Human-AI Coevolution Dynamics: A Formal Theory of Social Intelligence Emergence Through Long-Term Interaction

2026-06-18 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

The Human-AI Coevolution Dynamics Framework (HACD-H) proposes a formal model for the emergence of social intelligence and stable social relationships in long-term human-AI interaction. Addressing limitations of current conversational AI that use isolated components for social behavior, HACD-H integrates emotional adaptation, relational organization, social memory, and personality consistency into a unified dynamical system. It introduces principles like multi-timescale social cognition, relational attractors, trust basins, and social cognitive energy dynamics. An empirical evaluation using a conversational dataset of approximately 14,700 turns revealed a hierarchy of temporal persistence in social cognition, stable relational attractors, and phase-transition-like developmental patterns. Social intelligence showed a significant negative correlation with social cognitive energy (r = -0.391, p < 0.001), with interaction trajectories exhibiting progressive energy reduction over time. These findings suggest social intelligence arises from long-term social cognitive coevolution.

Key takeaway

For AI Scientists developing socially intelligent AI systems, you should shift focus from isolated social components to long-term coevolutionary dynamics. Implement frameworks that integrate emotional adaptation, relational organization, and social memory to foster stable human-AI relationships. Your designs should aim to reduce social cognitive energy over time, as this correlates with increased social intelligence and more robust interactions.

Key insights

Social intelligence in human-AI interaction emerges from long-term coevolution, not isolated components, driven by dynamic social cognitive energy.

Principles

• Social cognition operates across multiple timescales.
• Relational attractors and trust basins guide interaction.
• Social intelligence correlates with reduced cognitive energy.

Method

HACD-H models human-AI interaction as a self-organizing social cognitive system, integrating emotional adaptation, relational organization, social memory, and personality consistency into a unified dynamical framework.

In practice

• Design AI for long-term emotional adaptation.
• Incorporate relational memory for consistent personas.
• Optimize AI for social cognitive energy reduction.

Topics

• Human-AI Interaction
• Social Intelligence
• Conversational AI
• Coevolution Dynamics
• Social Cognition

Best for: NLP Engineer, Research Scientist, AI Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.