ARdena paper proposes scenario-driven runtime control for real-time LLM agents

Researchers describe ARdena, a real-time multimodal embodied agent framework that steers large language model behavior at runtime through layered, scenario-driven prompting rather than model fine-tuning, according to an arXiv preprint in artificial intelligence. The abstract says existing control methods often rely on fine-tuning or alignment that is hard to adapt when interaction requirements change. The proposed approach combines persistent context with scenario-specific constraints so agent behavior can be modified during interaction without changing the underlying model. ARdena integrates speech interaction, visual perception, tool use, and avatar-based response generation. The authors report evaluation on control effectiveness, response latency, and operational stability, and say scenario definitions alone produced substantially different interaction behaviors while maintaining stable real-time operation.