{
  "id": 15702428,
  "title": "WeAct: Dynamic Weight Switching as Agent Actions",
  "url": "https://urgent.news/2026/10/19/weact-dynamic-weight-switching-as-agent-actions",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-19T17:59:19.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.26847v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Long-horizon agents must learn heterogeneous capabilities from interleaved trajectories. Updating shared parameters to learn new capabilities can degrade previously acquired ones, resulting in catastrophic forgetting. As a trajectory unfolds, different stages may require different capabilities, yet conventional joint training and static adapter merging maintain a fixed parameter configuration…",
  "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."
}