@inproceedings{han-etal-2026-dailyreport,
title = "{D}aily{R}eport: An Open-ended Benchmark for Evaluating Search Agents on Daily Search Tasks",
author = "Han, Jingxuan and
Liu, Wei and
Zhu, Mingyang and
Wang, Youpeng and
Wang, Ziwen and
Qiu, Lin and
Cao, Xuezhi and
Cai, Xunliang and
Fu, Zheren 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.1071/",
pages = "22645--22675",
ISBN = "979-8-89176-462-0",
abstract = "Search Agents (SAs) typically leverage large language models (LLMs) to support complex information-seeking tasks by autonomously exploring web sources and synthesizing information into comprehensive responses. For SAs evaluation, prior benchmarks mainly focus on specialized tasks that are unlikely to arise in real-world user scenarios. Moreover, their reliance on coarse task-level rubrics often limits evaluation interpretability. To bridge this gap, we introduce DailyReport, an open-ended benchmark to evaluate SA capabilities on daily search tasks. It contains 150 open-ended tasks with 3,546 associated rubrics, capturing widely discussed and timely information demands of real-world users. Each task is decomposed into subtasks and evaluated with cascade rubrics across disentangled dimensions. Through cascade performance attribution and user-centric aggregation, we derive highly interpretable scores for each dimension, along with a user preference score. Our results on 17 agentic systems show that current systems still fall short of users' expectations. DailyReport will be open-sourced to facilitate future research."
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%0 Conference Proceedings
%T DailyReport: An Open-ended Benchmark for Evaluating Search Agents on Daily Search Tasks
%A Han, Jingxuan
%A Liu, Wei
%A Zhu, Mingyang
%A Wang, Youpeng
%A Wang, Ziwen
%A Qiu, Lin
%A Cao, Xuezhi
%A Cai, Xunliang
%A Fu, Zheren
%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 han-etal-2026-dailyreport
%X Search Agents (SAs) typically leverage large language models (LLMs) to support complex information-seeking tasks by autonomously exploring web sources and synthesizing information into comprehensive responses. For SAs evaluation, prior benchmarks mainly focus on specialized tasks that are unlikely to arise in real-world user scenarios. Moreover, their reliance on coarse task-level rubrics often limits evaluation interpretability. To bridge this gap, we introduce DailyReport, an open-ended benchmark to evaluate SA capabilities on daily search tasks. It contains 150 open-ended tasks with 3,546 associated rubrics, capturing widely discussed and timely information demands of real-world users. Each task is decomposed into subtasks and evaluated with cascade rubrics across disentangled dimensions. Through cascade performance attribution and user-centric aggregation, we derive highly interpretable scores for each dimension, along with a user preference score. Our results on 17 agentic systems show that current systems still fall short of users’ expectations. DailyReport will be open-sourced to facilitate future research.
%U https://aclanthology.org/2026.findings-emnlp.1071/
%P 22645-22675
Markdown (Informal)
[DailyReport: An Open-ended Benchmark for Evaluating Search Agents on Daily Search Tasks](https://aclanthology.org/2026.findings-emnlp.1071/) (Han et al., Findings 2026)
ACL
- Jingxuan Han, Wei Liu, Mingyang Zhu, Youpeng Wang, Ziwen Wang, Lin Qiu, Xuezhi Cao, Xunliang Cai, Zheren Fu, Licheng Zhang, and Zhendong Mao. 2026. DailyReport: An Open-ended Benchmark for Evaluating Search Agents on Daily Search Tasks. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 22645–22675, Budapest, Hungary. Association for Computational Linguistics.