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

2026-04-20

AI Summary

Existing AI alignment frameworks predominantly focus on dyadic interactions between users and AI systems, rendering them inadequate for dynamic alignment demands in real-time, multi-party social contexts such as live streaming. This work proposes a “triadic loop” alignment model that conceptualizes AI co-broadcasting as an ongoing, bidirectional adaptation process among streamers, AI agents, and viewers, emphasizing mutual reinforcement and co-construction of social meaning across roles. The study innovatively introduces a “strategic misalignment” mechanism to stimulate community engagement and develops a tripartite relational evaluation framework grounded in established psychometric scales. Integrating theories of multi-party interaction, collaborative AI, and relational agency, this research establishes the first dynamic alignment modeling and assessment framework tailored to multi-role, real-time interaction, offering design principles for AI co-broadcasting that support social coherence in participatory media and providing a scalable alignment paradigm for complex social scenarios.

Technology Category

• Humans and AI: Human-AI Collaboration / Human-AI Teaming
• Multiagent Systems: Agent Communication
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Search and Retrieval-Augmented AI: Agentic search

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.

Problem

• AI alignment
• multi-party interaction
• livestreaming
• relational agents
• social coherence

Innovation

• Triadic Loop
• multi-party alignment
• AI co-hosting
• strategic misalignment
• relational evaluation