{
  "id": 17352,
  "url": "https://arxiv.org/abs/2608.06105v1",
  "title": "Does Latent Context Help? A Controlled Evaluation of Inverse Reinforcement Learning in Arctic Shipping",
  "summary": "Artificial Intelligence (AI)-assisted navigation can help Arctic shipping adapt to rapidly changing sea-ice conditions, but reliable deployment requires reward models that are interpretable and robust to changing environments. Inverse reinforcement learning (IRL) provides a framework for recovering such rewards from vessel trajectories, while recent meta-IRL methods introduce latent context variables to capture behavioral heterogeneity. However, it remains unclear whether these latent representa",
  "authors": "Vaishnav Vaidheeswaran, Dilith Jayakody, Biruk Ambaw, Jaswanth Kumar, Md Mahbub Alam, Gabriel Spadon",
  "category": "research",
  "topics": "environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-06T14:39:55.000Z",
  "fetched_at": "2026-08-07T05:10:58.501Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/17352",
  "original_url": "https://arxiv.org/abs/2608.06105v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}