Meta 在最新論文中提出 PAMO 訓練方法，讓個人 AI 智慧體能利用跨平臺使用者歷史提升推薦精度。
- 實驗結果：在 Steam、Nintendo、Xbox 平臺上，PAMO 版 4B 智慧體達到 47.9% 的 top‑10 hit rate，遠超平臺原生 40.8%，並在所有 5 個測試集上優於傳統 RL 訓練。
New Meta paper shows that a personal AI agent can improve an app's recommendations with your history from other apps, if it learns when to defer.
Each app ranks items using only what you did there, plus patterns from other users. A personal agent sees activity across apps, but it can also break a good ranking by dropping the right item.
They built PAMO, a training method that hides the cross-app history to see which of the agent's answers depend on it. Answers that lean on that history get more credit, but only if ranking accuracy does not drop.
On real Steam users with Nintendo and Xbox history, a 4B PAMO agent reached 47.9% top-10 hit rate, versus 40.8% for the platform. It also beat standard RL training on all 5 test sets.
Personal agents that edit other apps' results should defer by default and override only when the user's history gives specific evidence.
– arxiv. org/abs/2610.07588
Title: "Personal-Agent Mediated Recommendation with Cross-Platform User History"