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Paper Citation Record · LEDGER

AI-assisted design of experiments at the frontiers of computation: methods and new perspectives

As of 14 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2501.04448.

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

pith.paper-citation-record.v1
2501.04448 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:35:45.671221Z

measured 24 of 24 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

24 of 24 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5fd3d426-e6a7-4dee-b737-cd2bb7218698 · outbound

This paper cites 2020 Update of the European Strategy for Particle Physics.

AI-assisted design of experiments at the frontiers of computation: methods and new perspectives 2020 Update of the European Strategy for Particle Physics

Reference 1

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Observation ff5fee58-1e8f-4f57-a4b4-06aea835af3f · outbound

This paper cites Reporting results in High Energy Physics publications: A manifesto.

AI-assisted design of experiments at the frontiers of computation: methods and new perspectives Reporting results in High Energy Physics publications: A manifesto

Reference 2

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Observation 385cab7f-ee02-4234-a8a4-f479ed0d2ade · outbound

This paper cites Toward the end-to-end optimization of particle physics instruments with differentiable programming.

AI-assisted design of experiments at the frontiers of computation: methods and new perspectives Toward the end-to-end optimization of particle physics instruments with differentiable programming

Reference 3

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Observation 5e47585f-2e3e-4eda-b826-75523ff7b6bf · outbound

This paper cites The Nature of Statistical Learning Theory.

AI-assisted design of experiments at the frontiers of computation: methods and new perspectives The Nature of Statistical Learning Theory

Reference 4

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Observation 28bd816e-7502-4848-85ed-3674914cc837 · outbound

This paper cites Mathematics of Deep Learning.

AI-assisted design of experiments at the frontiers of computation: methods and new perspectives Mathematics of Deep Learning

Reference 5

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Observation bb38aea7-4bfb-486f-8d00-8892d79040d1 · outbound

This paper cites Evaluating Derivatives.

AI-assisted design of experiments at the frontiers of computation: methods and new perspectives Evaluating Derivatives

Reference 6

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Observation fea21bf6-d50b-4dc8-933c-5b22052f31ae · outbound

This paper cites A Logical Calculus of Ideas Immanent in Nervous Activity.

AI-assisted design of experiments at the frontiers of computation: methods and new perspectives A Logical Calculus of Ideas Immanent in Nervous Activity

Reference 7

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Observation a71cd77b-9fef-4e7d-9220-2ef32419061a · outbound

This paper cites DeepLearningestmort.

AI-assisted design of experiments at the frontiers of computation: methods and new perspectives DeepLearningestmort

Reference 8

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Source-reported events for the cited work

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Observation ac5e0a85-2b3a-40d2-ab8f-49b0a1ef07b6 · outbound

This paper cites Making the world differentiable: on using self supervised fully recurrent neural networks for dynamic reinforcement learning and planning in non-stationary environ- ments.

AI-assisted design of experiments at the frontiers of computation: methods and new perspectives Making the world differentiable: on using self supervised fully recurrent neural networks for dynamic reinforcement learning and planning in non-stationary environ- ments

Reference 9

Resolution
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Observation db285335-f926-4f6d-b7ab-95a5f72f1877 · outbound

This paper cites Amethodforapproximatingoptimalstatisticalsignificanceswithmachine- learned likelihoods.

AI-assisted design of experiments at the frontiers of computation: methods and new perspectives Amethodforapproximatingoptimalstatisticalsignificanceswithmachine- learned likelihoods

Reference 10

Resolution
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Observation 15516bae-ca97-4911-8d74-f07cbc451212 · outbound

This paper cites MeasurementofCPasymmetriesandbranchingfractionsincharm- less two-body B-meson decays to pions and kaons.

AI-assisted design of experiments at the frontiers of computation: methods and new perspectives MeasurementofCPasymmetriesandbranchingfractionsincharm- less two-body B-meson decays to pions and kaons

Reference 11

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Observation 2c7881ae-ca20-41b6-96b0-ed43bdca41bd · outbound

This paper cites Search for 𝑡𝑡𝐻 production in the 𝐻 → 𝑏𝑏 decay chan- nel with √𝑠 = 13 TeV pp collisions at the CMS experiment.

