3 papers
cs.LG2026
A Regression Framework for Understanding Prompt Component Impact on LLM Performance
Andrew Lauziere, Jonathan Daugherty, Taisa Kushner
As large language models (LLMs) continue to improve and see further integration into software systems, so does the need to understand the conditions in which they will perform. We…
cs.PL2025
Designing Walrus: Relational Programming with Rich Types, On-Demand Laziness, and Structured Traces
Santiago Cuéllar, Naomi Spargo, Jonathan Daugherty +1
We present Walrus, a functional relational programming language embedded in Haskell that extends the miniKanren model with type-polymorphic unification, on-demand laziness, and a r…
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…