in Fixed Dimension.

Computational Math with Neural Networks is Hard Training Neural Networks is NP-Hard in Fixed Dimension

Reference 17

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41775334-c6a2-4926-8f96-5bf98e390e67 · outbound

This paper cites Gold-standard solutions to the Schr\"odinger equation using deep learning: How much physics do we need?.

Computational Math with Neural Networks is Hard Gold-standard solutions to the Schr\"odinger equation using deep learning: How much physics do we need?

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T14:51:21.571889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 452b2b57-0fc0-4f2b-9fe1-394388d566b2 · outbound

This paper cites Gilbarg and N.

Computational Math with Neural Networks is Hard Gilbarg and N

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:31.243302Z

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 ba7b9896-4936-4718-abbb-78565bddd49e · outbound

This paper cites Analysis of preintegration followed by quasi–Monte Carlo integration for distribution functions and densities.

Computational Math with Neural Networks is Hard Analysis of preintegration followed by quasi–Monte Carlo integration for distribution functions and densities

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:30.962126Z

Source-reported events for the cited work

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Observation 3fec6d3b-ebed-4cdc-99df-d0b8aacb908e · outbound

This paper cites Tight hardness results for training depth-2 ReLU networks.

Computational Math with Neural Networks is Hard Tight hardness results for training depth-2 ReLU networks

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:30.718641Z

Source-reported events for the cited work

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Observation f9ff6de6-19d9-4c07-a6e9-c2a3c11ba366 · outbound

This paper cites Hierarchical singular value decomposition of tensors.

Computational Math with Neural Networks is Hard Hierarchical singular value decomposition of tensors

Reference 22

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac248f02-0650-42d2-8242-5a8672b44647 · outbound

This paper cites A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differen- tial equations.

Computational Math with Neural Networks is Hard A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differen- tial equations

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:30.470017Z

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 a9ca1ec1-0446-4bf7-939f-1c1c6c9d39aa · outbound

This paper cites Theory-to-Practice Gap for Neural Networks and Neural Operators.

Computational Math with Neural Networks is Hard Theory-to-Practice Gap for Neural Networks and Neural Operators

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T14:51:22.262827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa7f7882-6c23-4d35-8216-59f0427c8de2 · outbound

This paper cites Proof of the theory-to-practice gap in deep learning via sampling complexity bounds for neural network approximation spaces.

Computational Math with Neural Networks is Hard Proof of the theory-to-practice gap in deep learning via sampling complexity bounds for neural network approximation spaces

Reference 25

Resolution: verified exact; doi, observed 2026-08-07T14:51:26.110580Z

Source-reported events for the cited work

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Observation f4dce1b6-c751-419c-8b28-f4ca0bbc7e7d · outbound

This paper cites A new scheme for the tensor representation.

Computational Math with Neural Networks is Hard A new scheme for the tensor representation

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T14:51:22.429773Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9213709c-dbe7-4ef1-b1fd-031a1a6aec43 · outbound

This paper cites Solving high-dimensional eigenvalue problems using deep neural networks: A diffusion Monte Carlo like approach.

Computational Math with Neural Networks is Hard Solving high-dimensional eigenvalue problems using deep neural networks: A diffusion Monte Carlo like approach

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:30.241936Z

Source-reported events for the cited work

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Observation 6dcc9eb0-2d25-4ea8-a5e9-bca49b71e77c · outbound

This paper cites Neural and spectral operator surrogates: unified construction and expression rate bounds.

Computational Math with Neural Networks is Hard Neural and spectral operator surrogates: unified construction and expression rate bounds

Reference 28

Resolution: verified exact; doi, observed 2026-08-07T14:51:25.721230Z

Source-reported events for the cited work

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Observation b9358824-6eff-41eb-8e35-bfca58aa45c7 · outbound

This paper cites On the complexity of k-SAT.

