11 papers
Temporal Specification Guidance for Text-to-Video Generation
Avishree Khare, Hideki Okamoto, Bardh Hoxha +2
Text-to-video models often generate the visual concepts requested in natural language prompts but fail to satisfy their intended ordering, timing, and persistence requirements. We…
Learning Strategies to Break Judges
Guruprerana Shabadi, Aaditya Naik, Rajeev Alur +1
As AI agents surpass human performance, it becomes exceedingly hard for system designers to evaluate them directly and understand their failure modes. Consequently, agents themselv…
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…
Report on NSF Workshop on Science of Safe AI
Rajeev Alur, Greg Durrett, Hadas Kress-Gazit +2
Recent advances in machine learning, particularly the emergence of foundation models, are leading to new opportunities to develop technology-based solutions to societal problems. H…
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…
Logicbreaks: A Framework for Understanding Subversion of Rule-based Inference
Anton Xue, Avishree Khare, Rajeev Alur +2
We study how to subvert large language models (LLMs) from following prompt-specified rules. We first formalize rule-following as inference in propositional Horn logic, a mathematic…