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Dovetail is a small, internationally distributed agent foundations research group led by Alex Altair, a former MIRI fellow, MATS scholar, and AI Safety Camp research lead. The group also includes Alfred Harwood (physics PhD from University College London), José Pedro Faustino (mathematics and computer science undergraduate at the University of São Paulo), Dalcy Ku (Harvard undergraduate and 2023 Atlas Fellow), and Daniel C (applied mathematics MSc student at Imperial College London).
The group's research is purely mathematical and does not involve machine learning implementation. It draws on dynamical systems, probability theory, information theory, algorithmic information theory, measure theory, and ergodic theory. Core research questions include formalizing the definition of optimization, defining agents and solving the agent structure problem, understanding convergence of abstractions, formalizing value fragility, and assessing utility maximization convergence. Dovetail explicitly positions itself as not doing formal verification or building provably safe AI, but rather developing foundational conceptual clarity in a field that currently lacks consensus definitions…
Dovetail believes that many of the core arguments about AI danger can be formalized as precise mathematical theorems, and that doing so would provide clearer safety guidance, improve coordination among researchers, and legitimize policy interventions. Their two key bets are: (1) that conceptual arguments about existential risk from AI can be converted into rigorous mathematical statements, eliminating ambiguity and enabling better coordination; and (2) that safety-relevant properties of AI systems can be understood through general architectural properties · such as optimizer strength and proximity to ideal agent designs · using mathematical tools rather than purely empirical exploration. By establishing foundational paradigms for agent theory and deconfusing researchers about the nature of AI agents, Dovetail aims to enable downstream safety research to be more targeted and effective.
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