5 citations · 9 across the 9 of their papers we have counts for
9 papers
Parallel SMT Solving via Dynamic Partitioning, Core-Guided Pruning, and Backbone Detection
Ilana Shapiro, Sorin Lerner, Nikolaj Bjørner
Exploiting parallelism in modern CPU architectures remains a longstanding challenge in optimizing SMT solvers. We introduce a novel framework for parallel SMT solving that uses fee…
Decision-Oriented Programming with Aporia
Saketh Ram Kasibatla, Raven Rothkopf, Hila Peleg +4
AI agents help developers express computational intent abstractly, but at a cost: developers cede decision-making authority to agents, often without realizing that important design…
The Command Line GUIde: Graphical Interfaces from Man Pages via AI
Saketh Ram Kasibatla, Kiran Medleri Hiremath, Raven Rothkopf +3
Although birthed in the era of teletypes, the command line shell survived the graphical interface revolution of the 1980's and lives on in modern desktop operating systems. The com…
HiLDe: Intentional Code Generation via Human-in-the-Loop Decoding
Emmanuel Anaya González, Raven Rothkopf, Sorin Lerner +1
While AI programming tools hold the promise of increasing programmers' capabilities and productivity to a remarkable degree, they often exclude users from essential decision-making…
Lemmanaid: Neuro-Symbolic Lemma Conjecturing
Yousef Alhessi, Sólrún Halla Einarsdóttir, George Granberry +4
Mathematicians and computer scientists are increasingly leveraging proof assistants to formalize and check complex proofs, a task that demands substantial expertise. Can we lower t…
Rango: Adaptive Retrieval-Augmented Proving for Automated Software Verification
Kyle Thompson, Nuno Saavedra, Pedro Carrott +6
Formal verification using proof assistants, such as Coq, enables the creation of high-quality software. However, the verification process requires significant expertise and manual…
Cobblestone: A Divide-and-Conquer Approach for Automating Formal Verification
Saketh Ram Kasibatla, Arpan Agarwal, Yuriy Brun +3
Formal verification using proof assistants, such as Coq, is an effective way of improving software quality, but requires significant effort and expertise. Machine learning can auto…
CoqPyt: Proof Navigation in Python in the Era of LLMs
Pedro Carrott, Nuno Saavedra, Kyle Thompson +3
Proof assistants enable users to develop machine-checked proofs regarding software-related properties. Unfortunately, the interactive nature of these proof assistants imposes most…
Learn from Failure: Fine-Tuning LLMs with Trial-and-Error Data for Intuitionistic Propositional Logic Proving
Chenyang An, Zhibo Chen, Qihao Ye +6
Recent advances in Automated Theorem Proving have shown the effectiveness of leveraging a (large) language model that generates tactics (i.e. proof steps) to search through proof s…