2 papers
cs.LG2026
Can LLMs Compress (and Decompress)? Evaluating Code Understanding and Execution via Invertibility
Nickil Maveli, Antonio Vergari, Shay B. Cohen
LLMs demonstrate strong performance on code benchmarks, yet consistent reasoning across forward and backward execution remains elusive. We present RoundTripCodeEval (RTCE), a bench…
cs.SE2025
What can Large Language Models Capture about Code Functional Equivalence?
Nickil Maveli, Antonio Vergari, Shay B. Cohen
Code-LLMs, LLMs pre-trained on large code corpora, have shown great progress in learning rich representations of the structure and syntax of code, successfully using it to generate…