arXiv subjects

Gregory B. Rehm

Publications and source records attributed to Gregory B. Rehm.

3 recordsLinked to original sources

arXiv2026

From Certain Doom to Survival: Agent-Driven Self-Governance in LLM Agent Societies

Multi-agent LLM systems are increasingly evaluated in social dilemmas, but most work treats governance as imposed by the experimenter, expressed rhetorically, or restricted to a fixed menu of mechanisms. We introduce GovSim-SelfGovern, an extension of the GovSim common-pool resource environment in which agents author executable Python governance rules, receive sandbox validation feedback, vote on proposed laws, and live under the rules they enact across rounds. To evaluate agent-driven self-governance, we examine three scenarios ranging from stable abundance to a fatal resource wall where five agents cannot all survive through harvest alone. To solve this, agents must write and debug useful laws in time before their institutions degrade sharply under resource pressure. Finally, we study a central alignment question: when agents hesitate to propose exile, are they rejecting it for normative reasons, or does it never enter their candidate set? Our results show that executable governance improves the space of possible interventions for agents, but survival depends on whether agents discover the right institutional mechanisms in time. Fiscal capacity enables redistribution, while deeper reasoning and removal of democratic veto make exile more feasible. GovSim-SelfGovern therefore adapts executable code actions to a common-pool governance setting and shows how scarcity turns institutional authorship into a political and ethical problem.

cs.MA↗

arXiv2021

Deep Learning-Based Detection of the Acute Respiratory Distress Syndrome: What Are the Models Learning?

The acute respiratory distress syndrome (ARDS) is a severe form of hypoxemic respiratory failure with in-hospital mortality of 35-46%. High mortality is thought to be related in part to challenges in making a prompt diagnosis, which may in turn delay implementation of evidence-based therapies. A deep neural network (DNN) algorithm utilizing unbiased ventilator waveform data (VWD) may help to improve screening for ARDS. We first show that a convolutional neural network-based ARDS detection model can outperform prior work with random forest models in AUC (0.95+/-0.019 vs. 0.88+/-0.064), accuracy (0.84+/-0.026 vs 0.80+/-0.078), and specificity (0.81+/-0.06 vs 0.71+/-0.089). Frequency ablation studies imply that our model can learn features from low frequency domains typically used for expert feature engineering, and high-frequency information that may be difficult to manually featurize. Further experiments suggest that subtle, high-frequency components of physiologic signals may explain the superior performance of DL models over traditional ML when using physiologic waveform data. Our observations may enable improved interpretability of DL-based physiologic models and may improve the understanding of how high-frequency information in physiologic data impacts the performance our DL model.

cs.LG↗

arXiv2019

Improving Mechanical Ventilator Clinical Decision Support Systems with A Machine Learning Classifier for Determining Ventilator Mode

Clinical decision support systems (CDSS) will play an in-creasing role in improving the quality of medical care for critically ill patients. However, due to limitations in current informatics infrastructure, CDSS do not always have com-plete information on state of supporting physiologic monitor-ing devices, which can limit the input data available to CDSS. This is especially true in the use case of mechanical ventilation (MV), where current CDSS have no knowledge of critical ventilation settings, such as ventilation mode. To enable MV CDSS to make accurate recommendations related to ventilator mode, we developed a highly performant ma-chine learning model that is able to perform per-breath clas-sification of 5 of the most widely used ventilation modes in the USA with an average F1-score of 97.52%. We also show how our approach makes methodologic improvements over previous work and that it is highly robust to missing data caused by software/sensor error.

cs.LG↗