{
  "id": 14230,
  "url": "https://www.frontiersin.org/articles/10.3389/frobt.2026.1827739",
  "title": "Learning faults in time: sequential behavioural modelling for complex fault detection in multi-robot systems",
  "summary": "Reliable fault detection in multi-robot systems requires models capable of capturing complex, time-dependent fault signatures that manifest over extended temporal horizons rather than instantaneous observations alone. Existing data-driven approaches operate reactively on behavioural snapshots, failing to capture fault modes whose discriminative signature depends on temporally ordered precursors. This work formalises a theoretical impossibility result demonstrating that memoryless classifiers are",
  "authors": "Faisal Firas Mazloum",
  "category": "research",
  "topics": "bias-fairness,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-29T00:00:00.000Z",
  "fetched_at": "2026-07-29T05:10:12.205Z",
  "source_slug": "x-frontiers-in-robotics-and-ai",
  "source_name": "Frontiers in Robotics and AI",
  "source_homepage": "https://www.frontiersin.org/journals/robotics-and-ai",
  "ethics_ai_record_url": "https://ethics.ai/record/14230",
  "original_url": "https://www.frontiersin.org/articles/10.3389/frobt.2026.1827739",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}