output
20112026
most citedAn Inductive Synthesis Framework for Verifiable Reinforcement Learning

52 citations

21 papers

cs.CR2026

Lazy Arithmetic using Systolic Arrays for Closing the Verification Gap on Embedded Systems

Taisa Kushner, Ryan McCleeary, Martin Brain

Complex algorithms such as deep neural networks are increasingly being deployed on embedded, resource constrained platforms. However, existing hardware and software schemes for imp…

cs.PL2026

CNnotator: LLM-Guided Memory Safety Annotation Synthesis

Twain Byrnes, Mike Dodds

Memory safety errors account for a large proportion of security bugs in systems written in C; modern languages such as Java and Rust prevent such bugs because they are memory-safe…

cs.PL2025

The CoCompiler: DSL Lifting via Relational Compilation

Naomi Spargo, Santiago Cuéllar, Jonathan Daugherty +2

Lifting low-level or legacy code into a domain-specific language (DSL) improves our ability to understand it, enables deeper formal reasoning, and facilitates safe modification. We…

q-bio.QM2025★ 3 cited

A Physiologically-Constrained Neural Network Digital Twin Framework for Replicating Glucose Dynamics in Type 1 Diabetes

Valentina Roquemen-Echeverri, Taisa Kushner, Peter G. Jacobs +1

Simulating glucose dynamics in individuals with type 1 diabetes (T1D) is critical for developing personalized treatments and supporting data-driven clinical decisions. Existing mod…

cs.LG2024★ 2 cited

Combining AI Control Systems and Human Decision Support via Robustness and Criticality

Walt Woods, Alexander Grushin, Simon Khan +1

AI-enabled capabilities are reaching the requisite level of maturity to be deployed in the real world, yet do not always make correct or safe decisions. One way of addressing these…

cs.PL2024★ 9 cited

KATch: A Fast Symbolic Verifier for NetKAT

Mark Moeller, Jules Jacobs, Olivier Savary Belanger +5

We develop new data structures and algorithms for checking verification queries in NetKAT, a domain-specific language for specifying the behavior of network data planes. Our result…