{
  "id": 17735891,
  "title": "MEMIG: Self-evolving Memory Construction for Antibody Optimization",
  "url": "https://urgent.news/2026/10/30/memig-self-evolving-memory-construction-for-antibody-optimization",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-30T04:39:18.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.41245v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Memory has become central to self-evolving LLM agents, which store and retrieve experience from past rollouts to guide future decisions. Existing systems represent this experience as free-form natural language and maintain it by rewriting, which fits tasks whose experience is narratable. However, in scientific discovery and optimization, experience is often a quantity measured by an oracle under…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}