Paper Citation Record · LEDGER

Temporal-IRL: Modeling Port Congestion and Berth Scheduling with Inverse Reinforcement Learning

As of 23 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2506.19843.

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

pith.paper-citation-record.v1
2506.19843 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:29:57.200155Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

25 of 25 outbound references displayed

verified exact1
verified fuzzy19
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation adc62bab-bcd1-49b8-917b-b0661acd064b · outbound

This paper cites A methodology to assess vessel berthing and speed optimization policies.

Temporal-IRL: Modeling Port Congestion and Berth Scheduling with Inverse Reinforcement Learning A methodology to assess vessel berthing and speed optimization policies

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:29:57.635087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e90e346f-2ccc-4e48-92b4-0eebd2f050fd · outbound

This paper cites Power relations in global supply chains and the unequal distribution of costs during crises: Abandoning garment suppliers and workers during the covid-19 pandemic.

Temporal-IRL: Modeling Port Congestion and Berth Scheduling with Inverse Reinforcement Learning Power relations in global supply chains and the unequal distribution of costs during crises: Abandoning garment suppliers and workers during the covid-19 pandemic

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:29:57.619163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b4935323-fcc9-4993-bbd5-f76a8a457b6f · outbound

This paper cites Elucidating us import supply chain dynamics.

Temporal-IRL: Modeling Port Congestion and Berth Scheduling with Inverse Reinforcement Learning Elucidating us import supply chain dynamics

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:29:57.601836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ead1765b-bbcb-44aa-9fdf-e8d2cb4f22e3 · outbound

This paper cites The cost of being landlocked: logistics costs and supply chain reliability, volume 4258.

Temporal-IRL: Modeling Port Congestion and Berth Scheduling with Inverse Reinforcement Learning The cost of being landlocked: logistics costs and supply chain reliability, volume 4258

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:29:57.584496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1d39fabf-7fda-4ba4-83b1-b767d047cc84 · outbound

This paper cites Dynamic inverse reinforcement learning for characterizing animal behavior.

Temporal-IRL: Modeling Port Congestion and Berth Scheduling with Inverse Reinforcement Learning Dynamic inverse reinforcement learning for characterizing animal behavior

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:29:57.565784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 114157d6-1880-4488-998e-e663cd83f1f2 · outbound

This paper cites Autoencoders.

Temporal-IRL: Modeling Port Congestion and Berth Scheduling with Inverse Reinforcement Learning Autoencoders

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:29:57.547471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ec26a8b6-1a44-46ef-929b-ae4554ae75ce · outbound

This paper cites The impact of port and trade security initiatives on maritime supply-chain management.

Temporal-IRL: Modeling Port Congestion and Berth Scheduling with Inverse Reinforcement Learning The impact of port and trade security initiatives on maritime supply-chain management

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:29:57.532093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 719ddc51-656c-4088-ab5b-569d3dda6156 · outbound

This paper cites Using machine learning to predict port congestion: A study of the port of paranagu \'a.

Temporal-IRL: Modeling Port Congestion and Berth Scheduling with Inverse Reinforcement Learning Using machine learning to predict port congestion: A study of the port of paranagu \'a

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:29:57.511172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 30c5a130-a31b-497e-a36f-c155cdcef730 · outbound

This paper cites Predicting traffic phases from car sensor data using machine learning.

Temporal-IRL: Modeling Port Congestion and Berth Scheduling with Inverse Reinforcement Learning Predicting traffic phases from car sensor data using machine learning

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:29:57.494788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1bfd7fe2-e4af-417a-a901-e8087885af50 · outbound

This paper cites Investigation and imitation of human captains' maneuver using inverse reinforcement learning.

Temporal-IRL: Modeling Port Congestion and Berth Scheduling with Inverse Reinforcement Learning Investigation and imitation of human captains' maneuver using inverse reinforcement learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:29:57.478472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 87debf92-57f7-4300-a596-b6e35423ea0d · outbound

This paper cites Long short-term memory.

Temporal-IRL: Modeling Port Congestion and Berth Scheduling with Inverse Reinforcement Learning Long short-term memory

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T18:29:57.128761Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab5dedb0-3b9c-4675-8944-1d8e195bcfad · outbound

This paper cites Reinforcement learning: A survey.

Temporal-IRL: Modeling Port Congestion and Berth Scheduling with Inverse Reinforcement Learning Reinforcement learning: A survey

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T18:29:57.133052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51526e2d-9c17-4834-8e41-72410972bd8e · outbound

This paper cites Forecasting public transport ridership: Management of information systems using cnn and lstm architectures.

