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

arXiv cs.AI Papers
human-ai-interaction social-intelligence coevolution social-cognition long-term-interaction formal-theory arxiv

Summary

Proposes the Human-AI Coevolution Dynamics Framework (HACD-H) as a formal model of human-AI interaction, integrating emotional adaptation, relational organization, social memory, and personality consistency. Results show social intelligence emerges from long-term social cognitive coevolution.

arXiv:2606.19144v1 Announce Type: new Abstract: Current conversational AI systems have made significant progress in language generation, personalization, and long-context interaction. However, most existing methods model social behavior through isolated components such as emotion modeling, memory retrieval, or persona conditioning, lacking a unified framework to explain the emergence of stable social relationships and social intelligence in long-term human-AI interaction.To address this, we propose the Human-AI Coevolution Dynamics Framework (HACD-H), a formal model of human-AI interaction as a self-organizing social cognitive system. HACD-H integrates emotional adaptation, relational organization, social memory, and personality consistency into a unified dynamical framework and introduces principles including multi-timescale social cognition, relational attractors, trust basins, developmental phase transitions, and social cognitive energy dynamics.We construct a conversational dataset with approximately 14,700 interaction turns and develop a theory-driven empirical evaluation framework. Results reveal a hierarchy of temporal persistence in social cognition, stable relational attractors, phase-transition-like developmental patterns, and a structured social cognitive energy landscape. Social intelligence shows a significant negative correlation with social cognitive energy (r = -0.391, p < 0.001), and interaction trajectories exhibit progressive energy reduction over time.These findings suggest that social intelligence emerges from long-term social cognitive coevolution rather than isolated conversational capabilities. HACD-H provides a unified theoretical foundation for modeling adaptive human-AI social interaction and developing socially intelligent AI systems.
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# Human-AI Coevolution Dynamics: A Formal Theory of Social Intelligence Emergence Through Long-Term Interaction
Source: [https://arxiv.org/abs/2606.19144](https://arxiv.org/abs/2606.19144)
[View PDF](https://arxiv.org/pdf/2606.19144)

> Abstract:Current conversational AI systems have made significant progress in language generation, personalization, and long\-context interaction\. However, most existing methods model social behavior through isolated components such as emotion modeling, memory retrieval, or persona conditioning, lacking a unified framework to explain the emergence of stable social relationships and social intelligence in long\-term human\-AI[this http URL](http://interaction.to/)address this, we propose the Human\-AI Coevolution Dynamics Framework \(HACD\-H\), a formal model of human\-AI interaction as a self\-organizing social cognitive system\. HACD\-H integrates emotional adaptation, relational organization, social memory, and personality consistency into a unified dynamical framework and introduces principles including multi\-timescale social cognition, relational attractors, trust basins, developmental phase transitions, and social cognitive energy[this http URL](http://dynamics.we/)construct a conversational dataset with approximately 14,700 interaction turns and develop a theory\-driven empirical evaluation framework\. Results reveal a hierarchy of temporal persistence in social cognition, stable relational attractors, phase\-transition\-like developmental patterns, and a structured social cognitive energy landscape\. Social intelligence shows a significant negative correlation with social cognitive energy \(r = \-0\.391, p < 0\.001\), and interaction trajectories exhibit progressive energy reduction over[this http URL](http://time.these/)findings suggest that social intelligence emerges from long\-term social cognitive coevolution rather than isolated conversational capabilities\. HACD\-H provides a unified theoretical foundation for modeling adaptive human\-AI social interaction and developing socially intelligent AI systems\.

## Submission history

From: Jingyi Zhou \[[view email](https://arxiv.org/show-email/205fe0f5/2606.19144)\] **\[v1\]**Wed, 17 Jun 2026 14:47:59 UTC \(2,441 KB\)

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