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Paper Citation Record · LEDGER

LineFlow: A Framework to Learn Active Control of Production Lines

As of 20 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 0 inbound Pith citation observations for arXiv:2505.06744.

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

pith.paper-citation-record.v1
2505.06744 v1

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:39:59.399768Z

measured 57 of 57 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

57 of 57 outbound references displayed

  • verified exact13
  • verified fuzzy16
  • unresolved26
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 50ba5009-d3a9-4f63-a6c0-8acbf8489128 · outbound

This paper cites write newline.

LineFlow: A Framework to Learn Active Control of Production Lines write newline

Reference 1

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no resolver link, observed 2026-08-15T22:39:59.149489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:39:59.149489Z digest=sha256:5e79021f317086617ec70dff93be4410f7b8083df3d4201eced098fca5604ef3

Observation bceb6105-303c-4ff8-8a20-7f8c9fa1ce1b · outbound

This paper cites an unresolved cited work.

LineFlow: A Framework to Learn Active Control of Production Lines Unresolved cited work

Reference 2

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no resolver link, observed 2026-08-15T22:39:59.155997Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-15T22:39:59.155997Z digest=sha256:86a543f29f521acf3c31751702cf4daab850badc31cbe161c7be98295569a041

Observation 5854cff2-8792-452e-be4c-2ae3c76dd649 · outbound

This paper cites Gekko optimization suite.

LineFlow: A Framework to Learn Active Control of Production Lines Gekko optimization suite

Reference 3

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:39:59.161399Z digest=sha256:be7cb3317e2199a3fde9645b3baef01e869d699c585a08ecad7a2122fe393acb

Observation c250bd86-b78e-47ea-8429-bf2235df2c39 · outbound

This paper cites Performance analysis of production lines: Discrete and continuous flow models.

LineFlow: A Framework to Learn Active Control of Production Lines Performance analysis of production lines: Discrete and continuous flow models

Reference 4

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.166055Z digest=sha256:cf990aebcef932d65d15a2883f42c836b07a2a9bbfecd0cddcca1bcc20850397

Observation 04688864-bc5d-41b3-8224-d2b051d7c093 · outbound

This paper cites Modeling and control of dispensing processes for surface mount technology.

LineFlow: A Framework to Learn Active Control of Production Lines Modeling and control of dispensing processes for surface mount technology

Reference 5

Resolution
metadata mismatch
raw_fallback, observed 2026-08-15T22:40:00.501864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.170232Z digest=sha256:48932657c5279196db8bfc45a331874d26d9aaaa3a0c4cdb4e0f057837fe147e

Observation 59f2bcb1-4d23-43e6-8e68-a487073ac039 · outbound

This paper cites Y., Malyutin, S., and Soukhal, A.

LineFlow: A Framework to Learn Active Control of Production Lines Y., Malyutin, S., and Soukhal, A

Reference 6

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verified exact
doi, observed 2026-08-15T22:39:59.644752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.174329Z digest=sha256:eafe450f531e8066f96333ffe6910b849074276a513043535c1cf33d747e3b2f

Observation cb3945bb-0e1f-41a5-887a-e3ea9be0d1e1 · outbound

This paper cites Deep reinforcement learning for optimal planning of assembly line maintenance.

LineFlow: A Framework to Learn Active Control of Production Lines Deep reinforcement learning for optimal planning of assembly line maintenance

Reference 7

Resolution
verified exact
doi, observed 2026-08-15T22:39:59.631147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.178578Z digest=sha256:3fcaa46f34d04adefbd1419d7c0fb11b4d12f50c5c1014a3912ed82fe26fab4b

Observation 79b9d5cd-7505-467e-9ce1-cc505d04c579 · outbound

This paper cites N., Cortez, P., Carvalho, M.

LineFlow: A Framework to Learn Active Control of Production Lines N., Cortez, P., Carvalho, M

Reference 8

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no resolver link, observed 2026-08-15T22:39:59.183777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:39:59.183777Z digest=sha256:c80da168246b79769efa0b66cb788d52d48d98a657b5d665f847b172d8a4c4ee

Observation ac3160a9-f917-4f02-9578-e0acfed2fea3 · outbound

This paper cites A reinforcement learning decision model for online process parameters optimization from offline data in injection molding.

