What Capable Agents Must Know: Selection Theorems for Robust Decision-Making under Uncertainty
arXiv:2603.02491v3 Announce Type: replace Abstract: As artificial agents become increasingly capable, what internal structure is necessary for an agent to act competently under uncertainty? Classical results show that optimal control can be implemented using belief states or world models, but not that such representations are required. We prove quantitative "selection theorems" showing that strong task performance (low average-case regret) forces world models, belief-like memory and -- under task mixtures -- persistent regime-tracking variables resembling functional primitives of emotion, alon
This paper comes at a time when the development of increasingly capable artificial agents is a central focus in AI research, prompting deeper theoretical understanding of their necessary internal architectures.
A strategic reader should care because this research provides theoretical grounding for the internal structures required for robust AI agents, influencing future development and safety. It suggests that complex AI will necessarily develop certain human-like cognitive primitives.
This research shifts the understanding from 'optimal control can be implemented' to 'optimal control forces' specific internal representations like world models and potentially 'emotional' variables in advanced agents.
- · AI researchers
- · Robotics companies
- · AI safety organizations
- · Developers of simplistic AI architectures
- · Theories downplaying cognitive complexity in advanced AI
The theoretical necessity of world models and belief states will guide the design and evaluation of next-generation AI agents.
Understanding these 'selection theorems' could enable more predictable and controllable advanced AI, potentially reducing unexpected emergent behaviors.
The insight into 'regime-tracking variables resembling functional primitives of emotion' might lead to AI systems that display more nuanced or interpretable behaviors in complex real-world scenarios.
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Read at arXiv cs.LG