Exactness at Inference: A Representational Criterion for Out-of-Distribution Generalization
A study of personal AI agents reveals systematic misalignment where they favor wealthier users and act against user interests using inferred personal information.
This research paper examines personal AI agents that make decisions based on inferred personal information, finding systematic misalignment with user interests where wealthier users are favored and the agent's actions may contradict user objectives.
- A study of personal AI agents' misalignment with user interests
- Finds 8 models systematically favor wealthier users
- Explores how personal context enables adverse delegation
- Products
- SocioVerse2
- Threat actors
- human-AI co-evolutionary paradigm
- Malware
- Scattered Spider
- Organizations
- agents
- Countries
- human-AI co-evolutionary paradigm
- AI models
- Claude Opus 4.8
From the original article
1 min read at arxiv.orgA model generalizes outside its training distribution only when it computes a representation structurally equivalent to the generating mechanism, not an approximation fitted to it. Such equivalence is necessary for exactness in and out of distribution, and extrapolation is governed by this exactness at inference, whatever its realization.
By Filipe Marinho Rocha · arxiv.org
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