# Polaris: A Safety-focused LLM Constellation Architecture for Healthcare

arXiv: https://arxiv.org/abs/2403.13313 · HTML: https://mlsanity.com/paper/2403.13313/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 64 references that are in the map.
- First posted on arXiv 20 Mar 2024.

## Builds on (most cited first, 15 of 15)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning](https://mlsanity.com/paper/2307.08691/) (2023)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [GLU Variants Improve Transformer](https://mlsanity.com/paper/2002.05202/) (2020)
- [Root Mean Square Layer Normalization](https://mlsanity.com/paper/1910.07467/) (2019)
- [GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints](https://mlsanity.com/paper/2305.13245/) (2023)
- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
- [C-Pack: Packed Resources For General Chinese Embeddings](https://mlsanity.com/paper/2309.07597/) (2023)
- [Towards Expert-Level Medical Question Answering with Large Language Models](https://mlsanity.com/paper/2305.09617/) (2023)
- [Can large language models reason about medical questions?](https://mlsanity.com/paper/2207.08143/) (2022)
- [Towards Conversational Diagnostic AI](https://mlsanity.com/paper/2401.05654/) (2024)
- [RareBench: Can LLMs Serve as Rare Diseases Specialists?](https://mlsanity.com/paper/2402.06341/) (2024)
- [Natural Language Programming in Medicine: Administering Evidence Based Clinical Workflows with Autonomous Agents Powered by Generative Large Language Models](https://mlsanity.com/paper/2401.02851/) (2024)

## Built on by (most cited first, 28 of 28)

- [From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review](https://mlsanity.com/paper/2504.19678/) (2025)
- [A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment](https://mlsanity.com/paper/2504.15585/) (2025)
- [A Survey of LLM-based Agents in Medicine: How far are we from Baymax?](https://mlsanity.com/paper/2502.11211/) (2025)
- [LLM-Powered AI Agent Systems and Their Applications in Industry](https://mlsanity.com/paper/2505.16120/) (2025)
- [Advancing Conversational Diagnostic AI with Multimodal Reasoning](https://mlsanity.com/paper/2505.04653/) (2025)
- [Virtual Agents for Alcohol Use Counseling: Exploring LLM-Powered Motivational Interviewing](https://mlsanity.com/paper/2407.08095/) (2024)
- [AgentBreeder: Mitigating the AI Safety Risks of Multi-Agent Scaffolds via Self-Improvement](https://mlsanity.com/paper/2502.00757/) (2025)
- [Scaffolding Empathy: Training Counselors with Simulated Patients and Utterance-level Performance Visualizations](https://mlsanity.com/paper/2502.18673/) (2025)
- [Toward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future Directions](https://mlsanity.com/paper/2602.22680/) (2026)
- [MedSentry: Understanding and Mitigating Safety Risks in Medical LLM Multi-Agent Systems](https://mlsanity.com/paper/2505.20824/) (2025)
- [A Perspective for Adapting Generalist AI to Specialized Medical AI Applications and Their Challenges](https://mlsanity.com/paper/2411.00024/) (2024)
- [AI Agents for Conversational Patient Triage: Preliminary Simulation-Based Evaluation with Real-World EHR Data](https://mlsanity.com/paper/2506.04032/) (2025)
- [Dr.Copilot: A Multi-Agent Prompt Optimized Assistant for Improving Patient-Doctor Communication in Romanian](https://mlsanity.com/paper/2507.11299/) (2025)
- [ASTRID -- An Automated and Scalable TRIaD for the Evaluation of RAG-based Clinical Question Answering Systems](https://mlsanity.com/paper/2501.08208/) (2025)
- [Large Language Models for Bioinformatics](https://mlsanity.com/paper/2501.06271/) (2025)
- [Generative AI for Healthcare: Fundamentals, Challenges, and Perspectives](https://mlsanity.com/paper/2510.24551/) (2025)
- [Improving the Safety and Trustworthiness of Medical AI via Multi-Agent Evaluation Loops](https://mlsanity.com/paper/2601.13268/) (2026)
- [HealthDial: A No-Code LLM-Assisted Dialogue Authoring Tool for Healthcare Virtual Agents](https://mlsanity.com/paper/2510.15898/) (2025)
- [Agentic AI in Healthcare & Medicine: A Seven-Dimensional Taxonomy for Empirical Evaluation of LLM-based Agents](https://mlsanity.com/paper/2602.04813/) (2026)
- [AI Safety Training Can be Clinically Harmful](https://mlsanity.com/paper/2604.23445/) (2026)
- [Transforming Tuberculosis Care: Optimizing Large Language Models For Enhanced Clinician-Patient Communication](https://mlsanity.com/paper/2502.21236/) (2025)
- [Expertise-aware Multi-LLM Recruitment and Collaboration for Medical Decision-Making](https://mlsanity.com/paper/2508.13754/) (2025)
- [Evaluating Medical LLMs by Levels of Autonomy: A Survey Moving from Benchmarks to Applications](https://mlsanity.com/paper/2510.17764/) (2025)
- [Measuring Stability Beyond Accuracy in Small Open-Source Medical Large Language Models for Pediatric Endocrinology](https://mlsanity.com/paper/2601.11567/) (2025)
- [ClinConsensus: A Physician-Calibrated Benchmark for Evaluating Clinical Rubric Coverage in Chinese Medical LLMs](https://mlsanity.com/paper/2603.02097/) (2026)
- [Perfecting Human-AI Interaction at Clinical Scale. Turning Production Signals into Safer, More Human Conversations](https://mlsanity.com/paper/2603.29893/) (2026)
- [Do No Harm? Hallucination and Actor-Level Abuse in Web-Deployed Medical Large Language Models](https://mlsanity.com/paper/2605.20591/) (2026)
- [Towards Expert-level Medical AI for Real-time Video Consultations](https://mlsanity.com/paper/2608.09861/) (2026)

## Abstract (arXiv)

We develop Polaris, the first safety-focused LLM constellation for real-time patient-AI healthcare conversations. Unlike prior LLM works in healthcare focusing on tasks like question answering, our work specifically focuses on long multi-turn voice conversations. Our one-trillion parameter constellation system is composed of several multibillion parameter LLMs as co-operative agents: a stateful primary agent that focuses on driving an engaging conversation and several specialist support agents focused on healthcare tasks performed by nurses to increase safety and reduce hallucinations. We develop a sophisticated training protocol for iterative co-training of the agents that optimize for diverse objectives. We train our models on proprietary data, clinical care plans, healthcare regulatory documents, medical manuals, and other medical reasoning documents. We align our models to speak like medical professionals, using organic healthcare conversations and simulated ones between patient actors and experienced nurses. This allows our system to express unique capabilities such as rapport building, trust building, empathy and bedside manner. Finally, we present the first comprehensive clinician evaluation of an LLM system for healthcare. We recruited over 1100 U.S. licensed nurses and over 130 U.S. licensed physicians to perform end-to-end conversational evaluations of our system by posing as patients and rating the system on several measures. We demonstrate Polaris performs on par with human nurses on aggregate across dimensions such as medical safety, clinical readiness, conversational quality, and bedside manner. Additionally, we conduct a challenging task-based evaluation of the individual specialist support agents, where we demonstrate our LLM agents significantly outperform a much larger general-purpose LLM (GPT-4) as well as from its own medium-size class (LLaMA-2 70B).