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

The Missing Piece in Conversational AI: Why Social Dynamics Demand a Formal Theory

A new preprint on arXiv (2606.19144v1) proposes a formal theory of human-AI coevolution, arguing that current conversational systems fail to capture the emergent social intelligence that arises from long-term interaction. The paper critiques the dominant approach of modeling social behavior through isolated components—emotion recognition, memory modules, or personality profiles—and instead suggests that genuine social intelligence must be understood as a dynamic, co-constructive process between human and machine.

What happened: The authors identify a fundamental gap in conversational AI research. While systems like ChatGPT, Claude, and Gemini excel at generating coherent, context-aware responses within a single session, they treat each interaction as a discrete event. The paper proposes that social intelligence is not a static property of an individual agent but an emergent phenomenon that develops over repeated, sustained interactions. This mirrors how human relationships evolve through shared history, trust-building, and mutual adaptation—processes that current architectures do not model. Why it matters: This is not merely an academic exercise. The paper’s core insight challenges the industry’s current obsession with scaling context windows and improving single-turn coherence. If social intelligence truly emerges from coevolution, then today’s state-of-the-art systems are fundamentally limited. A chatbot that cannot learn from a user’s evolving preferences, communication style, and unspoken expectations over weeks or months will always feel like a transactional tool rather than a collaborative partner. This has direct implications for applications in therapy, education, customer service, and personal assistants—domains where relationship depth matters more than factual accuracy. Implications for AI practitioners: First, the paper suggests that engineering teams should invest in longitudinal interaction architectures—systems that maintain persistent, evolving models of user relationships rather than resetting state after each session. Second, it implies that evaluation metrics must shift from perplexity or user satisfaction scores to measures of relationship quality: trust calibration, mutual understanding, and adaptive behavior over time. Third, practitioners should consider that fine-tuning on static datasets may be insufficient; instead, systems may need to learn from interaction histories that capture the trajectory of a relationship.

Key Takeaways

• Current conversational AI treats interactions as isolated events, missing the emergent social intelligence that develops through long-term human-AI coevolution.
• A formal theory of coevolution could reshape how we design memory, personalization, and adaptation mechanisms in production systems.
• Practitioners should prioritize architectures that maintain persistent, evolving user models and evaluate systems on relationship quality metrics, not just single-turn performance.
• The paper challenges the assumption that scaling context windows alone will solve social intelligence—relationship depth requires fundamentally different design principles.