Researchers propose a framework called 'cooperative observation' to improve the performance of personal AI systems. They argue that these systems need a model of their user's goals and constraints, but current limitations on what they can observe hinder this process. The authors suggest a feedback loop between usefulness, trust, and future access to observation quality, which they believe is essential for personal intelligence. A prototype called Organizm was tested with one subject over six months, and the researchers outline directions for evaluating the impact of observation quality on AI performance. --- Why it matters: This matters because it addresses a fundamental limitation in current personal AI systems: their inability to fully understand user goals and constraints due to observational limitations. Improving this aspect can lead to more effective and trustworthy AI assistants. Source: https://arxiv.org/abs/2608.17128