activity
20242026
most citedGuiding LLM Temporal Logic Generation with Explicit Separation of Data and Control

3 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LO2026

DateSAT: A Framework for Solving Date and Period Constraints

Leyi Cui, Shrey Tiwari, Rohan Padhye

Dates and calendar periods (i.e., days, months, years) appear frequently in tasks involving analysis of software, data, and documents. Prior research has shown that computer logic…

cs.SE2025★ 3 cited

CWEval: Outcome-driven Evaluation on Functionality and Security of LLM Code Generation

Jinjun Peng, Leyi Cui, Kele Huang +2

Large Language Models (LLMs) have significantly aided developers by generating or assisting in code writing, enhancing productivity across various tasks. While identifying incorrec…

cs.SE2024

Constrained LTL Specification Learning from Examples

Changjian Zhang, Parv Kapoor, Ian Dardik +4

Temporal logic specifications play an important role in a wide range of software analysis tasks, such as model checking, automated synthesis, program comprehension, and runtime mon…

cs.SE2024★ 1 cited

Combining LLM Code Generation with Formal Specifications and Reactive Program Synthesis

William Murphy, Nikolaus Holzer, Feitong Qiao +4

In the past few years, Large Language Models (LLMs) have exploded in usefulness and popularity for code generation tasks. However, LLMs still struggle with accuracy and are unsuita…

cs.LG2024★ 3 cited

Guiding LLM Temporal Logic Generation with Explicit Separation of Data and Control

William Murphy, Nikolaus Holzer, Nathan Koenig +4

Temporal logics are powerful tools that are widely used for the synthesis and verification of reactive systems. The recent progress on Large Language Models (LLMs) has the potentia…