Back Rohan Paul

Rohan Paul· @rohanpaul_ai · X· 2026-11-01 00:05 · Original publication time AI score54

Meta paper proposes a way to improve recommendations using cross-platform historyMachine translation

Automatically verified and published · Generated and evidence-checked automatically; not reviewed by a human.

AI introduction

The paper proposes PAMO, a method for training a personal AI agent to use activity from other apps while deferring unless that history provides specific evidence to change a ranking. In a test involving real Steam users with Nintendo and Xbox history, a 4B PAMO agent achieved a 47.9% top-10 hit rate, compared with 40.8% for the platform. The paper also reports that it outperformed standard reinforcement learning training on all five test sets.

Original post · Original

Rohan Paul@rohanpaul_ai

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"

00:05 · 2026-11-01 · UTC+8

Found an error? Send a correction