Paper Citation Record · LEDGER

Towards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models

As of 19 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2606.08633.

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

pith.paper-citation-record.v1
2606.08633 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-27T18:46:28.287989Z

measured 16 of 16 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

16 of 16 outbound references displayed

verified exact5
verified fuzzy0
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 33fc4ef7-7ca4-4af6-966f-2e521a46789e · outbound

This paper cites Long short-term memory,.

Towards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models Long short-term memory,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-27T18:46:28.287989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3981313b-dde4-4c86-ac4c-5ae1056ae673 · outbound

This paper cites Deep learning models for vessel’s eta prediction: bulk ports perspective,.

Towards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models Deep learning models for vessel’s eta prediction: bulk ports perspective,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-27T18:46:28.287989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 646c99c2-d196-41b9-996c-643b0cbe4003 · outbound

This paper cites Attention is all you need,.

Towards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models Attention is all you need,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-27T18:46:28.287989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da76fdf0-e9d6-4827-8ac1-daddc2ce94ba · outbound

This paper cites Advancements in deep learning techniques for time series forecast- ing in maritime applications: a comprehensive review,.

Towards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models Advancements in deep learning techniques for time series forecast- ing in maritime applications: a comprehensive review,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-27T18:46:28.287989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ce68f22-e59a-4764-bdb8-b8463b2f978a · outbound

This paper cites Informer: Beyond efficient transformer for long sequence time-series forecasting,.

Towards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models Informer: Beyond efficient transformer for long sequence time-series forecasting,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-27T18:46:28.287989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f57c27a-98dd-46a0-9c6a-3374d8299950 · outbound

This paper cites Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting,.

Towards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-27T18:46:28.287989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2258c1ba-5330-4a16-b2a3-e88235801748 · outbound

This paper cites A time series is worth 64 words: Long-term forecasting with transformers,.

Towards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models A time series is worth 64 words: Long-term forecasting with transformers,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-27T18:46:28.287989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ddf3365-b795-4404-9cb9-6d609a7b6308 · outbound

This paper cites Time-LLM: Time Series Forecasting by Reprogramming Large Language Models.

Towards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-07-02T22:37:26.045021Z

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.

Observation 0083bb11-4eef-43a8-9c04-6f822e5630a0 · outbound

This paper cites Urbangpt: Spatio-temporal large language models,.

Towards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models Urbangpt: Spatio-temporal large language models,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-27T18:46:28.287989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 586912a5-8e28-4ea5-aef6-0131f94456ec · outbound

This paper cites Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs.

Towards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-07-02T22:37:26.041851Z

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.

Observation ae3da14d-4974-4eb7-a903-f257f7ff764a · outbound

This paper cites AIS-LLM: A Unified Framework for Maritime Trajectory Prediction, Anomaly Detection, and Collision Risk Assessment with Explainable Forecasting.

Towards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models AIS-LLM: A Unified Framework for Maritime Trajectory Prediction, Anomaly Detection, and Collision Risk Assessment with Explainable Forecasting

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-02T22:37:26.038874Z

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.

Observation b1cf8d9b-a912-4e79-8772-21c05023e96e · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Towards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-07-02T22:37:26.044343Z

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.

Observation 01ab86cb-d785-4d3d-b37d-59a8fa19a4d1 · outbound

This paper cites Llama-3.1-tulu-3-8b-sft,.

Towards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models Llama-3.1-tulu-3-8b-sft,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-27T18:46:28.287989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2dbfa245-5f3d-4c8e-873e-72768c2d575a · outbound

This paper cites Your LLM Knows the Future: Uncovering Its Multi-Token Prediction Potential.

Towards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models Your LLM Knows the Future: Uncovering Its Multi-Token Prediction Potential

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-07-02T22:37:26.047624Z

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.

Observation 85351fd2-031b-4daa-82b3-7c6ab2d74537 · outbound

This paper cites Spatial- temporal large language model for traffic prediction,.

Towards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models Spatial- temporal large language model for traffic prediction,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-27T18:46:28.287989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8829ddb5-5f67-4582-99b7-4b662ca3baf7 · outbound

This paper cites St-llm+: Graph enhanced spatio-temporal large language models for traffic prediction,.

Towards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models St-llm+: Graph enhanced spatio-temporal large language models for traffic prediction,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-27T18:46:28.287989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.