mlsanityData to 14 Oct 2026

Human-in-the-Loop Multi-Agent Ventilator Decision Support with Contextual Bandit Preference Learning

arXiv 2605.23320 ↗First posted 22 May 2026cs.AIcited by 0

On the map: LLM agents and tool use → Medical and scientific domain agents

Abstract (arXiv)

Ventilator decision support requires sequential decisions that track evolving physiology and disease trajectories while respecting safety boundaries and clinician specific tuning styles. Rule based approaches rarely generalize personalization, and end to end reinforcement learning or single large language model systems remain difficult to control and audit. We propose the Ventilator Decision Support System (VDSS), a human in the loop multi agent framework that coordinates modular decision components through contract driven structured interfaces and produces traceable evidence for review. VDSS performs online preference adaptation with a contextual bandit, updating clinician specific preferences from the final accepted decision at each adjustment cycle and using them to guide subsequent recommendations. Structured rejection feedback triggers targeted replanning to reduce unproductive iterations and improve interaction stability. Retrospective ICU trajectory replay with expert review indicates higher recommendation acceptability and fewer interaction rounds to reach an acceptable plan, supporting clinically deployable human AI collaboration.

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 4 of its 27 references that are in the map. First posted on arXiv 22 May 2026.

Builds on

4 of 27 references.

References · 4

  1. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
  2. 2023MemGPT: Towards LLMs as Operating Systemscited by 1,389
  3. 2017An Actor-Critic Contextual Bandit Algorithm for Personalized Mobile Health Interventionscited by 17
  4. 2024Contract-based Design and Verification of Multi-Agent Systems with Quantitative Temporal Requirementscited by 1

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  1. 2024Methodology for Interpretable Reinforcement Learning for Optimizing Mechanical Ventilation
  2. 2026VentAgent: When LLMs Learn to Breathe -- Multi-Objective Arbitration for ARDS Ventilation
  3. 2022Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learning
  4. 2026Ensuring Safety in Automated Mechanical Ventilation through Offline Reinforcement Learning and Digital Twin Verification
  5. 2025Inferring Relative Consequences of Mechanical Ventilation from Observational Data Using Game-Based Comparisons
  6. 2026Agentifying Patient Dynamics within LLMs through Interacting with Clinical World Model
  7. 2025Mediator-Guided Multi-Agent Collaboration among Open-Source Models for Medical Decision-Making
  8. 2024Advancing Multi-Organ Disease Care: A Hierarchical Multi-Agent Reinforcement Learning Framework