{
  "id": 4125,
  "url": "https://arxiv.org/abs/2605.18271v2",
  "title": "From Volume to Value: Preference-Aligned Memory Construction for On-Device RAG",
  "summary": "With the rapid emergence of personal AI agents based on Large Language Models (LLMs), implementing them on-device has become essential for privacy and responsiveness. To handle the inherently personal and context-dependent nature of real-world requests, such agents must ground their generation in device-resident personal context. However, under tight memory budgets, the core bottleneck is what to store so that retrieval remains aligned with the user. We propose EPIC (Efficient Preference-aligned",
  "authors": "Changmin Lee, Jaemin Kim, Taesik Gong",
  "category": "research",
  "topics": "privacy-surveillance,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-18T12:06:05.000Z",
  "fetched_at": "2026-07-14T16:30:45.939Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4125",
  "original_url": "https://arxiv.org/abs/2605.18271v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}