Survey of 112 agentic AI for social good papers reveals moral-geographic asymmetry with 73% lacking geographic context (lowest for SDG 16) and only 25% reporting deployments.
Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Evolution has produced a multi-scale mosaic of interacting adaptive units. Innovations arise when perturbations push parts of the system away from stable equilibria into new regimes where previously well-adapted solutions no longer work. Here we explore the hypothesis that multi-agent systems sometimes display intrinsic dynamics arising from competition and cooperation that provide a naturally emergent curriculum, which we term an autocurriculum. The solution of one social task often begets new social tasks, continually generating novel challenges, and thereby promoting innovation. Under certain conditions these challenges may become increasingly complex over time, demanding that agents accumulate ever more innovations.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
The paper characterizes ASI and examines scaling, paradigm shifts, recursive self-improvement, and multi-agent collectives as routes from AGI to ASI, together with frictions and open questions about acceleration.
citing papers explorer
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Whose Good, Whose Place? The Moral Geography of Agentic AI for Social Good
Survey of 112 agentic AI for social good papers reveals moral-geographic asymmetry with 73% lacking geographic context (lowest for SDG 16) and only 25% reporting deployments.
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From AGI to ASI
The paper characterizes ASI and examines scaling, paradigm shifts, recursive self-improvement, and multi-agent collectives as routes from AGI to ASI, together with frictions and open questions about acceleration.