arXiv AI By Zhan-Yu Wu, Ji Luo, Alyssa Wu, Junjielong Xu, Richeng Xuan, Zhichao Hu, Chongming Gao, Xinting Hu

UserSWE: Evaluating Coding Agents in Multi-Turn Conversations with Dynamic Users

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arXiv Computation and Language
Oct 12

SWE-Duel: Towards a Self-Scaling Arena for Coding Agents

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
Hugging Face Trending Papers
Oct 8

SWE-Duel: Towards a Self-Scaling Arena for Coding Agents

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.