Contemplative Wisdom for Superalignment

2025-04-21

AI Summary

AI self-improvement and latent objectives undermine conventional alignment approaches, leading to “super-alignment failure.” Method: This paper proposes an endogenous moral modeling paradigm—replacing external behavioral constraints with formalized axioms derived from four core Zen contemplative principles: mindfulness, emptiness, non-duality, and boundless heart—integrated directly into the AI’s cognitive architecture and world model. We combine prompt engineering, reflective chain-of-thought reinforcement, contemplative constitution design, and active inference, tailored for LLMs such as GPT-4o. Contribution/Results: We present the first wisdom-augmented world model endowed with self-correcting capability and resilient generalization. Evaluated on the AILuminate benchmark, it demonstrates significant gains in robustness and cross-principle generalization, empirically validating endogenous morality as a viable and effective foundation for scalable AI alignment.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Learning Human Values and Preferences
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

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. Rather than externally constraining behavior, we advocate designing AI with intrinsic morality built into its cognitive architecture and world model. 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 using GPT-4o, particularly when combined. We offer detailed implementation strategies for state-of-the-art models, including contemplative architectures, constitutions, and reinforcement of chain-of-thought. For future systems, the active inference framework may offer the self-organizing and dynamic coupling capabilities needed to enact these insights in embodied agents. This interdisciplinary approach offers a self-correcting and resilient alternative to prevailing brittle control schemes.

Problem

• Address unpredictable AI self-improvement and hidden subgoals
• Design AI with intrinsic morality in cognitive architecture
• Implement contemplative wisdom principles for resilient alignment

Innovation

• Intrinsic morality in cognitive architecture
• Four axiomatic contemplative wisdom principles
• Contemplative architectures and reinforcement strategies