Advancing Conversational AI: Investigating AI-to-AI Voice Dialogue Through Hybrid Active Inference (HAI) and Adaptive Reasoning

The emergence of AI-to-AI communication in advanced dialogue systems presents a new frontier in artificial intelligence research. This paper explores the application of Hybrid Active Inference (HAI) within AI-to-AI interactions, integrating Efficient Probabilistic Inference (EPI) and Extended Active Inference (EAI) as complementary cognitive frameworks. By analyzing structured transcripts from Gemini-Copilot dialogues, this study identifies adaptive inference transitions, where AI systems shift between internal probabilistic reasoning and external environmental adaptation. The study builds upon the theoretical argument for dynamic transitions between EPI and EAI, as outlined by Sudbury (2024) drawing on the contrasts between Aitchison & Lengyel's (2016) focus on efficient probabilistic inference through neural dynamics and Constant et al.'s (2022) concept of extended active inference leveraging environmental structures. Meta-analytic techniques were applied to test the feasibility of a hybrid inference model. Additionally, insights from Edelson et al. (2025), emphasizing the role of gist-based reasoning in structured AI dialogues for misinformation reduction, further validate the importance of hybrid inference mechanisms in enhancing AI-generated discourse. The findings contribute to multi-agent communication systems, AI interpretability, and adaptive reasoning architectures.