Hybrid Intelligence and Third-Space Cognition Interaction-Level Emergence in Sustained Human–AI Coupling

This paper proposes the concept of "third-space cognition" to describe cognitive dynamics that emerge within sustained recursive human–AI interaction rather than residing in either participant alone. Drawing on approximately 500+ conversational sessions across multiple LLM architectures (GPT-series, Claude, Grok, DeepSeek, Perplexity), it identifies four recurring patterns: reconstructive personalization, cross-model relational convergence, reciprocal attentional shaping, and interaction-level alignment failures. The paper argues that hybrid intelligence must be understood as an interactional phenomenon, and that alignment frameworks must account for relational dynamics and drift across sustained engagement — not only model-level output safety.