arXiv:2606. 18181v1 Announce Type: cross Abstract: Illegal, unreported, and unregulated fishing (IUU) traditionally refers to fishing activities that violate applicable laws or occur in areas that lack applicable laws.
arXiv:2606. 29721v1 Announce Type: cross Abstract: Maritime anomaly detection is essential for ensuring maritime safety, security, and efficient traffic management at sea, with Automatic Identification System (AIS) data serving as a primary data source.
arXiv:2606. 08376v1 Announce Type: cross Abstract: As artificial intelligence (AI) systems are increasingly deployed across socially consequential domains, reports of AI-related harms and failures have grown in frequency and diversity.
arXiv:2609.16627v1 Announce Type: new Abstract: Quantifying organizational environmental action from publicly available web content remains a challenging environmental data science problem because re...
The paper introduces DGEval, a benchmark of 1,678 questions designed to assess large language models (LLMs) on the International Maritime Dangerous Goods (IMDG) Code Amendment 42‑24. It evaluates 13 models from six providers, finding that while the best model surpasses human practitioners on multiple‑choice tasks, all models perform poorly on safety‑critical areas such as stowage, segregation, and regulatory recall. The study concludes that LLMs can aid compliance tasks—especially structured Dangerous Goods List lookups with web search—but human oversight and authoritative source verification remain essential for safety‑critical deployment.
arXiv:2607. 17745v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly considered for environmental enforcement, but their ability to produce traceable enforcement decisions remains unclear.
arXiv:2606. 19710v1 Announce Type: cross Abstract: Court proceedings contain valuable evidence about human smuggling networks, but this information is often buried within unstructured, jargon-heavy legal documents.
arXiv:2609.22136v1 Announce Type: cross Abstract: Text anomaly detection, the task of identifying text instances that deviate from normal language patterns, is crucial for language-driven application...
arXiv:2606. 13249v1 Announce Type: new Abstract: Maritime accident adjudication reports contain critical tribunal findings for root cause analysis (RCA), yet retrieving relevant precedents and drafting consistent reports from decades of records remains labor-intensive.
arXiv:2605.29742v2 Announce Type: replace Abstract: Deploying Large Language Models (LLMs) for regulatory compliance demands rigorous traceability via comprehensive citations across multi-tiered auth...
arXiv:2603. 02150v2 Announce Type: replace-cross Abstract: The extraction of critical information from crime-related documents is a crucial task for law enforcement agencies.
arXiv:2607. 07834v1 Announce Type: new Abstract: Pseudo-nitzschia diatoms pose recurrent risks to coastal ecosystems and shellfish harvesting along the Portuguese Atlantic coast.
Diff Mining is a framework that identifies what a finetuned language model has learned by comparing its logits to those of its base model. It extracts per-context logit differences on a reference corpus and aggregates them into an interpretable token set using either a Top‑K frequency method or Non‑negative Matrix Factorization. The approach outperforms existing model‑diffing methods in domain detection and bias identification, and it requires only access to output logits, making it scalable to large models.