DailyReport Benchmark Evaluates Search Agents on Real-World Daily Tasks
Sonic Intelligence
New benchmark assesses search agents on daily, open-ended tasks.
Explain Like I'm Five
"Imagine you ask a smart computer program (a search agent) to find information for your daily questions, like 'What's the weather like?' or 'How do I fix this?' Most tests for these programs are too simple or for very specific things. DailyReport is a new, more realistic test that gives the program 150 everyday questions and then checks very carefully how well it answers each part. It found that even the best programs today aren't quite good enough for what people really need."
Deep Intelligence Analysis
The DailyReport benchmark comprises 150 open-ended tasks, supported by 3,546 associated rubrics. These tasks are meticulously designed to capture widely discussed and timely information demands of real-world users, ensuring relevance and practical applicability. To enhance interpretability, each task is decomposed into subtasks, and evaluation is conducted using cascade rubrics across disentangled dimensions. This approach allows for detailed performance attribution and user-centric aggregation, yielding highly interpretable scores for individual dimensions, alongside an overall user preference score. This level of detail provides actionable insights for developers.
Initial evaluations of 17 different agentic systems using DailyReport reveal a significant finding: current systems still fall short of users' expectations. This indicates a substantial gap between the current capabilities of LLM-powered search agents and the practical demands of everyday information retrieval. The implications are clear: while LLMs show promise in supporting complex information-seeking, their integration into effective search agents requires further refinement. DailyReport provides the necessary tools—a publicly available dataset and code—to facilitate future research and development, guiding the creation of more robust, reliable, and user-satisfying search agents that can truly augment human capabilities in daily information tasks.
Visual Intelligence
flowchart LR
A[Prior Benchmarks] --> B{Specialized Tasks}
B --> C[Limited Interpretability]
C --> D[DailyReport Benchmark]
D --> E{150 Open-ended Tasks}
E --> F[3,546 Cascade Rubrics]
F --> G[Interpretable Scores]
G --> H[Current Systems Fall Short]
Auto-generated diagram · AI-interpreted flow
Impact Assessment
Current search agent benchmarks often focus on specialized tasks, failing to reflect real-world user needs. DailyReport addresses this by providing a comprehensive, open-ended evaluation framework for common daily search tasks. This shift is crucial for developing search agents that are truly useful and reliable for everyday information-seeking, moving beyond academic exercises to practical utility.
Key Details
- DailyReport is an open-ended benchmark for evaluating Search Agents (SAs) on daily search tasks.
- It contains 150 open-ended tasks with 3,546 associated rubrics, reflecting real-world user information demands.
- Tasks are decomposed into subtasks and evaluated with cascade rubrics across disentangled dimensions for interpretability.
- The benchmark provides interpretable scores for each dimension and a user preference score.
- Results from testing 17 agentic systems indicate that current systems do not meet user expectations.
Optimistic Outlook
DailyReport will accelerate the development of more user-centric and effective search agents. By providing highly interpretable evaluation metrics and focusing on daily tasks, researchers can pinpoint weaknesses and iteratively improve agent capabilities. This will lead to AI agents that genuinely enhance user productivity and information access in everyday scenarios.
Pessimistic Outlook
The finding that current search agents fall short of user expectations, even on daily tasks, highlights a significant gap between AI capabilities and practical utility. Without substantial improvements driven by benchmarks like DailyReport, users may become disillusioned with search agents, limiting their adoption and hindering the potential for AI to assist in common information-seeking activities.
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