LineFlow: A Framework to Learn Active Control of Production Lines A reinforcement learning decision model for online process parameters optimization from offline data in injection molding

Reference 9

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no resolver link, observed 2026-08-15T22:39:59.188789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:39:59.188789Z digest=sha256:929da8702434e32a4adea4a5820b9e5d85a9b6c56e6586cd384c7d06edaa3b1a

Observation ae6f6408-e295-405f-9da1-f9dee8e1e1ef · outbound

This paper cites R., Millman, K.

LineFlow: A Framework to Learn Active Control of Production Lines R., Millman, K

Reference 10

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unresolved
no resolver link, observed 2026-08-15T22:39:59.193364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:39:59.193364Z digest=sha256:cac2871ece61c7cb0aaa1e304a046da5526bb260b24575c61c7377f1db4b01e9

Observation 5ee6a24a-a1b6-44e6-b977-d441ac58241a · outbound

This paper cites Memory-based control with recurrent neural networks.

LineFlow: A Framework to Learn Active Control of Production Lines Memory-based control with recurrent neural networks

Reference 11

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no resolver link, observed 2026-08-15T22:39:59.198374Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:39:59.198374Z digest=sha256:f2fe1fbfce6c78b1130761c5fb24f404d1188764a488a5a60451b12d8dd4d539

Observation 91748e3b-809d-4667-a924-202ab64a0dfb · outbound

This paper cites Y., and Jiang, J.

LineFlow: A Framework to Learn Active Control of Production Lines Y., and Jiang, J

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:40:00.834574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.203111Z digest=sha256:dbefb54a4074d129508ae76e4d54a9e01efde4f1863bee0727f332481ffdebcb

Observation 9e3cc7cd-3f3d-4b32-b087-1f8279fc5206 · outbound

This paper cites An intelligent weld control strategy based on reinforcement learning approach.

LineFlow: A Framework to Learn Active Control of Production Lines An intelligent weld control strategy based on reinforcement learning approach

Reference 13

Resolution
verified exact
doi, observed 2026-08-15T22:39:59.608458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.207287Z digest=sha256:c2ea4fc3f483822f5b590cfb1eb0bbc60588a917d3e7bffcf5aaa553e97ffe55

Observation 329c674a-f5af-4c87-9dd3-66a4a6bec620 · outbound

This paper cites M., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Ž \'i dek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S.

LineFlow: A Framework to Learn Active Control of Production Lines M., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Ž \'i dek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S

Reference 14

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verified fuzzy
raw_fallback, observed 2026-08-15T22:40:00.818605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.212526Z digest=sha256:a3bc0585ba02137f61e3ebe791264d7cb2bacedf3532b3cb8b4aaebb460121b8

Observation 5a027b3f-1dda-417b-b9c9-32063c4ad054 · outbound

This paper cites Machine learning applications in production lines: A systematic literature review.

LineFlow: A Framework to Learn Active Control of Production Lines Machine learning applications in production lines: A systematic literature review

Reference 15

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:39:59.217461Z digest=sha256:4186f427ba4f98c64c8200c05cade6c8cc52bf205fdb66cf35cd30ee54b825ea

Observation 69a5083d-f6bc-43b8-9982-df765c4ab58f · outbound

This paper cites an unresolved cited work.

LineFlow: A Framework to Learn Active Control of Production Lines Unresolved cited work

Reference 16

Resolution
verified exact
doi, observed 2026-08-15T22:39:59.594845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.221688Z digest=sha256:c375b31152bd99331985023c8acc3ec05b1bd1b83d7524b848bc6bd108bd5399

Observation bba75d2f-0b77-4217-862e-042664a6f2f6 · outbound

This paper cites Designing an adaptive production control system using reinforcement learning.

LineFlow: A Framework to Learn Active Control of Production Lines Designing an adaptive production control system using reinforcement learning

Reference 17

Resolution
verified exact
doi, observed 2026-08-15T22:39:59.581023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.226817Z digest=sha256:9f1b69e663cfb9e21d670439be62e10508404e6b92d974c7c844d175243d3296

Observation aa38bf9a-b8ca-44ce-85e2-7d128d153008 · outbound

This paper cites Data-driven dynamic bottleneck detection in complex manufacturing systems.

LineFlow: A Framework to Learn Active Control of Production Lines Data-driven dynamic bottleneck detection in complex manufacturing systems

Reference 18

Resolution
verified exact
doi, observed 2026-08-15T22:39:59.565976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.231051Z digest=sha256:f3416eebae430b8735df1d384c07475e00e4d704490b49301a702b6dad8d11ea

Observation daaf5337-de38-4551-93d5-33f9efd66be6 · outbound

This paper cites Real time production improvement through bottleneck control.