Temporal-IRL: Modeling Port Congestion and Berth Scheduling with Inverse Reinforcement Learning Forecasting public transport ridership: Management of information systems using cnn and lstm architectures

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:29:57.438044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation aa4fd899-fb7b-4ccf-ad85-d444052ec8bc · outbound

This paper cites Using artificial neural network model for berth congestion risk prediction.

Temporal-IRL: Modeling Port Congestion and Berth Scheduling with Inverse Reinforcement Learning Using artificial neural network model for berth congestion risk prediction

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:29:57.422178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4a63877f-d62e-4d08-afad-3fadb834b6ff · outbound

This paper cites Congestion analysis of waterborne, containerized imports from asia to the united states.

Temporal-IRL: Modeling Port Congestion and Berth Scheduling with Inverse Reinforcement Learning Congestion analysis of waterborne, containerized imports from asia to the united states

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:29:57.405041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 702ab5ae-b8fc-4f7a-8357-e229d7b8ffe5 · outbound

This paper cites Algorithms for inverse reinforcement learning.

Temporal-IRL: Modeling Port Congestion and Berth Scheduling with Inverse Reinforcement Learning Algorithms for inverse reinforcement learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T18:29:57.153377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce9cce75-b1b4-41d8-981c-e36f40cd72f3 · outbound

This paper cites A deep learning approach for port congestion estimation and prediction.

Temporal-IRL: Modeling Port Congestion and Berth Scheduling with Inverse Reinforcement Learning A deep learning approach for port congestion estimation and prediction

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:29:57.375414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 76827fb8-238f-49b3-ac66-411a3de2c13e · outbound

This paper cites Analysis of port waiting time due to congestion by applying markov chain analysis.

Temporal-IRL: Modeling Port Congestion and Berth Scheduling with Inverse Reinforcement Learning Analysis of port waiting time due to congestion by applying markov chain analysis

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:29:57.358718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7f5518f6-4e85-477c-93c4-92086f004b94 · outbound

This paper cites Rumelhart, Geoffrey E.

Temporal-IRL: Modeling Port Congestion and Berth Scheduling with Inverse Reinforcement Learning Rumelhart, Geoffrey E

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:29:57.342840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9d49aa12-efd9-4019-a36f-3980e19ed5fd · outbound

This paper cites Choosing a port: An analysis of containerized imports into the us.

Temporal-IRL: Modeling Port Congestion and Berth Scheduling with Inverse Reinforcement Learning Choosing a port: An analysis of containerized imports into the us

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:29:57.327869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ff8fa92c-9792-4d60-886c-7bcd242c3bad · outbound

This paper cites The 2020 covid-19 pandemic and global value chains.

Temporal-IRL: Modeling Port Congestion and Berth Scheduling with Inverse Reinforcement Learning The 2020 covid-19 pandemic and global value chains

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:29:57.311639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e07b7190-a92e-447f-b1ae-4acd29b0aaf2 · outbound

This paper cites Cross-Lingual Pitfalls: Automatic Probing Cross-Lingual Weakness of Multilingual Large Language Models.

Temporal-IRL: Modeling Port Congestion and Berth Scheduling with Inverse Reinforcement Learning Cross-Lingual Pitfalls: Automatic Probing Cross-Lingual Weakness of Multilingual Large Language Models

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-15T18:29:57.264568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7646a186-5934-4176-8c13-bb112357b79c · outbound

This paper cites SocialMaze: A Benchmark for Evaluating Social Reasoning in Large Language Models.

Temporal-IRL: Modeling Port Congestion and Berth Scheduling with Inverse Reinforcement Learning SocialMaze: A Benchmark for Evaluating Social Reasoning in Large Language Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T18:29:57.188033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83a80653-2634-4bcd-ba1a-907a27350bcd · outbound

This paper cites Evaluation of the marine traffic congestion of north harbor in busan port.

Temporal-IRL: Modeling Port Congestion and Berth Scheduling with Inverse Reinforcement Learning Evaluation of the marine traffic congestion of north harbor in busan port

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:29:57.293371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c8aec9be-1427-4228-ab9a-44f36d30e3ee · outbound

This paper cites Maximum entropy inverse reinforcement learning.

Temporal-IRL: Modeling Port Congestion and Berth Scheduling with Inverse Reinforcement Learning Maximum entropy inverse reinforcement learning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T18:29:57.200155Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.