A Safety-Aware Role-Orchestrated Multi-Agent LLM Framework for Behavioral Health Communication Simulation

• URL: http://arxiv.org/abs/2604.00249v1
• Date: Tue, 31 Mar 2026 21:21:31 GMT
• Title: A Safety-Aware Role-Orchestrated Multi-Agent LLM Framework for Behavioral Health Communication Simulation
• Abstract summary: Single-agent large language model (LLM) systems struggle to simultaneously support diverse conversational functions and maintain safety in behavioral health communication.<n>We propose a safety-aware, role-orchestrated multi-agent LLM framework designed to simulate supportive behavioral health dialogue through coordinated, role-differentiated agents.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• EMERGE-Policy: A Robot Mind Emerges Beyond a Single Policy [48.898967774856466]
  EMERGE-Policy coordinates both capability invocation and information exchange.<n>Main Agent retains task-level state within an active context window.<n>Role-specific Sub Agents process perception, execution monitoring, verification, and memory consolidation.
  arXiv  Detail & Related papers  (2026-08-30T16:46:06Z)
• Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems [65.90791804095561]
  Multi-agent systems address this through structured collaboration among specialized agents, but tighter coordination amplifies a less explored risk.<n>Existing surveys cover individual agent capabilities, multi-agent collaboration, or agent self-evolution separately, leaving the causal dependencies among them unexamined.<n>This survey provides a unified review organized around four causally linked stages, which we term the LIFE progression: Lay the capability foundation, Integrate agents through collaboration, Find faults through attribution, and Evolve through autonomous self-improvement.
  arXiv  Detail & Related papers  (2026-05-14T14:36:13Z)
• CPEMH: An Agentic Framework for Prompt-Driven Behavior Evaluation and Assurance in Foundation-Model Systems for Mental Health Screening [0.326654570054705]
  CPEMH serves as an engineering methodology for behavioral assurance in large-scale language systems.<n>Its modular agentic design, combining orchestrator, inference, and evaluation agents, ensures traceability, robustness, and robustness throughout the prompting lifecycle.
  arXiv  Detail & Related papers  (2026-05-11T23:52:01Z)
• Cooperative Informative Sensing for Monitoring Dynamic Indoor Environments via Multi-Agent Reinforcement Learning [56.64821510576244]
  We formulate cooperative active observation as a decentralized control problem in which multiple robots adjust their motion to directly optimize monitoring accuracy under partial observability.<n>We propose a learning-based framework for cooperative policies from decentralized observations using multi-agent reinforcement learning (MARL), supported by an architecture that handles variable numbers of humans and temporal dependencies.
  arXiv  Detail & Related papers  (2026-04-25T07:20:15Z)
• MedSAM-Agent: Empowering Interactive Medical Image Segmentation with Multi-turn Agentic Reinforcement Learning [53.37068897861388]
  MedSAM-Agent is a framework that reformulates interactive segmentation as a multi-step autonomous decision-making process.<n>We develop a two-stage training pipeline that integrates multi-turn, end-to-end outcome verification.<n>Experiments across 6 medical modalities and 21 datasets demonstrate that MedSAM-Agent achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-02-03T09:47:49Z)
• Interpreting Agentic Systems: Beyond Model Explanations to System-Level Accountability [0.6745502291821954]
  Agentic systems have transformed how Large Language Models can be leveraged to create autonomous systems with goal-directed behaviors.<n>Current interpretability techniques, developed primarily for static models, show limitations when applied to agentic systems.<n>This paper assesses the suitability and limitations of existing interpretability methods in the context of agentic systems.
  arXiv  Detail & Related papers  (2026-01-23T21:05:32Z)
• The Why Behind the Action: Unveiling Internal Drivers via Agentic Attribution [63.61358761489141]
  Large Language Model (LLM)-based agents are widely used in real-world applications such as customer service, web navigation, and software engineering.<n>We propose a novel framework for textbfgeneral agentic attribution, designed to identify the internal factors driving agent actions regardless of the task outcome.<n>We validate our framework across a diverse suite of agentic scenarios, including standard tool use and subtle reliability risks like memory-induced bias.