LineFlow: A Framework to Learn Active Control of Production Lines Real time production improvement through bottleneck control

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:40:00.804620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.235265Z digest=sha256:5a021d71b0d9d64934fc8e7f72bfdf3d0be2f4b963fe93d0f1f5e2e50ecd3be0

Observation 300996e9-3e49-4352-b109-8cbd7c65367b · outbound

This paper cites T., Tan, B.

LineFlow: A Framework to Learn Active Control of Production Lines T., Tan, B

Reference 20

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verified exact
doi, observed 2026-08-15T22:39:59.551601Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.239601Z digest=sha256:27b48cc76a2c0fd1a83b2675979fb8eea4e360a90ad8e6a1e868b4a8f5161fdc

Observation 59dea9fb-2bca-4f03-86e7-71e5d9d69f75 · outbound

This paper cites C., Schäfer, L., Matta, A., and Lanza, G.

LineFlow: A Framework to Learn Active Control of Production Lines C., Schäfer, L., Matta, A., and Lanza, G

Reference 21

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verified exact
doi, observed 2026-08-15T22:39:59.535798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.243966Z digest=sha256:de77adeb8fca3ce4670ad83302a751b766030c49c8c462bb835fb24b4bf3879a

Observation 72d9093c-e4b7-4c5f-95c9-04522e82729d · outbound

This paper cites C., Matta, A., and Lanza, G.

LineFlow: A Framework to Learn Active Control of Production Lines C., Matta, A., and Lanza, G

Reference 22

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verified exact
doi, observed 2026-08-15T22:39:59.522117Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.249264Z digest=sha256:b86ab064b676ce28d3215f6a4bc498ac4966febcb07fb332bf50529eff7fdea2

Observation b7f173c9-4107-4da8-8310-870230cfd795 · outbound

This paper cites The impact of industry 4.0 on bottleneck analysis in production and manufacturing: Current trends and future perspectives.

LineFlow: A Framework to Learn Active Control of Production Lines The impact of industry 4.0 on bottleneck analysis in production and manufacturing: Current trends and future perspectives

Reference 23

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:39:59.254371Z digest=sha256:24a2ce499ae019f6ac2376169d2e5142a678bd9da7ee9c623e5a701492094f9e

Observation ea579b42-9c2f-406d-ab4c-601848b4dbe5 · outbound

This paper cites an unresolved cited work.

LineFlow: A Framework to Learn Active Control of Production Lines Unresolved cited work

Reference 24

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:39:59.258716Z digest=sha256:9df59a8694d89f4e0d03a07c22dc820db3db7fcfb2f4933adfa314338d2b05cb

Observation 2279ebbc-cedc-48be-8d18-1fb1ce770067 · outbound

This paper cites P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., and Kavukcuoglu, K.

LineFlow: A Framework to Learn Active Control of Production Lines P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., and Kavukcuoglu, K

Reference 25

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no resolver link, observed 2026-08-15T22:39:59.262953Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:39:59.262953Z digest=sha256:5646b74f0c975ba5502a1a1876ff8471e3c606c41fe079318862071367eb7757

Observation d7f92f87-9c04-49f9-b92f-ab309c6832e3 · outbound

This paper cites Introduction to TPM: Total Productive Maintenance.

LineFlow: A Framework to Learn Active Control of Production Lines Introduction to TPM: Total Productive Maintenance

Reference 26

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raw_fallback, observed 2026-08-15T22:40:00.781504Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.267293Z digest=sha256:7a80424aa9ffdbff8993f9a1ad33a08fea22a0dda5e2dcbb5436546803c96258

Observation d958b0c1-3d29-4150-88a0-9b23f6777752 · outbound

This paper cites E., and Stone, P.

LineFlow: A Framework to Learn Active Control of Production Lines E., and Stone, P

Reference 27

Resolution
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raw_fallback, observed 2026-08-15T22:40:00.766019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.271609Z digest=sha256:5b6ceba34cc11e7d8e6a2231455336277d6706089173842806a9e644e212e3de

Observation 051b0705-cb04-461f-8501-fd4012f46935 · outbound

This paper cites A review on reinforcement learning: Introduction and applications in industrial process control.

