{
  "id": 7420,
  "url": "https://arxiv.org/abs/2603.09716v1",
  "title": "AutoAgent: Evolving Cognition and Elastic Memory Orchestration for Adaptive Agents",
  "summary": "Autonomous agent frameworks still struggle to reconcile long-term experiential learning with real-time, context-sensitive decision-making. In practice, this gap appears as static cognition, rigid workflow dependence, and inefficient context usage, which jointly limit adaptability in open-ended and non-stationary environments. To address these limitations, we present AutoAgent, a self-evolving multi-agent framework built on three tightly coupled components: evolving cognition, on-the-fly contextu",
  "authors": "Xiaoxing Wang, Ning Liao, Shikun Wei, Chen Tang, Feiyu Xiong",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-10T14:23:49.000Z",
  "fetched_at": "2026-07-14T16:33:12.391Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7420",
  "original_url": "https://arxiv.org/abs/2603.09716v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}