@inproceedings{wang-etal-2026-asuka,
title = "Asuka-Bench: Benchmarking Code Agents on Underspecified User Intent and Multi-Round Refinement",
author = "Wang, Xin and
Sun, Liangtai and
Zhu, Yaoming and
Zhou, Shuang and
Liu, Jiaxing and
Chen, FengJiao and
Qiu, Lin and
Cao, Xuezhi and
Cai, Xunliang and
Zhang, Licheng and
Mao, Zhendong",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1548/",
pages = "32938--32959",
ISBN = "979-8-89176-462-0",
abstract = "Existing code-generation benchmarks score a single mapping from a complete prompt to a one-shot output. However, real web development is different. Users seldom write a full spec at the start; many requirements only become clear once they look at an intermediate result and react to it. We present Asuka-Bench, a benchmark that pairs underspecified user intent with multi-round refinement, grounded in browser-rendered behavior. Each task is resolved through a closed loop: a Code Agent generates a web project, a UI Agent executes test cases on the deployed site, and a User LLM turns evaluation outcomes into natural-language feedback for the next round. The benchmark comprises 50 web tasks with 784 evaluation criteria and 2402 expected outcomes. We benchmark 8 LLMs across 2 agent frameworks. The results separate models clearly: weighted Task Pass Rate varies by 38 percentage points and models also differ substantially in their ability to repair from feedback. Asuka-Bench is also far from saturated: even the strongest model completes only 52{\%} of projects after three rounds."
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<abstract>Existing code-generation benchmarks score a single mapping from a complete prompt to a one-shot output. However, real web development is different. Users seldom write a full spec at the start; many requirements only become clear once they look at an intermediate result and react to it. We present Asuka-Bench, a benchmark that pairs underspecified user intent with multi-round refinement, grounded in browser-rendered behavior. Each task is resolved through a closed loop: a Code Agent generates a web project, a UI Agent executes test cases on the deployed site, and a User LLM turns evaluation outcomes into natural-language feedback for the next round. The benchmark comprises 50 web tasks with 784 evaluation criteria and 2402 expected outcomes. We benchmark 8 LLMs across 2 agent frameworks. The results separate models clearly: weighted Task Pass Rate varies by 38 percentage points and models also differ substantially in their ability to repair from feedback. Asuka-Bench is also far from saturated: even the strongest model completes only 52% of projects after three rounds.</abstract>
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%0 Conference Proceedings
%T Asuka-Bench: Benchmarking Code Agents on Underspecified User Intent and Multi-Round Refinement
%A Wang, Xin
%A Sun, Liangtai
%A Zhu, Yaoming
%A Zhou, Shuang
%A Liu, Jiaxing
%A Chen, FengJiao
%A Qiu, Lin
%A Cao, Xuezhi
%A Cai, Xunliang
%A Zhang, Licheng
%A Mao, Zhendong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wang-etal-2026-asuka
%X Existing code-generation benchmarks score a single mapping from a complete prompt to a one-shot output. However, real web development is different. Users seldom write a full spec at the start; many requirements only become clear once they look at an intermediate result and react to it. We present Asuka-Bench, a benchmark that pairs underspecified user intent with multi-round refinement, grounded in browser-rendered behavior. Each task is resolved through a closed loop: a Code Agent generates a web project, a UI Agent executes test cases on the deployed site, and a User LLM turns evaluation outcomes into natural-language feedback for the next round. The benchmark comprises 50 web tasks with 784 evaluation criteria and 2402 expected outcomes. We benchmark 8 LLMs across 2 agent frameworks. The results separate models clearly: weighted Task Pass Rate varies by 38 percentage points and models also differ substantially in their ability to repair from feedback. Asuka-Bench is also far from saturated: even the strongest model completes only 52% of projects after three rounds.
%U https://aclanthology.org/2026.findings-emnlp.1548/
%P 32938-32959
Markdown (Informal)
[Asuka-Bench: Benchmarking Code Agents on Underspecified User Intent and Multi-Round Refinement](https://aclanthology.org/2026.findings-emnlp.1548/) (Wang et al., Findings 2026)
ACL
- Xin Wang, Liangtai Sun, Yaoming Zhu, Shuang Zhou, Jiaxing Liu, FengJiao Chen, Lin Qiu, Xuezhi Cao, Xunliang Cai, Licheng Zhang, and Zhendong Mao. 2026. Asuka-Bench: Benchmarking Code Agents on Underspecified User Intent and Multi-Round Refinement. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 32938–32959, Budapest, Hungary. Association for Computational Linguistics.