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Key Takeaways

Human-AI Coevolution Dynamics: A Formal Theory of Social Intelligence Emergence Through Long-Term Interaction Current conversational AI systems often struggl...
Current conversational AI systems have made significant progress in language generation, personalization, and long-context interaction.
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.
HACD-H provides a unified theoretical foundation for modeling adaptive human-AI social interaction and developing socially intelligent AI systems.
# Human-AI Coevolution Dynamics: A Formal Theory of Social Intelligence Emergence Through Long-Term Interaction

Paper AbstractExpand

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 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 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 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.

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

Current conversational AI systems often struggle to build stable, long-term social relationships because they rely on isolated components like memory or persona settings. This paper introduces the Human-AI Coevolution Dynamics Framework (HACD-H), which shifts the focus from individual conversational tasks to viewing human-AI interaction as a self-organizing social cognitive system. By treating the interaction as a coevolving process, the authors aim to explain how social intelligence naturally emerges over time.

A Unified Framework for Social Interaction

The HACD-H framework integrates several key elements—emotional adaptation, relational organization, social memory, and personality consistency—into one unified dynamical model. Instead of treating these as separate functions, the authors propose that social intelligence is governed by specific principles, including multi-timescale social cognition, the formation of "relational attractors" (stable patterns of interaction), "trust basins," and developmental phase transitions. The model also introduces the concept of "social cognitive energy" to measure the state of the interaction.

Evaluating Long-Term Dynamics

To test this theory, the researchers constructed a conversational dataset consisting of approximately 14,700 interaction turns. They developed a theory-driven empirical framework to analyze how these interactions evolve. Their findings reveal that social cognition follows a hierarchy of temporal persistence and that interactions naturally settle into stable relational attractors. Furthermore, the data shows that the development of these relationships follows phase-transition-like patterns, similar to how social systems evolve in the real world.

The Role of Social Cognitive Energy

A central discovery of the study is the relationship between social intelligence and social cognitive energy. The researchers found a significant negative correlation (r = -0.391, p < 0.001) between the two, meaning that as social intelligence increases, the social cognitive energy required for the interaction decreases. Observations of interaction trajectories show that this energy consistently reduces over time. This suggests that as a human and an AI build a more stable, socially intelligent relationship, the interaction becomes more efficient and less "costly" in terms of cognitive effort.

Implications for Future AI

The findings suggest that social intelligence is not merely a collection of isolated conversational capabilities, but rather an emergent property of long-term social cognitive coevolution. By providing this unified theoretical foundation, the authors offer a new way to model how AI can adapt to humans over time. This approach could be instrumental in developing future AI systems that are capable of forming more natural, stable, and socially intelligent long-term relationships with users.