3 papers
cs.SE2026
Agentic Interpretation: Lattice-Structured Evidence for LLM-Based Program Analysis
Jacqueline L. Mitchell, Chao Wang
Large language models can consult information that fixed static analyzers cannot, such as documentation, current security advisories, version-specific metadata, and informal API co…
cs.LG2026
Analyzing Fairness of Neural Network Prediction via Counterfactual Dataset Generation
Brian Hyeongseok Kim, Jacqueline L. Mitchell, Chao Wang
Interpreting the inference-time behavior of deep neural networks remains a challenging problem. Existing approaches to counterfactual explanation typically ask: What is the closest…
cs.LG2025
Understanding Formal Reasoning Failures in LLMs as Abstract Interpreters
Jacqueline L. Mitchell, Brian Hyeongseok Kim, Chenyu Zhou +1
Large language models (LLMs) are increasingly used for program verification, and yet little is known about \emph{how} they reason about program semantics during this process. In th…