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

2026-04-20 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

DailyReport introduces an open-ended benchmark for evaluating Search Agents (SAs) on real-world daily search tasks, addressing limitations of prior specialized benchmarks. It comprises 150 tasks and 3,546 associated rubrics, derived from trending topics and user comments on platforms like Weibo and Facebook, reflecting authentic user information needs. The benchmark employs a user-centric cascade evaluation pipeline, decomposing tasks into subtasks and assessing them across instruction following, factuality, and rationality dimensions. This yields interpretable dimensional scores and a user preference score. Empirical assessment of 17 agentic systems, including GPT 5.4-based configurations, revealed that current SAs struggle significantly with factuality, rationality, and user preference, falling short of user expectations despite strong instruction-following abilities. The dataset and code are publicly available.

Key takeaway

For AI Engineers developing Search Agent systems, recognize that current models, even GPT 5.4-based configurations, significantly underperform on real-world factuality, rationality, and user preference. Your development efforts should prioritize robust evidence gathering, cross-source verification, and logical reasoning over trending topics. Utilize benchmarks like DailyReport to rigorously test and validate improvements in these critical user-centric dimensions, ensuring outputs genuinely satisfy complex information needs.

Key insights

DailyReport offers a user-centric benchmark for Search Agents, evaluating real-world tasks via cascade rubrics and user preference scores.

Principles

• Benchmark SAs on authentic, evolving daily user needs.
• Decompose tasks into subtasks for cascade evaluation.
• Quantify user preference alongside dimensional performance.

Method

DailyReport decomposes tasks into subtasks, applies cascade rubrics across instruction following, factuality, and rationality, then uses cascade attribution and subtask importance for interpretable dimensional and user preference scores.

In practice

• Benchmark new SAs with DailyReport's real-world tasks.
• Prioritize SA development on factuality and rationality.
• Implement explicit citation verification mechanisms.

Topics

• Search Agents
• LLM Evaluation
• Benchmark Datasets
• Factuality Metrics
• User Preference Scoring
• Information Retrieval

Code references

• AGI-Eval-Official/DailyReport
• LearningCircuit/local-deep-research

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.