SWE‑Duel proposes a self‑scaling benchmark for coding agents, where tasks and oracles are generated at evaluation time to prevent data leakage and adapt difficulty to participant performance. In the arena, two agents take turns as Red and Blue: Red proposes a feature, injects a subtle bug, and must repair it blind, while Blue reviews the pull request without knowing the bug and must pass all tests. A tournament of 11 model‑harness participants on 15 repositories across five languages showed GPT‑5.5/Codex CLI as the top solver and Claude Opus 4.8/mini‑swe‑agent as the top task author, with newer model versions producing harder tasks for older ones.
By Wei Minn, Zhensu Sun, Lwin Khin Shar, David Lo
SWE‑Duel proposes a self‑scaling benchmark for coding agents, where tasks and oracles are generated at evaluation time to prevent data leakage and adapt difficulty to the agents’ capabilities. In the arena, two agents take turns modifying a real repository: the Red agent adds a feature, introduces a subtle bug, and the Blue agent must fix it without knowing the bug exists. A tournament of 11 model‑harness participants on 15 repositories in five languages showed GPT‑5.5 as the best solver and Claude Opus 4.8 as the best task author, with newer model versions producing harder tasks for older ones.
arXiv:2610.18090v1 Announce Type: new
Abstract: Ambiguous or incomplete task specifications complicate both execution and clarification for language-model agents. Yet agents' responses to these infor...
By Kaiyu Huang, Yiting Li, Angyang Gu, Pinjia He, Minchen Yu, Qingjiang Shi
arXiv:2610.16067v1 Announce Type: cross
Abstract: Agent evaluation harnesses are software systems that run test suites, generate inputs, and decide outcomes with test oracles. This paper audits such...
By Junjie Chen
The paper introduces a controlled evaluation to disentangle answer coverage, repeatable task advantages, and gains from pre‑execution selection in large‑language‑model (LLM) harnesses. On 386 MATH‑500 tasks, eight generated harnesses and a baseline with nine identical copies were compared over three executions each, revealing that identical programs provide a 2.16‑point repeat‑averaged oracle headroom while generated programs show more repeatable score patterns but mainly expose persistent weaknesses. The study concludes that coverage and repeatability alone cannot justify claims of useful specialization and proposes an evaluation standard for harness diversity that requires task advantages to persist across executions and improve on additional fixed‑program executions under matched inference budgets.
By Ziyang Xu, Haitian Zhong, Hao Zhou, Hao Qin, Chenhan Jin, Te Qi, Shengze Xu, Tieyong Zeng
arXiv:2610.16859v2 Announce Type: replace
Abstract: For repository-grounded coding agents, a functionally correct patch is not necessarily an acceptable one: developers also constrain how the task mu...
By Wenqiang Zhu, Xinping Lei, Zhiyi Lai, Zhengshuo Gong, Tianzhuang He, Songwei Yu, Weihao Li, Jingxuan Xu, Zihang Xu, Gang Wu, Zhongqiu Wang, Yuhui Bai, Jie Li, Zhao Liu, Junhao Fan, Yan Wu, Yanan Wu, Jiaheng Liu, Quan Gu, Ariel Deng