{
  "id": 13708,
  "url": "https://arxiv.org/abs/2607.22157v1",
  "title": "Learning on the Job: Continual Learning from Deployment Feedback for Frozen-Weights Agents",
  "summary": "AI agents encounter learning opportunities in every episode they run, and discard nearly all of them: the underlying models are frozen at deployment, so an agent that resolves a difficult request today starts from zero when it recurs tomorrow. Yet ordinary operation already produces feedback, in the form of outcome verdicts and after-the-fact corrections. We show that this feedback is a sufficient signal for continual learning when the frozen model is paired with an external memory that distils",
  "authors": "Valentin Tablan, Scott Taylor, Kristoffer Bernhem",
  "category": "research",
  "topics": "jobs-economy,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-24T10:01:00.000Z",
  "fetched_at": "2026-07-27T05:10:06.638Z",
  "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/13708",
  "original_url": "https://arxiv.org/abs/2607.22157v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}