LineFlow: A Framework to Learn Active Control of Production Lines A review on reinforcement learning: Introduction and applications in industrial process control

Reference 28

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:39:59.276001Z digest=sha256:2f52583f5f012e66a291d155df647045d7ed63f33e4f76f339a23a12a074c671

Observation 312fb8cc-46cb-4f8c-8f4a-b9193c54f029 · outbound

This paper cites C., Kuhnle, A., and Lanza, G.

LineFlow: A Framework to Learn Active Control of Production Lines C., Kuhnle, A., and Lanza, G

Reference 29

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verified exact
doi, observed 2026-08-15T22:39:59.506981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.280319Z digest=sha256:01199acb379491cd67c1889077bd9b59f998145184087ae64cef3219fc8b9cde

Observation f8f0378b-3e3c-4e92-98b3-ad5c633382d6 · outbound

This paper cites Irizarry, M., Resto, P., and Mej \' a, H.

LineFlow: A Framework to Learn Active Control of Production Lines Irizarry, M., Resto, P., and Mej \' a, H

Reference 30

Resolution
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raw_fallback, observed 2026-08-15T22:40:00.752314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.284548Z digest=sha256:9011bfe6483bd2e18572cc78cb9c2461afe10643f4496c76a8ead98bc7754ffb

Observation 23f34723-89e1-4ea1-be3d-89a487f61372 · outbound

This paper cites Generalization, Mayhems and Limits in Recurrent Proximal Policy Optimization.

LineFlow: A Framework to Learn Active Control of Production Lines Generalization, Mayhems and Limits in Recurrent Proximal Policy Optimization

Reference 31

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no resolver link, observed 2026-08-15T22:39:59.288557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:39:59.288557Z digest=sha256:065dac84230dfa541beae0cd5ee6430f47e8c831d2b4c935feac723b21c2bef5

Observation 1389f7e9-d515-432d-a38f-dc1cb26750cb · outbound

This paper cites Memory gym: Partially observable challenges to memory-based agents.

LineFlow: A Framework to Learn Active Control of Production Lines Memory gym: Partially observable challenges to memory-based agents

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:40:00.738422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.293050Z digest=sha256:bd7143df742706e053d203a132024dd4691c809f4e961ededf968790b7c2af81

Observation 6c73cd76-db09-4376-be44-1659b0f91926 · outbound

This paper cites M., Ou, W., Yenradee, P., and Huynh, V.-N.

LineFlow: A Framework to Learn Active Control of Production Lines M., Ou, W., Yenradee, P., and Huynh, V.-N

Reference 33

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no resolver link, observed 2026-08-15T22:39:59.297366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:39:59.297366Z digest=sha256:ee981f38eccc0f7a02947cb9b7c6fb7c306393be9089b45cc6f77c1a9d1ea3f9

Observation 8b3f6244-2135-4fd1-8589-915e857cb817 · outbound

This paper cites Stable-baselines3: Reliable reinforcement learning implementations.

LineFlow: A Framework to Learn Active Control of Production Lines Stable-baselines3: Reliable reinforcement learning implementations

Reference 34

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:39:59.301361Z digest=sha256:16a890a601a756d68ff7085c2fb5b35e3aba5918c95b21749b5255f65b6260a1

Observation 1631eb60-7f27-44cf-bb30-500527e06749 · outbound

This paper cites Bosch production line performance.

LineFlow: A Framework to Learn Active Control of Production Lines Bosch production line performance

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:40:00.715879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.305500Z digest=sha256:f7df92a249231fbc2e0103db44ab6da8c5108a97d054c5f53dfde19dddb43290

Observation ed99d3bf-44aa-4a9c-a17e-b97e445630f0 · outbound

This paper cites Shifting bottleneck detection.

LineFlow: A Framework to Learn Active Control of Production Lines Shifting bottleneck detection

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:40:00.701945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.309838Z digest=sha256:9ccbeb75447044bc6077157bb5b3ab92b99896d6db4de304ff21e4361ca7e619

Observation 437a01d4-8585-4b1b-a4d2-8ca26abe9551 · outbound

This paper cites Reliable shop floor bottleneck detection for flow lines through process and inventory observations.

LineFlow: A Framework to Learn Active Control of Production Lines Reliable shop floor bottleneck detection for flow lines through process and inventory observations

Reference 37

Resolution
verified exact
doi, observed 2026-08-15T22:39:59.492576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.313880Z digest=sha256:998a5d7298eacc988813a6b3fec725d9870597da5befbd47d6bb1eba8cac7aae

Observation eb3bc357-f753-4beb-a063-dc5247f4570a · outbound

This paper cites Bottleneck prediction using the active period method in combination with buffer inventories.

