Paper Citation Record · LEDGER

Physics Instrument Design with Reinforcement Learning

As of 14 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2412.10237.

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

pith.paper-citation-record.v1
2412.10237 v1

Coverage vector

measured 34 of 34 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-11T16:16:18.694149Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

34 of 34 outbound references displayed

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unresolved26
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External citation measurements

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Outbound references

Observation 3f92361d-ab2c-4912-804d-a1255e7e38d7 · outbound

This paper cites Efficient Forward-Mode Algorithmic Derivatives of Geant4.

Physics Instrument Design with Reinforcement Learning Efficient Forward-Mode Algorithmic Derivatives of Geant4

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T16:16:18.426904Z

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Observation a8f48bf1-bc55-4f8f-a4c6-c9b89c7cc5af · outbound

This paper cites Optimising the active muon shield for the ship experiment at cern.

Physics Instrument Design with Reinforcement Learning Optimising the active muon shield for the ship experiment at cern

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T16:16:18.437701Z

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Observation 98556694-7dcd-4a72-b8af-d7b05ccff72d · outbound

This paper cites Caloclouds: fast geometry-independent highly-granular calorimeter simulation.

Physics Instrument Design with Reinforcement Learning Caloclouds: fast geometry-independent highly-granular calorimeter simulation

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T16:16:18.443565Z

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Observation 93b3be9a-3bcb-4fd6-9be8-ec5711fe2e1f · outbound

This paper cites Caloclouds ii: ultra-fast geometry-independent highly-granular calorimeter simulation.

Physics Instrument Design with Reinforcement Learning Caloclouds ii: ultra-fast geometry-independent highly-granular calorimeter simulation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T16:16:18.454524Z

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Observation 1e7ccf69-31be-4dc1-8e3e-402ad24d9238 · outbound

This paper cites Gauger, Christian Glaser, Atılım G.

Physics Instrument Design with Reinforcement Learning Gauger, Christian Glaser, Atılım G

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T16:16:18.460294Z

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Observation 99282447-4f2a-4dc3-8a7f-2d72f1bd2662 · outbound

This paper cites Electron-ion collider: The next QCD frontier: Understanding the glue that binds us all.

Physics Instrument Design with Reinforcement Learning Electron-ion collider: The next QCD frontier: Understanding the glue that binds us all

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T16:16:18.468107Z

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Observation 3ee48a48-54e6-4f06-b380-02f82f5fd0e7 · outbound

This paper cites Fabjan and Fabiola Gianotti.

Physics Instrument Design with Reinforcement Learning Fabjan and Fabiola Gianotti

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T16:16:18.476523Z

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Observation 618e39da-e855-4301-8292-750f799f961a · outbound

This paper cites Catastrophic forgetting in connectionist networks.

Physics Instrument Design with Reinforcement Learning Catastrophic forgetting in connectionist networks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T16:16:18.482264Z

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Observation 1deff352-094e-433b-984c-cd5dd73f004b · outbound

This paper cites Geant4—a simulation toolkit.

Physics Instrument Design with Reinforcement Learning Geant4—a simulation toolkit

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T16:16:18.494603Z

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Observation e45c2ef8-5a0b-44b5-93de-ade476adaa5f · outbound

This paper cites Generative adversarial nets.

Physics Instrument Design with Reinforcement Learning Generative adversarial nets

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T16:16:18.500455Z

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Observation 978d1710-fa96-4e98-974a-36c7aaaf5e0b · outbound

This paper cites an unresolved cited work.

Physics Instrument Design with Reinforcement Learning Unresolved cited work

Reference 11

Resolution: verified exact; doi, observed 2026-08-11T16:16:18.986054Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ca21bf4c-8c6c-4801-a259-66f3c3823fa8 · outbound

This paper cites an unresolved cited work.

Physics Instrument Design with Reinforcement Learning Unresolved cited work

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T16:16:18.525694Z

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Observation d9bacf74-f1cb-41c5-8c23-67692debc79c · outbound

This paper cites Denoising diffusion probabilistic models.

Physics Instrument Design with Reinforcement Learning Denoising diffusion probabilistic models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T16:16:18.532096Z

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Observation 4af46050-365f-452d-9bf9-bd79e0b7ba31 · outbound

This paper cites Distributed Prioritized Experience Replay.

Physics Instrument Design with Reinforcement Learning Distributed Prioritized Experience Replay

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T16:16:18.540606Z

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Observation d57ea3ac-619c-4d60-9032-b9ff68136e66 · outbound

This paper cites Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph, and image data.

Physics Instrument Design with Reinforcement Learning Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph, and image data

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T16:16:18.550563Z

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Observation 8f92973e-1ada-4214-b29f-e6053b8188a9 · outbound

This paper cites Kingma and Max Welling.

Physics Instrument Design with Reinforcement Learning Kingma and Max Welling

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T16:16:18.558441Z

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Observation 2820f34e-7833-4d39-bde2-74f1f1ca79f0 · outbound

This paper cites Le, James Laudon, Richard Ho, Roger Carpenter, and Jeff Dean.

