AgentSWE is a new benchmark for agent software engineering that evaluates coding agents across three lifecycle stages: creating an agent from a natural‑language requirement, editing existing agent codebases, and optimizing toward a measurable target. The benchmark consists of 25 commission‑style tasks, where builders iterate on a few illustrative cases and are then tested on held‑out cases that cover the full requirement, with hard pass/fail gates or objective‑based grading. Experiments show that current coding agents fail to meet the requirements, revealing common failure modes such as overfitting, incomplete runtimes, and unsupported completion claims.
By Jiahao Wang, Hongjin Qian, Yuyang Hu, Jiajun Zhang, Zheng Liu
FrontierCS 2 is a new benchmark comprising 48 open‑ended computer‑science research tasks that place language‑model agents inside a research environment, allowing them to submit incremental patches to a working repository. Each patch is scored by a judge on a development suite, while the final evaluation uses a held‑out suite and a private reference derived from the original authors’ code. The benchmark covers 13 domains, includes a private set of over 100 future tasks, and evaluates 12 agents, with Claude Opus 5.5 achieving the highest pass rate of 58%.
By Qiuyang Mang, Wenhao Chai, Runyuan He, Dongruixuan Li, Zihan Zheng, Yujun He, Xuanyi Zhou, Hao Wang, Siddhant Ray, Bo Peng, Ruyi Ji, Shuo Yang, Haocheng Xi, Boru Chen, Xinyang David Han, Melissa Pan, Mayank Mishra, Boxi Yu, Sijia Liu, Kerem Akillioglu, Yi Pan, Zirui Wang, Mert Cemri, Diogo Antunes, Yichuan Wang, Aiwei Ivy Zhang, Zhenyang Chen, Qizheng Zhang, Kaiyuan Liu, Wenyuan Huang, Sherry Ruan, Shu Liu, Dacheng Li, Zerui Cheng, Zeyu Shen, Shang Zhou, Hanfei Chen, Yuzhong Zhang, Bohan Lyu, Qu Ao, Han Zheng, Chonghe Jiang, Jingzhuo Hu, Xiaokun Chen, Kunjun Li, Zhiqing Zhong, Haoye Tian, Tong Zhao, Yunzhong He, Hanchen Li, Ofir Press, Jiantao Jiao, Kunle Olukotun, Karthik R. Narasimhan, Jingbo Shang, Koushik Sen, Matei Zaharia, Alex Dimakis, Ion Stoica, Joseph E. Gonzalez, Alvin Cheung
The paper introduces Traverse, a benchmark of 2,518 agent trajectories and 6,967 annotated mistakes across software engineering, computer use, and science tasks, revealing that failures often go unrecovered and can cause irreversible harm before a run is deemed successful. It shows that human judges struggle to detect the first mistake in most runs, while a 4‑billion‑parameter verifier called Scout can locate failures more effectively and improve task success when used to select among candidate runs. The study demonstrates that making failure detection inexpensive and reliable can enable long‑horizon agents to learn from their own mistakes and increase trustworthiness in autonomous AI.
By Salman Rahman, Yubin Kim, Mihir Parmar, A. Ali Heydari, Genglin Liu, Simon A. Lee, Weizhi Zhang, Arian Hosseini, Ahmed A. Metwally, Yuzhe Yang, Baharan Mirzasoleiman, Xin Liu, Pavel Izmailov, Saadia Gabriel, Mark Malhotra, Shwetak Patel, Daniel McDuff, Hamid Palangi