LineFlow: A Framework to Learn Active Control of Production Lines Bottleneck prediction using the active period method in combination with buffer inventories

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:40:00.687674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.318002Z digest=sha256:190e449d5c7604024e19d313e65923dc83304df8295082467ccb5af98dd6eeae

Observation c6f55455-8b5b-48a6-a847-408b9ee0b99c · outbound

This paper cites and Becker, C.

LineFlow: A Framework to Learn Active Control of Production Lines and Becker, C

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:40:00.673362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.322130Z digest=sha256:370bf74e815060885329b7914f095400cc8b44055883ae579525fb87ed6a6a80

Observation 8d9db04b-102d-4cd7-854f-4d05791bb481 · outbound

This paper cites Trust region policy optimization.

LineFlow: A Framework to Learn Active Control of Production Lines Trust region policy optimization

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-15T22:39:59.326009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:39:59.326009Z digest=sha256:41349febefb625c404f58a260e6671d51b82cf1e53257ae357de0c05132dac49

Observation d8f81b86-b7a8-498e-a9d7-e9c45848db2f · outbound

This paper cites Proximal Policy Optimization Algorithms.

LineFlow: A Framework to Learn Active Control of Production Lines Proximal Policy Optimization Algorithms

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-15T22:39:59.330079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:39:59.330079Z digest=sha256:a72d0fa43609fd6f9e410b939321973a65a89d622df70e5c4f03d36807e3477d

Observation 82cad94f-99ff-4e19-9311-6ea8d3b37511 · outbound

This paper cites skrl: Modular and flexible library for reinforcement learning.

LineFlow: A Framework to Learn Active Control of Production Lines skrl: Modular and flexible library for reinforcement learning

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:40:00.650384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.334146Z digest=sha256:37fabf7347e3fdccaae30173795fdf0bab182b4996e4c0eceb950cc61e079f6e

Observation 6d1654b2-7410-43f1-ae8c-17d6145dadcd · outbound

This paper cites Intelligent scheduling of discrete automated production line via deep reinforcement learning.

LineFlow: A Framework to Learn Active Control of Production Lines Intelligent scheduling of discrete automated production line via deep reinforcement learning

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-15T22:39:59.338352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:39:59.338352Z digest=sha256:63854057316859e23d3f3d97af6d1364d6f36c364e52d2e97dee1cf4b4cf81ca

Observation 7b369288-dd34-4a34-be5e-1f44e517ee57 · outbound

This paper cites Real-time scheduling for a smart factory using a reinforcement learning approach.

LineFlow: A Framework to Learn Active Control of Production Lines Real-time scheduling for a smart factory using a reinforcement learning approach

Reference 44

Resolution
verified exact
doi, observed 2026-08-15T22:39:59.478642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.342808Z digest=sha256:4b97220bc23c570bd82818c41a5682d4fa434b93cb95420825e42824730dacdb

Observation 185aa2ec-7314-4bf2-a5fa-69f4e69cb48c · outbound

This paper cites J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al.

LineFlow: A Framework to Learn Active Control of Production Lines J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-15T22:39:59.347063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:39:59.347063Z digest=sha256:6229ea09cb1cc7dbbd68a0799c87384032c158f37e38b58f8e2ec4716bf0984f

Observation 091f7f73-3fbc-4f14-bd47-4ca5dbfe56de · outbound

This paper cites Simpy 4.1 webpage, 2025.

LineFlow: A Framework to Learn Active Control of Production Lines Simpy 4.1 webpage, 2025

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:40:00.626798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.351118Z digest=sha256:6b3fe138be6176f03de2a7b575f4feffc8b939828f4437027c33e515ffcfe1dd

Observation 0eaa4b93-0667-4767-8f23-0a512773590b · outbound

This paper cites A., Th \"u rer, M., and Chang, Q.

LineFlow: A Framework to Learn Active Control of Production Lines A., Th \"u rer, M., and Chang, Q

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:40:00.612671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.355146Z digest=sha256:8c91650b8c0567490e5e1118c7bfcf28e39862350a59a035bdb1b0837396d53c

Observation 7c486446-448f-4eac-8b38-0cfec5872a30 · outbound

This paper cites an unresolved cited work.

