# Toward Personal Intelligence Through Cooperative Observation

arXiv: https://arxiv.org/abs/2608.17128 · HTML: https://mlsanity.com/paper/2608.17128/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 43 references that are in the map.
- First posted on arXiv 17 Aug 2026.

## Builds on (most cited first, 13 of 13)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Towards Understanding Sycophancy in Language Models](https://mlsanity.com/paper/2310.13548/) (2023)
- [Performative Prediction](https://mlsanity.com/paper/2002.06673/) (2020)
- [Cooperative Inverse Reinforcement Learning](https://mlsanity.com/paper/1606.03137/) (2016)
- [From Agent Traces to Trust: A Survey of Evidence Tracing and Execution Provenance in LLM Agents](https://mlsanity.com/paper/2606.04990/) (2026)
- [What are you optimizing for? Aligning Recommender Systems with Human Values](https://mlsanity.com/paper/2107.10939/) (2021)
- [Toward a Theory of Hierarchical Memory for Language Agents](https://mlsanity.com/paper/2603.21564/) (2026)
- [ClawTrap: A MITM-Based Red-Teaming Framework for Real-World OpenClaw Security Evaluation](https://mlsanity.com/paper/2603.18762/) (2026)
- [Why Solve It Twice? Hierarchical Accumulation of Skills for Transfer-Efficient ML Engineering](https://mlsanity.com/paper/2606.30911/) (2026)
- [GOD model: Privacy Preserved AI School for Personal Assistant](https://mlsanity.com/paper/2502.18527/) (2025)
- [Online Behavioral Advertising: A Literature Review and Research Agenda](https://mlsanity.com/paper/2511.01895/) (2025)

## Abstract (arXiv)

A personal AI system needs a model of the user's goals, constraints, and ongoing commitments to plan and act on their behalf, and the quality of that model is bounded by what the system can observe. Broader observation does not by itself improve assistance because a bounded system must select and compress information for the task at hand. We argue that this observation bottleneck has a cooperative structure: the system builds a partial model of the user's changing life, the user evaluates its actions, and the user's consent and control shape what it can observe next. Useful and inspectable behavior can give users a reason to maintain or expand the observation channel, while failures can lead them to correct, narrow, revoke, or abandon it. We use the term cooperative observation for this feedback loop among usefulness, trust, and future access, and propose it as a framework for personal intelligence. We report a preliminary single-subject account from Organizm, a prototype used over six months, and outline evaluation directions for measuring how observation quality shapes personal AI.