Computational Math with Neural Networks is Hard On the complexity of k-SAT

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:30.084356Z

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 760c55bd-665b-4d57-b6d3-6ddd4699d3c5 · outbound

This paper cites Which Problems Have Strongly Exponential Complex- ity?.

Computational Math with Neural Networks is Hard Which Problems Have Strongly Exponential Complex- ity?

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:29.942126Z

Source-reported events for the cited work

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Observation 5745fecf-3a01-4627-ae1a-bcac443d7417 · outbound

This paper cites QTT approximation of elliptic solution operators in higher dimensions.

Computational Math with Neural Networks is Hard QTT approximation of elliptic solution operators in higher dimensions

Reference 31

Resolution: verified exact; doi, observed 2026-08-07T14:51:25.355110Z

Source-reported events for the cited work

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Observation 11d7ed3a-7676-4627-adb5-1042b62ca973 · outbound

This paper cites Operator Learning: Algorithms and Analysis.

Computational Math with Neural Networks is Hard Operator Learning: Algorithms and Analysis

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T14:51:22.985705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b0f50f5-89d0-4089-88ec-df60a3ddfc94 · outbound

This paper cites The Decision Problem for a Class of First-Order Formulas in Which all Disjunctions are Binary.

Computational Math with Neural Networks is Hard The Decision Problem for a Class of First-Order Formulas in Which all Disjunctions are Binary

Reference 33

Resolution: verified exact; doi, observed 2026-08-07T14:51:24.970549Z

Source-reported events for the cited work

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Observation a48c3d4f-80a9-4529-9fed-8e7b0dfdd69d · outbound

This paper cites Error estimates for DeepONets: a deep learning framework in infinite dimensions.

Computational Math with Neural Networks is Hard Error estimates for DeepONets: a deep learning framework in infinite dimensions

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T14:51:23.119649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5082aef0-c3b7-41e2-9700-b20562466c04 · outbound

This paper cites Neural Networks are Integrable.

Computational Math with Neural Networks is Hard Neural Networks are Integrable

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:51:27.681119Z

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 16c992a7-f03c-4364-97d0-caa2f2c08a32 · outbound

This paper cites Exponential ReLU neural network approximation rates for point and edge singularities.

Computational Math with Neural Networks is Hard Exponential ReLU neural network approximation rates for point and edge singularities

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:29.828033Z

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 b45db3c4-7ad2-4baa-a168-8f799e7bf872 · outbound

This paper cites Marcati and C.

Computational Math with Neural Networks is Hard Marcati and C

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T14:51:23.476015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34e3e47a-250a-4d61-9ba0-8d33959db929 · outbound

This paper cites Deep learning in high dimension: ReLU neural network expression for Bayesian PDE inversion.

Computational Math with Neural Networks is Hard Deep learning in high dimension: ReLU neural network expression for Bayesian PDE inversion

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:29.699650Z

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 9b49969a-a903-452a-a8e3-455fa77cb572 · outbound

This paper cites Approximation of 2 d × 2d matrices using tensor decomposition.

Computational Math with Neural Networks is Hard Approximation of 2 d × 2d matrices using tensor decomposition

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T14:51:23.640204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation beef8bfb-c5c3-4c79-bfec-72a8dbc9e8a7 · outbound

This paper cites Topological properties of the set of functions generated by neural networks of fixed size.

Computational Math with Neural Networks is Hard Topological properties of the set of functions generated by neural networks of fixed size

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T14:51:23.700826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1a26a04-2c60-4872-a536-4803270ce023 · outbound

This paper cites On the difference between Turing machine time and random-access machine time.

Computational Math with Neural Networks is Hard On the difference between Turing machine time and random-access machine time

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:29.587670Z

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 fdf6c7c0-e497-4dc0-a5c1-0415489b82b3 · outbound

This paper cites On quadrature rules for solving Partial Differential Equations using Neural Networks.

Computational Math with Neural Networks is Hard On quadrature rules for solving Partial Differential Equations using Neural Networks

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:29.419545Z

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 09b69af4-296f-4816-9fc0-5a8248bc495a · outbound

This paper cites A probabilistic algorithm for k-SAT and constraint satisfaction problems.

