ARdena Framework Enables Real-Time Control of LLM Agents
A new framework called ARdena introduces layered scenario-driven control for large language model (LLM) agents, allowing runtime behavior modification through structured prompting. Developed by researchers and detailed in arXiv paper 2607.22651, ARdena combines persistent context with scenario-specific constraints to adjust agent behavior during interaction without retraining the underlying model. The system is implemented as a real-time multimodal embodied agent integrating speech, visual perception, tool use, and avatar-based response generation. Evaluation focuses on control effectiveness, response latency, and operational stability, addressing the challenge of reliably controlling LLM agents in dynamic interactive environments.
Key facts
- arXiv paper 2607.22651 introduces ARdena framework
- ARdena enables runtime behavior control through structured prompting
- Framework combines persistent context with scenario-specific constraints
- No model fine-tuning or alignment procedures required
- ARdena is a real-time multimodal embodied agent
- Integrates speech interaction, visual perception, tool use, and avatar-based response generation
- Evaluated on control effectiveness, response latency, and operational stability
- Addresses challenge of controlling LLM agents in real-time interactive environments
Entities
Institutions
- arXiv