{
  "id": 1345,
  "url": "https://arxiv.org/abs/2606.07909v2",
  "title": "MemToolAgent: Leveraging Memory for Tool Using Agents Based on Environment and User Feedback",
  "summary": "Modern large language model (LLM) agents can use external tools to help users solve complex tasks. However, for problems that require learning from long-term historical events or from previous agent-environment interactions, LLM agents are required to use memory mechanisms to store and retrieve experiences. While sophisticated memory systems exist for dialogue agents, few studies have empirically examined how to improve agents' tool-using capabilities through past user-agent conversations. We pr",
  "authors": "Suleyman Armagan Er, Danilo Ribeiro, Yogesh Virkar, Surafel Lakew, Adi Kalyanpur, James Gung et al.",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-06T00:15:00.000Z",
  "fetched_at": "2026-07-14T14:15:12.457Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/1345",
  "original_url": "https://arxiv.org/abs/2606.07909v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}