From the 1 of 4 linked papers with an AI index.
4 papers
D-LiFT: Improving LLM-based Decompiler Backend via Code Quality-driven Fine-tuning
Muqi Zou, Hongyu Cai, Hongwei Wu +9
The paper introduces D-LiFT, a system that fine‑tunes large language models with a code‑quality‑aware reinforcement learning approach to improve the accuracy and readability of dec…
Root-Cause-Driven Automated Vulnerability Repair
Hulin Wang, Zion Leonahenahe Basque, Jie Hu +13
Recent LLM-based systems have made automated vulnerability repair increasingly practical, but two challenges remain. First, without strong signals about where a bug originates, rep…
SCRIBE: Practical Static Binary Patching via Binary-Aware Recompilation of Decompiled Code
Han Dai, Soumyakant Priyadarshan, Abdullah Imran +2
When source code or the original toolchain is unavailable, patching binaries is difficult because it requires editing low-level assembly code directly. As an alternative, one can d…
BuildBench: Benchmarking LLM Agents on Compiling Real-World Open-Source Software
Zehua Zhang, Ati Priya Bajaj, Divij Handa +13
Automatically compiling open-source software (OSS) projects is a vital, labor-intensive, and complex task, which makes it a good challenge for LLM Agents. Existing methods rely on…