The Triadic Loop: A Framework for Negotiating Alignment in AI Co-hosted Livestreaming

TL;DR

This paper introduces the Triadic Loop, a framework for understanding and designing AI co-hosts in livestreaming that emphasizes multi-party, dynamic, and reciprocal alignment among streamers, AI, and audiences.

Contribution

It presents a novel triadic framework for AI alignment in multi-user social environments, extending beyond dyadic models to include community and relational dynamics.

Findings

Proposes the Triadic Loop as a model for multi-party alignment.

Highlights the importance of bidirectional adaptation among actors.

Introduces 'strategic misalignment' to sustain engagement.

Abstract

AI systems are increasingly embedded in multi-user social environments, yet most alignment frameworks conceptualize interaction as a dyadic relationship between a single user and an AI system. Livestreaming platforms challenge this assumption: interaction unfolds among streamers and audiences in real time, producing dynamic affective and social feedback loops. In this paper, we introduce the Triadic Loop, a conceptual framework that reconceptualizes alignment in AI co-hosted livestreaming as a temporally reinforced process of bidirectional adaptation among three actors: streamer $\leftrightarrow$ AI co-host, AI co-host $\leftrightarrow$ audience, and streamer $\leftrightarrow$ audience. Unlike instruction-following paradigms, bidirectional alignment requires each actor to continuously reshape the others, meaning misalignment in any sub-loop can destabilize the broader system. Drawing on literature from multi-party interaction, collaborative AI, and relational agents, we articulate how AI co-hosts function not only as mediators but as performative participants and community members shaping collective meaning-making. We further propose "strategic misalignment" as a mechanism for sustaining community engagement and introduce three relational evaluation constructs grounded in established instruments. The framework contributes a model of dynamic multi-party alignment, an account of cross-loop reinforcement, and design implications for AI co-hosts that sustain social coherence in participatory media environments.