A Safety-Aware Role-Orchestrated Multi-Agent LLM Framework for Behavioral Health Communication Simulation
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. Conversational responsibilities are decomposed across specialized agents, including empathy-focused, action-oriented, and supervisory roles, while a prompt-based controller dynamically activates relevant agents and enforces continuous safety auditing. Using semi-structured interview transcripts from the DAIC-WOZ corpus, we evaluate the framework with scalable proxy metrics capturing structural quality, functional diversity, and computational characteristics. Results illustrate clear role differentiation, coherent inter-agent coordination, and predictable trade-offs between modular orchestratio
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. Conversational responsibilities are decomposed across specialized agents, including empathy-focused, action-oriented, and supervisory roles, while a prompt-based controller dynamically activates relevant agents and enforces continuous safety auditing. Using semi-structured interview transcripts from the DAIC-WOZ corpus, we evaluate the framework with scalable proxy metrics capturing structural quality, functional diversity, and computational characteristics. Results illustrate clear role differentiation, coherent inter-agent coordination, and predictable trade-offs between modular orchestration, safety oversight, and response latency when compared to a single-agent baseline. This work emphasizes system design, interpretability, and safety, positioning the framework as a simulation and analysis tool for behavioral health informatics and decision-support research rather than a clinical intervention.
Executive Summary
This article proposes a safety-aware, role-orchestrated multi-agent LLM framework designed to simulate supportive behavioral health dialogue. The framework decomposes conversational responsibilities across specialized agents, including empathy-focused, action-oriented, and supervisory roles, while a prompt-based controller dynamically activates relevant agents and enforces continuous safety auditing. The authors evaluate the framework using semi-structured interview transcripts from the DAIC-WOZ corpus, demonstrating clear role differentiation, coherent inter-agent coordination, and predictable trade-offs between modular orchestration, safety oversight, and response latency. This work emphasizes system design, interpretability, and safety, positioning the framework as a simulation and analysis tool for behavioral health informatics and decision-support research.
Key Points
- ▸ The framework proposes a safety-aware, role-orchestrated multi-agent LLM approach for behavioral health communication simulation.
- ▸ Conversational responsibilities are decomposed across specialized agents, including empathy-focused, action-oriented, and supervisory roles.
- ▸ The framework uses a prompt-based controller to dynamically activate relevant agents and enforce continuous safety auditing.
Merits
Novel Approach to Behavioral Health Communication
The proposed framework offers a unique solution to the challenges of single-agent LLM systems in behavioral health communication, emphasizing system design, interpretability, and safety.
Interpretability and Safety Features
The framework's safety-aware design and prompt-based controller enable continuous auditing and safety enforcement, addressing concerns related to LLM safety and reliability.
Demerits
Limited Evaluation Dataset
The authors' evaluation using semi-structured interview transcripts from the DAIC-WOZ corpus may not be representative of real-world behavioral health communication scenarios, limiting the framework's generalizability.
Scalability and Computational Complexity
The framework's performance and scalability may be impacted by the increased complexity of multi-agent interactions, potentially leading to higher computational requirements and reduced response latency.
Expert Commentary
The proposed framework is a significant contribution to the field of behavioral health communication, offering a novel approach to addressing the challenges of single-agent LLM systems. However, the authors' reliance on a limited evaluation dataset and the potential scalability concerns of the framework's multi-agent design merit further investigation. As the field continues to evolve, the development of more robust and reliable LLMs will be essential for high-stakes applications like behavioral health communication.
Recommendations
- ✓ Future research should focus on expanding the evaluation dataset to better represent real-world behavioral health communication scenarios and exploring methods to mitigate scalability concerns.
- ✓ Developers should prioritize the deployment of safety-aware LLMs in behavioral health applications, emphasizing the importance of continuous auditing and safety enforcement.
Sources
Original: arXiv - cs.AI