  arXiv  Detail & Related papers  (2026-01-21T15:22:21Z)
• Agentic Reasoning for Large Language Models [122.81018455095999]
  Reasoning is a fundamental cognitive process underlying inference, problem-solving, and decision-making.<n>Large language models (LLMs) demonstrate strong reasoning capabilities in closed-world settings, but struggle in open-ended and dynamic environments.<n>Agentic reasoning marks a paradigm shift by reframing LLMs as autonomous agents that plan, act, and learn through continual interaction.
  arXiv  Detail & Related papers  (2026-01-18T18:58:23Z)
• InterAgent: Physics-based Multi-agent Command Execution via Diffusion on Interaction Graphs [72.5651722107621]
  InterAgent is an end-to-end framework for text-driven physics-based multi-agent humanoid control.<n>We introduce an autoregressive diffusion transformer equipped with multi-stream blocks, which decouples proprioception, exteroception, and action to cross-modal interference.<n>We also propose a novel interaction graph exteroception representation that explicitly captures fine-grained joint-to-joint spatial dependencies.
  arXiv  Detail & Related papers  (2025-12-08T10:46:01Z)
• Formalizing the Safety, Security, and Functional Properties of Agentic AI Systems [10.734711935895225]
  We introduce a modeling framework for agentic AI systems composed of two foundational models.<n>The first, the host agent model, formalizes the top-level entity that interacts with the user, decomposes tasks, and orchestrates their execution by leveraging external agents and tools.<n>The second, the task lifecycle model, details the states and transitions of individual sub-tasks from creation to completion, providing a fine-grained view of task management and error handling.
  arXiv  Detail & Related papers  (2025-10-15T22:02:30Z)
• Agentic Services Computing [51.50424046053763]
  We propose Agentic Services Computing, a paradigm that reimagines services as autonomous, adaptive, and collaborative agents.<n>We organize ASC around a four-phase lifecycle: Design, Deployment, Operation, and Evolution.
  arXiv  Detail & Related papers  (2025-09-29T07:29:18Z)
• TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems [8.683314804719506]
  This review presents a structured analysis of Trust, Risk, and Security Management (TRiSM) in the context of Agentic Multi-Agent Systems (AMAS)<n>We begin by examining the conceptual foundations of Agentic AI and highlight its architectural distinctions from traditional AI agents.<n>We then adapt and extend the AI TRiSM framework for Agentic AI, structured around key pillars: textit Explainability, ModelOps, Security, Privacy and textittheir lifecycle governance<n>A risk taxonomy is proposed to capture the unique threats and vulnerabilities of Agentic AI, ranging from coordination failures to
  arXiv  Detail & Related papers  (2025-06-04T16:26:11Z)
• Modular Speaker Architecture: A Framework for Sustaining Responsibility and Contextual Integrity in Multi-Agent AI Communication [0.0]
  Sustaining coherent, role-aware communication across multi-agent systems remains a foundational challenge in AI.<n>We propose the Modular Speaker Architecture (MSA), a framework that decomposes speaker behavior into modular components for role tracking, responsibility continuity, and contextual coherence.
  arXiv  Detail & Related papers  (2025-06-01T17:39:51Z)
• Learning responsibility allocations for multi-agent interactions: A differentiable optimization approach with control barrier functions [12.074590482085831]
  We seek to codify factors governing safe multi-agent interactions via the lens of responsibility. We propose a data-driven modeling approach based on control barrier functions and differentiable optimization.
  arXiv  Detail & Related papers  (2024-10-09T20:20:41Z)
• AntEval: Evaluation of Social Interaction Competencies in LLM-Driven Agents [65.16893197330589]
  Large Language Models (LLMs) have demonstrated their ability to replicate human behaviors across a wide range of scenarios. However, their capability in handling complex, multi-character social interactions has yet to be fully explored. We introduce the Multi-Agent Interaction Evaluation Framework (AntEval), encompassing a novel interaction framework and evaluation methods.
  arXiv  Detail & Related papers  (2024-01-12T11:18:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.