Train the Trainers -- An Agentic AI Framework for Peer-Based Mental Health Support in Battlefield Environments
Authors: Atmaram Yarlagadda, Eranga Bandara, Ross Gore, Anita H. Clayton, Preston Samuel, Christopher K. Rhea, Sachin Shetty, Ravi Mukkamala, +3 more
Organizations: McDonald Army Health Center, Newport News, VA, USA · Old Dominion University, Norfolk, VA, USA · Department of Psychiatry and Neurobehavioral Sciences, University of Virginia School of Medicine, Charlottesville, VA, USA · Blanchfield Army Community Hospital, Fort Campbell, KY, USA · Florida International University, FL, USA · Accenture Technology Labs, Arlington, VA, USA · Deloitte & Touche LLP, USA
Modern military operations expose soldiers to sustained psychological stress, leading to acute reactions, post-traumatic stress symptoms, and other mental health issues. Although the U.S. Department of Defense offers evidence-based therapies, access to trained professionals in forward-deployed and contested environments is limited. As a result, soldiers with early-stage distress are often evacuated to rear medical facilities, delaying care, reducing readiness, and increasing long-term risks. This paper proposes a Train-the-Trainers framework in which soldiers who have completed therapy and returned to duty are trained as peer facilitators to provide first-line psychological support in operational settings. To scale and standardize this model under severe resource and connectivity constraints, we introduce an agentic AI-enabled platform that augments these recovered soldiers with specialized AI agents. The recovered soldier acts as a human supervisor, coordinating agents for symptom triage, guided peer-support interventions, operational constraint reasoning, training and simulation, and structured documentation for clinical escalation when needed. The AI agents use consensus-driven decision support in high-stakes environments. The architecture functions in air-gapped and low-connectivity settings, maintaining human oversight and ethical safeguards. A functional prototype was developed with the McDonald U.S. Army Health Center, Newport News, VA, USA. By combining peer-based intervention with consensus-driven agentic AI decision support, the framework seeks to cut response times, prevent symptom escalation, reduce unnecessary evacuations, and improve continuity of care. This work shows how agentic AI can serve as a force multiplier for mental health support in austere environments and identifies pathways for broader evaluation and deployment across defense and humanitarian operations.
Figures & tables
Figure 1: Comparison between direct human–LLM interaction and agentic AI–LLM interaction. In the top panel, a human user directly constructs and submits a prompt to a language model, manually interpreting the response. In the bottom panel, an AI agent autonomously manages the interaction loop by constructing prompts, invoking the language model, interpreting responses, and determining subsequent actions without direct human intervention. This agent-mediated workflow enables iterative reasoning, tool integration, and task automation, forming the basis of agentic AI systems used for structured decision support and multi-step workflows.
Figure 2: Human-supervised agentic AI architecture for the Train-the-Trainers framework. A recovered soldier acting as a peer trainer serves as the central human supervisor, coordinating multiple specialized AI agents that provide advisory decision support for assessment, peer intervention guidance, operational constraint reasoning, escalation and referral, documentation and after-action reporting, and training and simulation. All agent interactions are advisory in nature, with authority and execution remaining strictly under human control.
Figure 3: Overview of the supervised fine-tuning pipeline used to adapt a base LLM to domain-specific tasks. A curated, domain-specific dataset is used to perform supervised fine-tuning of a base LLM, with Low-Rank Adapter (LoRA) modules applied to enable parameter-efficient training. The resulting fine-tuned model retains the general linguistic capabilities of the base model while incorporating domain-specific knowledge, supporting efficient deployment and updates in resource-constrained and air-gapped environments.
Figure 4: Consensus-driven LLM consortium and reasoning workflow used by the agentic AI platform. An LLM agent generates a task-specific prompt, which is forwarded via the agent orchestration layer to multiple fine-tuned LLMs (e.g., Llama-3, Mistral, and Qwen). Each model independently generates a response based on the same prompt and contextual inputs. The resulting outputs are collected and processed by the orchestration layer, which constructs a structured reasoning prompt for a dedicated reasoning-oriented LLM (GPT-OSS). The reasoning LLM evaluates, reconciles, and synthesizes the multiple model outputs to produce a final consolidated response accompanied by an interpretable rationale. This consensus-driven process improves robustness, mitigates single-model bias, and supports explainable, human-supervised decision support.
Figure 5: Structure of the psychiatric assessment fine-tuning dataset. Each sample contains an instruction prompt, a psychiatrist-patient conversation (2.07k–5.07k characters), a DSM-5 diagnosis label, and a condition category. The dataset was constructed from publicly available data and synthetically generated conversations covering five psychiatric conditions.
Table 2: Fine-Tuning Configuration for Psychiatric Assessment Model
Figure 6: Training metrics during fine-tuning: learning rate schedule (left) showing warmup to 1×10−4 followed by linear decay, and training loss (right) decreasing from 1.9 to 0.90 over 180 steps, indicating effective learning of the diagnostic classification task.
Figure 7: Validation metrics during fine-tuning: evaluation loss (left) decreasing from 1.8 to 1.195, closely following the training loss trajectory and indicating good generalization without overfitting, and evaluation runtime (right) stabilizing at approximately 3.91 seconds.
Figure 8: Training progression metrics: epoch count (left) showing linear progression through approximately 3.6 epochs, and gradient norm (right) increasing gradually from 0.28 to 0.71, indicating stable optimization without gradient explosion.
Metric
Value
Training Duration
494.24 seconds (8.24 minutes)
Hardware
NVIDIA A100-SXM4-40GB
GPU Maximum Memory
39.557 GB
Initial Memory Reserved
6.725 GB
Peak Memory Reserved
7.789 GB
Memory for LoRA Training
1.064 GB
Table 3: Training Resource Utilization for Psychiatric Assessment Model
Figure 9: Computational efficiency metrics: total floating-point operations (left) reaching 5.65×1016 FLOPs, and cumulative training loss (right) at 1.203, demonstrating the computational cost of parameter-efficient fine-tuning.
Figure 10: Inference performance metrics during validation: samples per second (left) stabilizing at approximately 12.78, and steps per second (right) at approximately 6.39, indicating consistent evaluation throughput throughout training.
Condition
Correct
Total
Accuracy
Generalized Anxiety Disorder
13
13
100.0%
Bipolar I Disorder
14
14
100.0%
Panic Disorder
8
8
100.0%
Major Depressive Disorder
5
8
62.5%
Post-Traumatic Stress Disorder
4
7
57.1%
Overall
44
50
88.0%
Table 4: Assessment Label Alignment Accuracy by Condition
Figure 11: Representative assessment output of the fine-tuned Llama-3 LLM aligned with structured reference labels (DSM-5 296.21).
Figure 12: Representative assessment output of the fine-tuned Mistral LLM aligned with structured reference labels (DSM-5 300.01).
Figure 13: Representative assessment output of the fine-tuned Qwen2 LLM aligned with structured reference labels (DSM-5 295.90).
Figure 14: Consensus-driven assessment synthesis produced by the reasoning-oriented LLM through multi-model evaluation and reconciliation.
Old Dominion University, Norfolk, VA, USA · AI Motion Labs, Melbourne, Australia · Department of Psychiatry and Neurobehavioral Sciences, University of Virginia School of Medicine, Charlottesville, VA, USA +3