AI-assisted design of experiments at the frontiers of computation: methods and new perspectives Search for 𝑡𝑡𝐻 production in the 𝐻 → 𝑏𝑏 decay chan- nel with √𝑠 = 13 TeV pp collisions at the CMS experiment

Reference 12

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Source-reported events for the cited work

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Observation 6cf8920c-2bb8-4dde-b516-5a28df323f8a · outbound

This paper cites Machine Learning and Differentiable Programming for Statistics at the LHC.

AI-assisted design of experiments at the frontiers of computation: methods and new perspectives Machine Learning and Differentiable Programming for Statistics at the LHC

Reference 13

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Observation 3389c7be-ea5c-4dd0-bb82-be888550b8a3 · outbound

This paper cites UnveilingtheStructureofWideFlatMinimainNeuralNetworks.

AI-assisted design of experiments at the frontiers of computation: methods and new perspectives UnveilingtheStructureofWideFlatMinimainNeuralNetworks

Reference 14

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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.

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Observation 72aeddf3-86fd-42ed-beeb-17e0d9837533 · outbound

This paper cites TomOpt: differential optimisation for task- and constraint-aware design of particle detectors in the context of muon tomography.

AI-assisted design of experiments at the frontiers of computation: methods and new perspectives TomOpt: differential optimisation for task- and constraint-aware design of particle detectors in the context of muon tomography

Reference 15

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Source-reported events for the cited work

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Observation e2c17b19-f2f8-4ea5-a5ee-30b2c870725d · outbound

This paper cites Automatic Optimization of a Parallel-Plate Avalanche Counter with Optical Readout.

AI-assisted design of experiments at the frontiers of computation: methods and new perspectives Automatic Optimization of a Parallel-Plate Avalanche Counter with Optical Readout

Reference 16

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Source-reported events for the cited work

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Observation 6b3bb463-7ae7-4061-9ed7-d45fd2d1e281 · outbound

This paper cites End-To-End Optimization of the Layout of a Gamma Ray Observatory.

AI-assisted design of experiments at the frontiers of computation: methods and new perspectives End-To-End Optimization of the Layout of a Gamma Ray Observatory

Reference 17

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Observation ff49144a-19ae-4e02-978a-868b261af53e · outbound

This paper cites Modelling the neurons of the electrosensory lobe in Gym- notus omarorum with differentiable programming.

AI-assisted design of experiments at the frontiers of computation: methods and new perspectives Modelling the neurons of the electrosensory lobe in Gym- notus omarorum with differentiable programming

Reference 18

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Observation a379f641-407d-4e7e-acd7-5c27e678fa18 · outbound

This paper cites Nat Commun 15, 3392 (2024).

AI-assisted design of experiments at the frontiers of computation: methods and new perspectives Nat Commun 15, 3392 (2024)

Reference 19

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Observation cc921866-fef6-4fbc-a176-e8d4c00496d2 · outbound

This paper cites Neuromorphic Readout For Homogeneous Hadron Calorimeters.

AI-assisted design of experiments at the frontiers of computation: methods and new perspectives Neuromorphic Readout For Homogeneous Hadron Calorimeters

Reference 20

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Observation 1a38e1b3-ec80-4ca5-b9e4-e74bfb4a1720 · outbound

This paper cites Enhanced low-energy supernova burst detection in large liquid argon time projection chambers enabled by Q-Pix.

AI-assisted design of experiments at the frontiers of computation: methods and new perspectives Enhanced low-energy supernova burst detection in large liquid argon time projection chambers enabled by Q-Pix

Reference 21

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Observation 6637e28d-be92-45ff-aa1d-2b215a13c9d9 · outbound

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AI-assisted design of experiments at the frontiers of computation: methods and new perspectives MachineLearningwithQuantumComputers

Reference 22

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Observation f08a542e-eaea-49a5-beac-dbb7c0df2713 · outbound

This paper cites Event Classification with Quantum Machine Learning in High-Energy Physics.

AI-assisted design of experiments at the frontiers of computation: methods and new perspectives Event Classification with Quantum Machine Learning in High-Energy Physics

Reference 23

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Observation 23b9997d-1eed-4852-b426-abe14e9350cf · outbound

This paper cites Sentiment Analysis with QuantumNaturalLanguageProcessing.

AI-assisted design of experiments at the frontiers of computation: methods and new perspectives Sentiment Analysis with QuantumNaturalLanguageProcessing

Reference 24

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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.

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Pith citing papers

No inbound Pith citation observations are available.