FDE-Gym is a new benchmark that evaluates AI agents on 95 end‑to‑end delivery tasks drawn from real customer scenarios across 18 sectors. Unlike traditional benchmarks that use well‑specified tasks, FDE-Gym places agents in simulated customer environments where they must clarify ambiguous requirements, interact with stakeholders, and uncover unstated constraints to improve a customer’s success metric. In tests, the best model configuration achieved a mean score of 0.56 and solved only 14 of the 95 tasks, highlighting a significant gap between well‑specified problem solving and real‑world AI delivery.
By Dongxu Yang, Hongda Yan, Renxin Wang, Baoli Zhang, Haojia Zuo, Ying Zhao, Youmin Chen, Xiaojian Liao, Congming Gao, Tianyang Jiang, Yixiao Chen, Qing Wang, Zhiguo Yu, Xiaqing Li, Yuhao Zhang, Jiawen Sun, Renzhe Xu, De Mi, Tiancheng Wu, Xianya Fu, Xuanlu Zhao, Bo Feng, Meixuan Ren, Guanming Lin, Ziheng Zhou, Xinhua Wang
arXiv:2610.14539v1 Announce Type: new
Abstract: Real-world business requests often leave key requirements unstated because frontline practitioners struggle to articulate rules they know from experien...
By Huaiming Li, Can Huang, Chufan Wu, Danyu Liu, Xiaomin Yuan, Qinze Li, Yui Lo, Wenhui Bai, Qianlong Wang, Zengle Ge, Xiyu Yan, Yuheng Cheng, Jinhui Ren, Guokai Chen, Jiaqun Liu, Haoran Li, Qianhui Liu, Annan Li, Jianmin Wu, Dawei Yin, Dou Shen
arXiv:2607. 22642v1 Announce Type: new Abstract: Enterprise coding agents translate natural-language analytical requests into executable code over proprietary APIs, schemas, and metric definitions.
By Aakash Kolekar, Sahika Genc, Shahriar Shariat, Bunyamin Sisman, Tibor Mezi, Barbara Poblete, Shree Vandana Kachroo, Calvin Chi, Parth Parmar, Ari Singer, Prayaas Jain, Cindy Barker, Benoit Dumoulin
The study evaluates local open‑weight large language model agents for data‑engineering tasks, using a benchmark of fifteen mobility‑workflow tasks that cover discovery, processing, enrichment, and reporting. Ten local model configurations were tested in one‑shot and closed‑loop conditions, producing 1,500 scored attempts on a consumer‑grade GPU. Results show that models larger than two billion parameters achieve up to 85.3% artifact‑level success, with closed‑loop workspaces boosting pass rates by 26.7–52.0 percentage points, and a quantized 9‑billion‑parameter model reaching 69.3% success with a 6.5 GB memory footprint.
By Jorge Garc\'ia-Carrasco, Javier Sanchis, Alejandro Reina-Reina, Alejandro Mat\'e, Juan Trujillo
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
arXiv:2606. 16000v1 Announce Type: cross Abstract: We introduce GRACE-DS, a Guarded Reward-guided Agent Correction Environment in Data Science for pre-deployment evaluation of LLM-powered AutoML agents.
By Aleksandr Tsymbalov, Danis Zaripov, Artem Epifanov, Anastasya Palienko