Aashish Yadavally

T-REX: Teaching Large Language Models to Reason with Verbalized Execution Semantics

Post-Hoc Attention Steering of Large Language Models for Robust Code Understanding under Obfuscation

Code obfuscation is widely used in software systems and malware to conceal program logic and hinder analysis, posing significant challenges for both human developers and automated tools. While large language models (LLMs) have shown strong capabilities in code understanding, their robustness to obfuscation remains poor...

On Behavioral Alignment of Model-Code and Human-Code Understandability via Behavioral Proxies

Code understandability is a critical aspect of software quality. Prior research has largely focused on this attribute from a human-centric or code-centric perspective, while it should be viewed as a relational property arising from the interaction between a reader and the code. With the increasing adoption of large lan...