A Safety-Aware Role-Orchestrated Multi-Agent LLM Framework for Behavioral Health Communication Simulation
Summary: arXiv:2604.00249v1 Announce Type: new
Abstract: Single-agent large language model (LLM) systems struggle to simultaneously support diverse conversational functions and maintain safety in behavioral health communication. We propose a safety-aware, role-orchestrated multi-agent LLM framework designed to simulate supportive behavioral health dialogue through coordinated, role-differentiated agents.
The growing demand for effective behavioral health communication has highlighted the limitations of traditional single-agent LLM systems. These systems often fail to balance the complexity of various conversational functions while ensuring the safety of users during sensitive interactions. In response to these challenges, we present our innovative multi-agent framework that enhances the simulation of supportive dialogues.
- Role Differentiation: Our framework decomposes conversational responsibilities across specialized agents. Each agent is designed with a specific focus, including:
- Empathy-focused agents that prioritize emotional support.
- Action-oriented agents that provide actionable recommendations.
- Supervisory agents that oversee interactions to ensure safety.
- Dynamic Activation: A prompt-based controller is utilized to activate relevant agents based on the context of the conversation. This ensures that the most appropriate responses are generated, enhancing the overall quality of the interaction.
- Continuous Safety Auditing: The framework is designed to enforce continuous safety measures, allowing for real-time monitoring of interactions and immediate corrective actions if necessary.
To evaluate the effectiveness of our multi-agent framework, we conducted an analysis using semi-structured interview transcripts from the DAIC-WOZ corpus. This evaluation focused on scalable proxy metrics that captured:
- Structural Quality: Assessing the coherence and cohesiveness of the dialogue produced by the multi-agent system.
- Functional Diversity: Measuring the range of conversational functions supported by the agents.
- Computational Characteristics: Analyzing the responsiveness and efficiency of the framework compared to traditional single-agent systems.
The results of our evaluation indicate significant improvements in role differentiation and inter-agent coordination. Notably, the framework demonstrated predictable trade-offs between modular orchestration, safety oversight, and response latency when benchmarked against a single-agent baseline.
This research underscores the importance of system design, interpretability, and safety in behavioral health informatics. By positioning our framework as a simulation and analysis tool, we aim to contribute to decision-support research rather than clinical interventions. The findings suggest a promising direction for future advancements in the use of AI for behavioral health communication.