aiqbench analysis
DailyReport introduces a novel benchmark for evaluating search agents using 150 open-ended tasks derived from real-world user queries, addressing the limitations of previous specialized datasets. By decomposing complex searches into subtasks with granular rubrics, it provides interpretable performance metrics that reveal current agentic systems still struggle to meet practical expectations. This work advances the field by offering a rigorous framework for assessing LLM-based search capabilities in realistic scenarios rather than narrow benchmarks.
DailyReport: An Open-ended Benchmark for Evaluating Search Agents on Daily Search Tasks
arXiv:2606.12871v1 Announce Type: new 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. To facilitate future research, our dataset and code are made publicly available at https://github.com/AGI-Eval-Official/DailyReport.
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. To facilitate future research, our dataset and code are made publicly available at this https URL.
Artificial Intelligence (cs.AI)
arXiv:2606.12871 [cs.AI]
(or arXiv:2606.12871v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.12871 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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