Computational Math with Neural Networks is Hard A probabilistic algorithm for k-SAT and constraint satisfaction problems

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:29.232881Z

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 ae8ef4f0-bf41-4174-8d78-ac725cde14a1 · outbound

This paper cites Deep solution operators for variational inequalities via proximal neural networks.

Computational Math with Neural Networks is Hard Deep solution operators for variational inequalities via proximal neural networks

Reference 44

Resolution: malformed identifier; no resolver link, observed 2026-08-07T14:51:23.996311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9eee41d8-9f11-4bb1-a071-8ff478b1c456 · outbound

This paper cites Deep Operator Network Approximation Rates for Lipschitz Operators.

Computational Math with Neural Networks is Hard Deep Operator Network Approximation Rates for Lipschitz Operators

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T14:51:24.049918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59b5dc6d-e1ce-4269-a2b9-ce68913105be · outbound

This paper cites Deep learning in high dimension: neural network expression rates for analytic functions in L2(Rd, γd).

Computational Math with Neural Networks is Hard Deep learning in high dimension: neural network expression rates for analytic functions in L2(Rd, γd)

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T14:51:24.153165Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06a01ec9-4026-4c92-8fe2-7aec67498b94 · outbound

This paper cites an unresolved cited work.

Computational Math with Neural Networks is Hard Unresolved cited work

Reference 47

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:51:29.081447Z

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 5b4ba884-1b98-4092-96e1-8f1d7b52371e · outbound

This paper cites Le probl` eme de Dirichlet pour les ´ equations elliptiques du second ordre ` a coefficients discontinus.

Computational Math with Neural Networks is Hard Le probl` eme de Dirichlet pour les ´ equations elliptiques du second ordre ` a coefficients discontinus

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:28.898628Z

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 26fb6330-e874-4d6e-88c5-c615d8d29e58 · outbound

This paper cites On the Complexity of Derivation in Propositional Calculus.

Computational Math with Neural Networks is Hard On the Complexity of Derivation in Propositional Calculus

Reference 49

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T14:51:28.686884Z

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 e594795c-c614-448e-a8a0-1ca4fd39b920 · outbound

This paper cites Weak adversarial networks for high-dimensional partial differ- ential equations.

Computational Math with Neural Networks is Hard Weak adversarial networks for high-dimensional partial differ- ential equations

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:28.504547Z

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 3e47c877-116b-47a4-9515-d71ead372d29 · outbound

This paper cites an unresolved cited work.

Computational Math with Neural Networks is Hard Unresolved cited work

Reference 166

Resolution: verified exact; doi, observed 2026-08-07T14:51:26.404584Z

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 20bdce06-52d2-418a-ad45-be5589899974 · outbound

This paper cites an unresolved cited work.

Computational Math with Neural Networks is Hard Unresolved cited work

Reference 375

Resolution: unresolved; no resolver link, observed 2026-08-07T14:51:22.787521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2c5de12-c005-4bb2-af13-daa62d50f989 · outbound

This paper cites an unresolved cited work.

Computational Math with Neural Networks is Hard Unresolved cited work

Reference 1127

Resolution: verified exact; doi, observed 2026-08-07T14:51:24.672116Z

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 2e08f9da-eabc-49bc-bc13-aa083abf23a0 · outbound

This paper cites Towards optimal hierarchical training of neural networks.

Computational Math with Neural Networks is Hard Towards optimal hierarchical training of neural networks

Reference 2024

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T14:51:28.123104Z

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.

Pith citing papers

Observation aaf84f3c-6cc6-4dc2-8706-dcf73216b0a2 · inbound

Adaptive anisotropic composite quadratures for residual minimisation in neural PDE approximations cites this paper.

Adaptive anisotropic composite quadratures for residual minimisation in neural PDE approximations Computational Math with Neural Networks is Hard

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:36:08.149796Z

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.