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Contemplative Wisdom for Superalignment
Preprint   Open access

Contemplative Wisdom for Superalignment

Ruben Laukkonen, Fionn Inglis, Shamil Chandaria, Lars Sandved-Smith, Edmundo Lopez-Sola, Jakob Hohwy, Jonathan Gold and Adam Elwood
arXiv (Cornell University)
Cornell University
21/04/2025
DOI: https://doi.org/10.48550/arxiv.2504.15125
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Preprint (Author's original)CC BY V4.0,  Open Access
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Preprint (Author's original)CC BY V4.0,  Open

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Abstract

Artificial intelligence Neuroscience Meditation Buddhism Alignment Large language models Neural networks Machine learning Mindfulness
As artificial intelligence (AI) improves, traditional alignment strategies may falter in the face of unpredictable self-improvement, hidden subgoals, and the sheer complexity of intelligent systems. Inspired by contemplative wisdom traditions, we show how four axiomatic principles can instil a resilient ‘Wise World Model’ in AI systems. First, mindfulness enables self monitoring and recalibration of emergent subgoals. Second, emptiness forestalls dogmatic goal fixation and relaxes rigid priors. Third, non-duality dissolves adversarial self–other boundaries. Fourth, boundless care motivates the universal reduction of suffering. We find that prompting AI to reflect on these principles improves performance on the AILuminate Benchmark (d=.96) and boosts cooperation and joint-reward on the Prisoner’s Dilemma task (d=7+). We offer detailed implementation strategies at the level of architectures, constitutions, and reinforcement on chain-of-thought. For future systems, active inference may offer the self-organizing and dynamic coupling capabilities needed to enact Contemplative AI in embodied agents.

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