arXiv AI By Daniel Furman, Preeya Kirani, Ibrahim Sheikh, Zhen Li, Peter Zhong, Michele Catasta

ViBench 2.0: A Benchmark for Long-Horizon Software Building

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ViBench 2.0 is a new benchmark for long‑horizon software building, featuring 17 full‑stack web products that evolve through 241 feature requests. The benchmark evaluates models only when the entire product is finished, using 544 test plans per product to ensure a strict grading protocol. Initial results show a reliability gap: top models pass most test plans but rarely deliver fully working products, though recent releases have improved performance significantly.

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arXiv AI
Oct 12

AgentSWE: Can Coding Agents Build the Agent You Actually Want?

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 AI
Jun 10

How can we assess human-agent interactions? Case studies in software agent design

arXiv:2510. 09801v3 Announce Type: replace Abstract: While benchmarks measure the accuracy of LLM-powered agents, they mostly assume full automation, failing to represent the collaborative nature of real-world use cases.

By Valerie Chen, Rohit Malhotra, Xingyao Wang, Juan Michelini, Xuhui Zhou, Aditya Bharat Soni, Hoang H. Tran, Calvin Smith, Ameet Talwalkar, Graham Neubig