Human-AI Coevolution Dynamics: A Formal Theory of Social Intelligence Emergence Through Long-Term Interaction
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
The rapid advancement in conversational AI capabilities has exposed the limitations of current siloed approaches to social intelligence, necessitating a more unified and theoretical framework.
This research provides a foundational theory for understanding and building socially intelligent AI, moving beyond mere conversational fluency to establishing stable human-AI relationships, significantly altering the potential applications and societal integration of AI.
The focus in AI development shifts from isolated components of social behavior to a coevolutionary dynamic, implying a more integrated and relationally-oriented design for future AI systems.
- · AI ethicists and social scientists
- · Developers of general AI systems
- · AI-driven service industries
- · Users engaging with AI
- · AI systems relying solely on isolated social modeling
- · Companies with static AI persona development
- · Traditional interaction design paradigms
AI systems will exhibit more coherent and stable social behaviors, fostering deeper user engagement.
Human-AI interaction becomes a two-way street, where human behavior also influences AI development and vice-versa.
The definition of 'social intelligence' itself might evolve, incorporating the unique dynamics of human-AI partnerships.
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Read at arXiv cs.AI