Agentic-imodels evolves scikit-learn regressors via an autoresearch loop to jointly boost predictive performance and LLM-simulatability, improving downstream agentic data science tasks by up to 73% on the BLADE benchmark.
An evaluation of the human-interpretability of explanation
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Pilot study uses pretrained video encoder features from lung ultrasound to predict 30-day CHF readmission, finding lower-lung views and temporal differences most informative with top MLP F1 of 0.80.
Cognitive models of user reasoning strategies with XAI methods on tabular data fit human forward-simulation decisions better than ML baselines and support hypothesis testing without new user studies.
RL post-trained models show stronger awareness of learned policies and better generalization to new tasks than SFT models, but display weaker alignment between internal reasoning traces and final outputs, especially under GRPO.
Human-grounded evaluation finds no significant performance improvement from adding SHAP explanations to model confidence scores in alert processing.
citing papers explorer
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Agentic-imodels: Evolving agentic interpretability tools via autoresearch
Agentic-imodels evolves scikit-learn regressors via an autoresearch loop to jointly boost predictive performance and LLM-simulatability, improving downstream agentic data science tasks by up to 73% on the BLADE benchmark.
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Prognostic Value of Lung Ultrasound Biomarkers for Readmission Risk in Congestive Heart Failure: A Pilot Data-Driven Analysis
Pilot study uses pretrained video encoder features from lung ultrasound to predict 30-day CHF readmission, finding lower-lung views and temporal differences most informative with top MLP F1 of 0.80.
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CoAX: Cognitive-Oriented Attribution eXplanation User Model of Human Understanding of AI Explanations
Cognitive models of user reasoning strategies with XAI methods on tabular data fit human forward-simulation decisions better than ML baselines and support hypothesis testing without new user studies.
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Thinking About Thinking: Evaluating Reasoning in Post-Trained Language Models
RL post-trained models show stronger awareness of learned policies and better generalization to new tasks than SFT models, but display weaker alignment between internal reasoning traces and final outputs, especially under GRPO.
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A Human-Grounded Evaluation of SHAP for Alert Processing
Human-grounded evaluation finds no significant performance improvement from adding SHAP explanations to model confidence scores in alert processing.