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New UserSWE benchmark reveals coding agent performance varies with interaction turns

By PulseAugur Editorial · [1 sources] ·

A new benchmark called UserSWE has been developed to evaluate the effectiveness of coding AI agents in multi-turn conversational scenarios. Unlike previous benchmarks that present issues in a single turn, UserSWE simulates dynamic user interactions and provides execution-grounded verdicts at each step. This approach aims to better reflect real-world bug-fixing processes. Evaluations on eleven agents showed that performance rankings vary significantly based on the number of interaction turns allowed, with Claude Fable 5.1 excelling in single-turn tasks and GPT-6-Astra performing best with eight turns. AI

IMPACT Highlights the need for interactive evaluation of coding agents, suggesting current benchmarks may misrepresent their real-world utility.

RANK_REASON The cluster contains a research paper introducing a new benchmark for evaluating AI agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New UserSWE benchmark reveals coding agent performance varies with interaction turns

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The cluster contains a research paper introducing a new benchmark for evaluating AI agents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · 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

    arXiv:2610.29822v1 Announce Type: new Abstract: As coding agents become more capable, developers increasingly use them to fix bugs through multi-turn interactions. Existing benchmarks do not adequately cover this scenario. Most present a complete issue report in a single turn wit…

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