LineFlow: A Framework to Learn Active Control of Production Lines Unresolved cited work

Reference 48

Resolution
unresolved
raw_fallback, observed 2026-08-15T22:40:00.598097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.359601Z digest=sha256:f90577fd69b8bcdd24196c60f214a6ab62e1aec0b456026c4a396a92a07c18cb

Observation 6a0eb2d9-384e-4191-ba91-22c1d90f6f78 · outbound

This paper cites and Chauhan, S.

LineFlow: A Framework to Learn Active Control of Production Lines and Chauhan, S

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-15T22:39:59.363604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:39:59.363604Z digest=sha256:229f60e277a90ea21832deda1ef7c02feb64bd73448ebb110055757513abb439

Observation 99384850-70fc-4fe3-acd4-880a706bdc72 · outbound

This paper cites A parallel deep reinforcement learning framework for controlling industrial assembly lines.

LineFlow: A Framework to Learn Active Control of Production Lines A parallel deep reinforcement learning framework for controlling industrial assembly lines

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-15T22:39:59.368116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:39:59.368116Z digest=sha256:616bc17716c390c4b430c9afa190b1e7ce69d7908b2586731187f4a0be2611f7

Observation 4c57a98a-ed1b-4b0e-bb05-6cbde7770ec0 · outbound

This paper cites Gymnasium: A Standard Interface for Reinforcement Learning Environments.

LineFlow: A Framework to Learn Active Control of Production Lines Gymnasium: A Standard Interface for Reinforcement Learning Environments

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-15T22:39:59.372428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:39:59.372428Z digest=sha256:76d02787f9cc1180a6c14d908a48458adc5a67cb180582159d23dc4c80dfdc98

Observation 26537d3d-1234-4143-8c7f-7f802c2496f5 · outbound

This paper cites an unresolved cited work.

LineFlow: A Framework to Learn Active Control of Production Lines Unresolved cited work

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-15T22:39:59.377749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:39:59.377749Z digest=sha256:a369a05042521715b1571c1f80f6bf07bd0593cbbef8650eebdd964e724fe295

Observation ed8096bd-d018-4bd9-9ebf-78e959c1b15b · outbound

This paper cites M., Mathieu, M., Dudzik, A., Chung, J., Choi, D.

LineFlow: A Framework to Learn Active Control of Production Lines M., Mathieu, M., Dudzik, A., Chung, J., Choi, D

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-15T22:39:59.382814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:39:59.382814Z digest=sha256:e8ae2f80258bd69196bc95c5113d8b2373c6a477bf02d39dc97c874666fadae0

Observation f9aa09b4-bf25-4362-99b4-cfe810357212 · outbound

This paper cites Multi-agent reinforcement learning based maintenance policy for a resource constrained flow line system.

LineFlow: A Framework to Learn Active Control of Production Lines Multi-agent reinforcement learning based maintenance policy for a resource constrained flow line system

Reference 54

Resolution
verified exact
doi, observed 2026-08-15T22:39:59.446170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.386972Z digest=sha256:e6ddc9de87efe4d0b6db7f3fc2e842d926f3b1d4792a6f2aa9cf55dbb6b7969d

Observation 24aba305-2c6f-479a-8a1f-a01807938f04 · outbound

This paper cites D ata S tructures for S tatistical C omputing in P ython.

LineFlow: A Framework to Learn Active Control of Production Lines D ata S tructures for S tatistical C omputing in P ython

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-15T22:39:59.391495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:39:59.391495Z digest=sha256:7457877823d6d1237e384311915c4ab30811a4c84d1e6162800828f3bff23236

Observation 8b2dd161-baeb-432c-885f-12eb59d3c9e2 · outbound

This paper cites Daydreamer: World models for physical robot learning.

LineFlow: A Framework to Learn Active Control of Production Lines Daydreamer: World models for physical robot learning

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:40:00.575195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.395660Z digest=sha256:1b84716ee883b90e7cc0484a0e09cb51853c11b3c445442e00fabe9d83f208de

Observation d074f798-9f3c-4b24-a337-5cb478bb1a2a · outbound

This paper cites and Lan, R.

LineFlow: A Framework to Learn Active Control of Production Lines and Lan, R

Reference 57

Resolution
metadata mismatch
raw_fallback, observed 2026-08-15T22:39:59.799717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-15T22:39:59.399768Z digest=sha256:e96ec870dda7f24ad88257c8272d907bdf58fdae609e7fff474d274e47220f94

Pith citing papers

No inbound Pith citation observations are available.