crawlQA: Agentic Web QA Benchmark

• crawlQA is a benchmark and dataset construction method for multi-hop question answering over web-crawled content, emphasizing real-world complexity and compositional query formulation.
• It employs a multi-stage pipeline including recursive web crawling, LLM-based question generation, and robust answer annotation to simulate authentic, multi-page research behaviors.
• The framework supports agent training and evaluation by enabling deep reasoning, evidence synthesis, and sequential decision-making across diverse web resources.

crawlQA is a benchmark and dataset construction methodology for question answering over web-crawled content, designed to support agentic systems requiring multi-step reasoning and deep web information seeking. Positioned at the intersection of autonomous agent training, information retrieval, and question answering (QA), crawlQA emphasizes real-world complexity and compositionality in query formulation and answer discovery, going beyond shallow or factoid QA over static corpora.

1. Definition and Core Objectives

crawlQA denotes a curated dataset of question-answer (QA) pairs constructed by automatically crawling real-world web pages—typically by recursively following hyperlinks on trusted domains (e.g., arXiv, GitHub, Wikipedia)—and using LLMs to generate complex, compositional questions that require aggregating information across multiple subpages. This design simulates authentic web research behaviors, reflecting realistic user objectives that span synthesis, comparison, and cross-site navigation. The methodology focuses on building benchmarks and training data for evaluating and training agentic web agents capable of multi-step information seeking, deep reasoning, and autonomous web exploration (Wu et al., 28 May 2025).

2. Dataset Construction Methodology

The crawlQA dataset is constructed through a multi-stage pipeline:

• Web Page Crawling: Trusted entry-point pages are selected, and outgoing hyperlinks are recursively traversed to gather a rich, web-crawled corpus comprising subpages with diverse content and structures.
• Question Generation: For each set of crawled subpages, a strong LLM (e.g., GPT-4o) is prompted to generate questions that explicitly reference multiple subpages, enforcing nontrivial compositionality. Supported question types include COUNT (aggregation/counting entities), MULTI-HOP (multi-fact reasoning), and INTERSECTION (cross-page comparison).
• Answer Annotation: Gold answers are programmatically extracted or LLM-verified from the harvested subpages, ensuring answer integrity and aligning evaluation with the constructed QA challenge.
• Quality Control: Steps include LLM-in-the-loop validation and postprocessing to filter trivial or underspecified queries and enforce diversity in question types and difficulties.

This methodology yields datasets in which questions cannot be answered by inspecting a single web document, thus requiring multi-hop synthesis and aggregation.

3. Role in Agentic Web Research and Model Training

crawlQA serves as a central resource for training, evaluating, and benchmarking agentic web-interacting models, especially those instantiated using frameworks such as ReAct (Reasoning + Acting). In the agent construction paradigm described in (Wu et al., 28 May 2025), crawlQA constitutes the core “browsing data” stage that exposes agents to the kinds of reasoning—long-horizon search, multi-step decomposition, evidence synthesis—demanded in sophisticated information seeking tasks.

During training, crawlQA enables:

• Supervised Fine-tuning (SFT): Agent models learn from high-quality, stepwise trajectories over crawlQA data, coupling chain-of-thought reasoning with web actions.
• Reinforcement Learning (RL): The multi-step structure of crawlQA queries supports robust RL optimization, rewarding not just final correctness but successful decomposition, retrieval, and multi-source synthesis.

4. Technical Characteristics and Benchmark Structure

The distinguishing characteristics of crawlQA include:

• Multi-hop & Compositional Queries: Each QA pair is constructed to demand evidence from at least two distinct web subpages, often requiring set operations, reasoning chains, or aggregation.
• Real Web Content: All contexts are real crawled web pages, reflecting the language, formatting, and structural variability of the open web (unlike synthetic or Wikipedia-only QA datasets).
• Agent-Friendly Format: Data is readily consumable by agentic architectures, with QA pairs accompanied by detailed web traversal logs, enabling evaluation of action-observation reasoning as well as answer generation.
• Integration with e2hQA: crawlQA is often used in conjunction with e2hQA (Easy-to-Hard QA), which provides a curriculum of questions from simple to highly compositional, further enhancing the data landscape for agentic system training (Wu et al., 28 May 2025).

5. Impact on Evaluation and Advancements

crawlQA fundamentally advances QA and agentic web research by providing a ground-truth resource that requires “deep reading” and compositional inference over open-domain, real-world web content. It enables:

• Robust Agent Evaluation: Testing agents on crawlQA reveals strengths and weaknesses in planning, memory, action selection, and multi-document reasoning.
• Data-centric RL and SFT: Empirical evidence from (Wu et al., 28 May 2025) shows that agents trained on crawlQA, using robust trajectory filtering and chain-of-thought strategies, outperform vanilla ReAct agents and general-purpose LLMs on benchmarks like GAIA and WebWalkerQA.
• Scalable Benchmarking: crawlQA supports scalable creation of new, high-difficulty agentic tasks by leveraging LLMs for automatic question and trajectory generation.

A plausible implication is that crawlQA’s approach—systematic, agent-guided crawling and question construction—can be extended to other domains and enhanced with more sophisticated verification, temporal reasoning, or grounded instruction following, becoming a foundation for future autonomous information-seeking benchmarks.

6. Relationship to Broader QA and RAG Benchmarks

crawlQA departs from traditional QA datasets in several key dimensions:

Aspect	crawlQA	Traditional QA (e.g., SQuAD, NQ)
Context Source	Web-crawled, multi-page	Wikipedia/news, single doc
Compositionality	Multi-hop, multi-source	Often single-hop
Agent Trajectory Support	Yes (actions/observations)	No
Goal	Autonomy, synthesis, planning	Factoid extraction

The methodology addresses shortcomings of simple factual recall tasks and supports evaluation against the information needs and reasoning depth presented in emergent agent systems. In concert with other emergent benchmarks (e.g., CRUMQs (Liu et al., 13 Oct 2025), WebQuest (Wang et al., 6 Sep 2024)), crawlQA helps define the new landscape of QA and retrieval-augmented generation where robust, agentic reasoning is paramount.