# Train the Trainers -- An Agentic AI Framework for Peer-Based Mental Health Support in Battlefield Environments arXiv: https://arxiv.org/abs/2605.16269 ยท HTML: https://mlsanity.com/paper/2605.16269/ ## Key facts - No citing papers in the map yet (data through 14 Oct 2026). - Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use. - Builds on 29 of its 60 references that are in the map. - First posted on arXiv 31 Mar 2026. ## Placed in - LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/) ## Builds on (most cited first, 29 of 29) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024) - [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024) - [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023) - [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022) - [LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://mlsanity.com/paper/2403.13372/) (2024) - [gpt-oss-120b & gpt-oss-20b Model Card](https://mlsanity.com/paper/2508.10925/) (2025) - [Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions](https://mlsanity.com/paper/2503.23278/) (2025) - [Survey on Evaluation of LLM-based Agents](https://mlsanity.com/paper/2503.16416/) (2025) - [Pixtral 12B](https://mlsanity.com/paper/2410.07073/) (2024) - [Towards Accurate Differential Diagnosis with Large Language Models](https://mlsanity.com/paper/2312.00164/) (2023) - [Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes](https://mlsanity.com/paper/2304.09433/) (2023) - [Data-efficient Fine-tuning for LLM-based Recommendation](https://mlsanity.com/paper/2401.17197/) (2024) - [LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models](https://mlsanity.com/paper/2404.01230/) (2024) - [Agentic AI for Scientific Discovery: A Survey of Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2503.08979/) (2025) - [Model Context Protocol (MCP) at First Glance: Studying the Security and Maintainability of MCP Servers](https://mlsanity.com/paper/2506.13538/) (2025) - [A Tutorial on LLM Reasoning: Relevant Methods behind ChatGPT o1](https://mlsanity.com/paper/2502.10867/) (2025) - [DrHouse: An LLM-empowered Diagnostic Reasoning System through Harnessing Outcomes from Sensor Data and Expert Knowledge](https://mlsanity.com/paper/2405.12541/) (2024) - [MDD-5k: A New Diagnostic Conversation Dataset for Mental Disorders Synthesized via Neuro-Symbolic LLM Agents](https://mlsanity.com/paper/2408.12142/) (2024) - [A Practical Guide for Designing, Developing, and Deploying Production-Grade Agentic AI Workflows](https://mlsanity.com/paper/2512.08769/) (2025) - [Agentsway -- Software Development Methodology for AI Agents-based Teams](https://mlsanity.com/paper/2510.23664/) (2025) - [Towards Responsible and Explainable AI Agents with Consensus-Driven Reasoning](https://mlsanity.com/paper/2512.21699/) (2025) - [Standardization of Psychiatric Diagnoses -- Role of Fine-tuned LLM Consortium and OpenAI-gpt-oss Reasoning LLM Enabled Decision Support System](https://mlsanity.com/paper/2510.25588/) (2025) - [Proof-of-TBI -- Fine-Tuned Vision Language Model Consortium and OpenAI-o3 Reasoning LLM-Based Medical Diagnosis Support System for Mild Traumatic Brain Injury (TBI) Prediction](https://mlsanity.com/paper/2504.18671/) (2025) - [LoHan: Low-Cost High-Performance Framework to Fine-Tune 100B Model on a Consumer GPU](https://mlsanity.com/paper/2403.06504/) (2024) - [Model Context Contracts - MCP-Enabled Framework to Integrate LLMs With Blockchain Smart Contracts](https://mlsanity.com/paper/2510.19856/) (2025) - [Large Language Models for Interpretable Mental Health Diagnosis](https://mlsanity.com/paper/2501.07653/) (2025) - [On-Device Qwen2.5: Efficient LLM Inference with Model Compression and Hardware Acceleration](https://mlsanity.com/paper/2504.17376/) (2025) - [Advancements in Machine Learning and Deep Learning for Early Detection and Management of Mental Health Disorder](https://mlsanity.com/paper/2412.06147/) (2024) ## Abstract (arXiv) 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.