7 papers · 1 filter
Let it Cook: Learning to Wait in Sequential Decision Making
Christopher Watson, Arjun Krishna, Dinesh Jayaraman +1
In sequential decision making, an agent typically observes its environment and acts at every timestep. However, such active participation may not always be necessary; tasks such as…
Do We Need Frontier Models to Verify Mathematical Proofs?
Aaditya Naik, Guruprerana Shabadi, Rajeev Alur +1
Advances in training, post-training, and inference-time methods have enabled frontier reasoning models to win gold medals in math competitions and settle challenging open problems.…
CAMEL: An ECG Language Model for Forecasting Cardiac Events
Neelay Velingker, Alaia Solko-Breslin, Mayank Keoliya +9
Electrocardiograms (ECG) are electrical recordings of the heart that are critical for diagnosing cardiovascular conditions. ECG language models (ELMs) have recently emerged as a pr…
Stable Prediction of Adverse Events in Medical Time-Series Data
Mayank Keoliya, Seewon Choi, Rajeev Alur +2
Early event prediction (EEP) systems continuously estimate a patient's imminent risk to support clinical decision-making. For bedside trust, risk trajectories must be accurate and…
Risk-Sensitive Agent Compositions
Guruprerana Shabadi, Rajeev Alur
From software development to robot control, modern agentic systems decompose complex objectives into a sequence of subtasks and choose a set of specialized AI agents to complete th…
Scenario-based Compositional Verification of Autonomous Systems with Neural Perception
Christopher Watson, Rajeev Alur, Divya Gopinath +2
Recent advances in deep learning have enabled the development of autonomous systems that use deep neural networks for perception. Formal verification of these systems is challengin…
CTSketch: Compositional Tensor Sketching for Scalable Neurosymbolic Learning
Seewon Choi, Alaia Solko-Breslin, Rajeev Alur +1
Many computational tasks benefit from being formulated as the composition of neural networks followed by a discrete symbolic program. The goal of neurosymbolic learning is to train…