DailyReport: An Open-ended Benchmark for Evaluating Search Agents on Daily Search Tasks

2026-06-11 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Advanced, quick

Summary

DailyReport is introduced as an open-ended benchmark designed to evaluate Search Agents (SAs) on daily, real-world information-seeking tasks. This benchmark addresses the shortcomings of previous evaluations, which often focused on specialized tasks and lacked detailed interpretability due to coarse rubrics. DailyReport comprises 150 open-ended tasks, supported by 3,546 associated rubrics that capture timely user information demands. It employs a novel evaluation approach, decomposing tasks into subtasks and utilizing cascade rubrics across disentangled dimensions. This method, combined with cascade performance attribution and user-centric aggregation, yields highly interpretable scores for each dimension and a user preference score. Initial results from testing 17 agentic systems indicate that current SAs fall short of user expectations. The dataset and code are publicly available.

Key takeaway

For AI Engineers developing or evaluating Search Agents, you should integrate the DailyReport benchmark into your testing pipeline. Traditional benchmarks are insufficient for real-world scenarios, and this new open-ended benchmark, with its detailed rubrics, provides superior interpretability. Leverage its public dataset and code to rigorously assess your agent's performance against user expectations, as current systems demonstrably fall short.

Key insights

DailyReport offers an open-ended benchmark with detailed, interpretable rubrics to evaluate search agents on real-world tasks, revealing current systems fall short.

Principles

• Evaluation must reflect real-world user needs.
• Detailed rubrics improve evaluation interpretability.
• Decompose complex tasks for granular assessment.

Method

Decompose open-ended tasks into subtasks, apply cascade rubrics across disentangled dimensions, then use cascade performance attribution and user-centric aggregation to derive interpretable dimensional and user preference scores.

In practice

• Use DailyReport to benchmark search agents.
• Access public dataset and code for SA development.

Topics

• Search Agents
• LLM Evaluation
• Open-ended Benchmarks
• Evaluation Rubrics
• User Preference Scores
• DailyReport Dataset

Code references

• AGI-Eval-Official/DailyReport

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.