{
  "id": 4053,
  "url": "https://arxiv.org/abs/2605.19352v1",
  "title": "Brain alignment of reasoning and action representations from vision-language and action models during naturalistic gameplay",
  "summary": "Understanding how humans and artificial intelligence systems predict and plan by interacting with their environment is a fundamental challenge at the intersection of neuroscience and machine learning. Most brain-encoding studies focus on aligning artificial models with brain activity during language comprehension or passive visual processing, while interactive brain-alignment studies have to date been largely limited to reinforcement-learning (RL) agents and theory-based models. To address this ",
  "authors": "Subba Reddy Oota, Anant Khandelwal, Khushbu Pahwa, Satya Sai Srinath Namburi, Tanmoy Chakraborty, Bapi S. Raju et al.",
  "category": "research",
  "topics": "safety-alignment,agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-19T04:40:14.000Z",
  "fetched_at": "2026-07-14T16:30:41.584Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4053",
  "original_url": "https://arxiv.org/abs/2605.19352v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}