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

Problem
Current conversational AI systems excel in language generation and personalization but often lack a cohesive framework to model social behavior in long-term interactions. Existing approaches typically isolate components such as emotion modeling, memory retrieval, and persona conditioning, failing to capture the dynamics of stable social relationships and social intelligence. This paper addresses this gap by proposing a unified model that integrates these components into a coherent framework. Notably, this work is a preprint and has not undergone peer review.

Method
The authors present the Human-AI Coevolution Dynamics Framework (HACD-H), conceptualizing human-AI interaction as a self-organizing social cognitive system. HACD-H incorporates several principles: multi-timescale social cognition, relational attractors, trust basins, developmental phase transitions, and social cognitive energy dynamics. The framework is empirically evaluated using a dataset comprising approximately 14,700 interaction turns, which allows for a theory-driven analysis of conversational dynamics. The model’s architecture emphasizes the interplay between emotional adaptation, relational organization, social memory, and personality consistency, providing a comprehensive approach to understanding social intelligence in AI.

Results
The empirical evaluation reveals several key findings: a hierarchy of temporal persistence in social cognition, the existence of stable relational attractors, and phase-transition-like developmental patterns in interactions. Notably, social intelligence is found to have a significant negative correlation with social cognitive energy (r = -0.391, p < 0.001), indicating that as social cognitive energy decreases, social intelligence increases. Additionally, interaction trajectories demonstrate a progressive reduction in energy over time, suggesting that effective long-term interactions are characterized by diminishing cognitive energy.

Limitations
The authors acknowledge that while HACD-H provides a robust theoretical framework, it is primarily based on empirical observations from a single dataset, which may limit generalizability. Furthermore, the model’s complexity may pose challenges in practical implementation and scalability in real-world applications. The authors do not address potential biases in the dataset or the implications of varying cultural contexts on social intelligence dynamics.

Why it matters
The introduction of HACD-H offers a significant advancement in the understanding of human-AI interactions, particularly in modeling adaptive social behavior. By framing social intelligence as an emergent property of long-term coevolution, this work lays the groundwork for developing more socially intelligent AI systems capable of sustaining meaningful interactions. The implications extend to various applications, including personalized AI companions, customer service bots, and collaborative AI systems. This foundational theory can guide future research in enhancing the social capabilities of AI, as discussed in detail in the paper available on arXiv.