ATHENA-R1 is an RL-trained agent using 212 biomedical tools that achieves 94.7% accuracy on drug reasoning and 82.9% on treatment reasoning tasks, outperforming GPT-5 by 17.8 and 10.7 points respectively.
Multi-ethnic study of atherosclerosis: Objectives and design
6 Pith papers cite this work, alongside 418 external citations. Polarity classification is still indexing.
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ConvRt estimates epidemic Rt via successive deconvolution and penalized-likelihood spline fitting, claiming better speed, accuracy, and narrative transparency than Bayesian methods in simulations.
Proposes compatibility scores for bivariate causal statements that quantify plausibility via the confounding implied by the induced multivariate model, plus an incompatibility score based on acyclicity and faithfulness constraints.
ATune combines Gaussian theoretical analysis with burn-in simulation data to select system-specific splitting integrators and hyperparameter credible intervals for improved HMC stability and performance.
A case study develops a sparse dictionary learning approach to model pediatric asthma exacerbations from multiple risk factors and reports consensus on relative risks across statistical and machine learning models.
A review synthesizes evidence from EEG, EMG, ECG, PPG and ocular signals to argue that waveform morphology, rather than modality or model class, primarily determines TSC performance and interpretability.
citing papers explorer
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An AI agent for treatment reasoning over a biomedical tool universe
ATHENA-R1 is an RL-trained agent using 212 biomedical tools that achieves 94.7% accuracy on drug reasoning and 82.9% on treatment reasoning tasks, outperforming GPT-5 by 17.8 and 10.7 points respectively.
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Fast, Frequentist Estimation of Epidemic Reproduction Numbers
ConvRt estimates epidemic Rt via successive deconvolution and penalized-likelihood spline fitting, claiming better speed, accuracy, and narrative transparency than Bayesian methods in simulations.
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Evaluating Bivariate Causal Statements Based on Mutual Compatibility
Proposes compatibility scores for bivariate causal statements that quantify plausibility via the confounding implied by the induced multivariate model, plus an incompatibility score based on acyclicity and faithfulness constraints.
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Adaptive tuning of Hamiltonian Monte Carlo methods
ATune combines Gaussian theoretical analysis with burn-in simulation data to select system-specific splitting integrators and hyperparameter credible intervals for improved HMC stability and performance.
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Learning to model pediatric asthma exacerbation from multiple risk factors: a case study in coastal Virginia
A case study develops a sparse dictionary learning approach to model pediatric asthma exacerbations from multiple risk factors and reports consensus on relative risks across statistical and machine learning models.
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Modality vs. Morphology: A Framework for Time Series Classification for Biological Signals
A review synthesizes evidence from EEG, EMG, ECG, PPG and ocular signals to argue that waveform morphology, rather than modality or model class, primarily determines TSC performance and interpretability.