Hugging Face Trending Papers

Static analysis-guided agentic AI translation enables Rust as a full stack bioinformatics language

Read the original on Hugging Face Trending Papers →

The field of bioinformatics struggles with legacy code - old code that is commonly used but may no longer have a maintainer, or may be written in an now-unfamiliar language (e. g.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Hugging Face Trending Papers.

arXiv AI
Jul 7

RustMizan: A Compilable, Contamination-Aware Benchmarking Framework for Rust Vulnerabilities

arXiv:2607. 04729v1 Announce Type: cross Abstract: LLM agents are increasingly applied to vulnerability analysis, but existing benchmarks have not kept pace.

By Tarek Elsayed, Shiping Yang, Eunsong Koh, Sanika Goyal, Vincent Huang, Paul Ngo, Nathan Young, Mohammad Omidvar Tehrani, Alvyn Kang, Arnell Kang, Zeyu Chen, Ang\'elica Moreira, Xuan Feng, Angel X. Chang, Nick Sumner, Steven Y. Ko
arXiv AI
Aug 17

Fine-Tuning Qwen3-27B for C-to-Rust Code Translation: A Three-Stage Curriculum of Pretraining, Debugging-Aware SFT, and Task-Specific SFT

arXiv:2608. 13681v1 Announce Type: cross Abstract: Translating C code into safe, idiomatic Rust is a longstanding software-engineering goal because it can eliminate entire classes of memory-safety vulnerabilities while preserving the functional behavior of legacy systems.

By Pu Zhao, Changdi Yang, Yixiao Chen, Yi Gao, Yifan Cao, Haochen Zeng, Yanzhi Wang
arXiv AI
Sep 4

Bioinfoysis Technical Report

The Bioinfoysis Technical Report introduces a multi‑agent harness designed to improve long‑horizon bioinformatics tasks by maintaining persistent, artifact‑grounded analysis runs. It combines global planning with step‑wise, evidence‑driven replanning, ensuring intermediate results are tied to responsible agents and preventing stale evidence reuse. The system was evaluated on BixBench and LAB‑Bench 2, achieving state‑of‑the‑art accuracy and demonstrating that reliable bioinformatics automation relies on robust planning, execution, memory, and evidence flow.

By Qingyang Shao, Xin Zhang, Zhouyang Yuan, Xianying Chen, Yujia Xiang, Zihao Yang, Tong Ye, Yangqi Zhang, Jiakang Xu, Xiaoqing Yan, Xuan Luo, Keyi Li, Enci Fan, Kai Kang, Zhuohan Liu, Xingyu Jin, Chunran Teng, Tao Li, Xinyu Lv, Minghui Wang, Wenfeng Li, Yidan Gao, Siyu Liu, Mingrui Luo, Zhu Liang, Guanren Qiao, Zhiping Xu