Physics Instrument Design with Reinforcement Learning Le, James Laudon, Richard Ho, Roger Carpenter, and Jeff Dean

Reference 17

Resolution: verified exact; doi, observed 2026-08-11T16:16:18.942517Z

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Observation 228288b8-c380-401f-9233-f08ee07f5ed3 · outbound

This paper cites Machine-learning optimized design of experiments (mode), 2024.

Physics Instrument Design with Reinforcement Learning Machine-learning optimized design of experiments (mode), 2024

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:16:19.532525Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1ab3e05c-7b2a-4fcc-87a9-88b620a4678f · outbound

This paper cites The beam and detector of the na62 experiment at cern.

Physics Instrument Design with Reinforcement Learning The beam and detector of the na62 experiment at cern

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T16:16:18.578763Z

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Observation 51a90018-3665-4597-a1bd-292f9400f85b · outbound

This paper cites 2022 NA62 Status Report to the CERN SPSC.

Physics Instrument Design with Reinforcement Learning 2022 NA62 Status Report to the CERN SPSC

Reference 20

Resolution: verified exact; raw_fallback, observed 2026-08-11T16:16:19.276507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a007981c-538f-4a9c-a532-ee18baded4ce · outbound

This paper cites Calogan: Simulating 3d high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks.

Physics Instrument Design with Reinforcement Learning Calogan: Simulating 3d high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T16:16:18.591161Z

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Observation 35ebfd01-7c68-4b87-b503-281b3e5adabb · outbound

This paper cites Learning representations of irregular particle-detector geometry with distance-weighted graph networks.

Physics Instrument Design with Reinforcement Learning Learning representations of irregular particle-detector geometry with distance-weighted graph networks

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T16:16:18.597501Z

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Observation d9b99be9-154b-4a58-bc22-dc1c4983ae42 · outbound

This paper cites End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks.

Physics Instrument Design with Reinforcement Learning End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks

Reference 23

Resolution: verified exact; doi, observed 2026-08-11T16:16:18.872966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 58d3e3c7-a9c0-4c82-801e-00de8b8ea085 · outbound

This paper cites End-to-End Multi-Track Reconstruction using Graph Neural Networks at Belle II.

Physics Instrument Design with Reinforcement Learning End-to-End Multi-Track Reconstruction using Graph Neural Networks at Belle II

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T16:16:18.609362Z

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Observation 30b00e85-75f7-4821-83a1-dfabcfeea614 · outbound

This paper cites Variational inference with normalizing flows.

Physics Instrument Design with Reinforcement Learning Variational inference with normalizing flows

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T16:16:18.615268Z

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Observation c2dd82d9-ce0c-4481-84b2-ee43db104701 · outbound

This paper cites Overview of the lhcb experiment.

Physics Instrument Design with Reinforcement Learning Overview of the lhcb experiment

Reference 26

Resolution: verified exact; doi, observed 2026-08-11T16:16:18.853413Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 10ec7fcc-7b94-499c-8e8b-6b0cfac26877 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Physics Instrument Design with Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T16:16:18.632840Z

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Observation 600d4596-3f21-4123-8010-6bd1c41a2371 · outbound

This paper cites Black-box optimization with local generative surrogates.

Physics Instrument Design with Reinforcement Learning Black-box optimization with local generative surrogates

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:16:19.489290Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0c7d4d52-ded7-46a8-b3f2-e85b37bb62ca · outbound

This paper cites an unresolved cited work.

Physics Instrument Design with Reinforcement Learning Unresolved cited work

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T16:16:18.645762Z

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Observation af90bff4-0ca4-4837-808f-a10659cb1549 · outbound

This paper cites A general reinforcement learning algorithm that masters chess, shogi, and go through self-play.

Physics Instrument Design with Reinforcement Learning A general reinforcement learning algorithm that masters chess, shogi, and go through self-play

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T16:16:18.655860Z

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Observation 73721f66-b981-41f2-9ac0-678f13c38d27 · outbound

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

Physics Instrument Design with Reinforcement Learning Tomopt: differential optimisation for task- and constraint-aware design of particle detectors in the context of muon tomography

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T16:16:18.662620Z

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Observation 883d2d25-e02d-4101-8962-659f4194b929 · outbound

This paper cites Reinforcement learning: An introduction.

Physics Instrument Design with Reinforcement Learning Reinforcement learning: An introduction

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T16:16:18.668450Z

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Observation b4418d9f-b3bd-4561-a0c3-108283cdb995 · outbound

This paper cites Particle-based fast jet simulation at the lhc with variational autoencoders.

Physics Instrument Design with Reinforcement Learning Particle-based fast jet simulation at the lhc with variational autoencoders

Reference 33

Resolution: verified exact; doi, observed 2026-08-11T16:16:18.779120Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7b5355ba-a318-4540-b298-6466056c4541 · outbound

This paper cites Zare, and Patrick Riley.

Physics Instrument Design with Reinforcement Learning Zare, and Patrick Riley

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T16:16:18.694149Z

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Pith citing papers

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