RLVR post-training of LLMs on semantic AIS data improves long-horizon maritime trajectory and destination forecasting over zero-shot LLMs and deep learning baselines, with 4B models performing best.
AIS-LLM: A unified framework for maritime tra- jectory prediction, anomaly detection, and collision risk assessment with explainable forecasting
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
CmIVTP fuses AIS motion data and CCTV scene features via a cross-modal interaction transformer and introduces the Maritime-MmD+ dataset to improve multimodal vessel trajectory prediction.
The paper proposes a bidirectional continuum between LLMs and control systems, covering LLM-assisted controller design, control-based LLM steering, and state-space modeling of LLMs.
citing papers explorer
-
Towards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models
RLVR post-training of LLMs on semantic AIS data improves long-horizon maritime trajectory and destination forecasting over zero-shot LLMs and deep learning baselines, with 4B models performing best.
-
CmIVTP: Cross-modal Interaction-based Vessel Trajectory Prediction for Maritime Intelligence
CmIVTP fuses AIS motion data and CCTV scene features via a cross-modal interaction transformer and introduces the Maritime-MmD+ dataset to improve multimodal vessel trajectory prediction.
-
When control meets large language models: From words to dynamics
The paper proposes a bidirectional continuum between LLMs and control systems, covering LLM-assisted controller design, control-based LLM steering, and state-space modeling of LLMs.