# DeepWideSearch: Benchmarking Depth and Width in Agentic Information Seeking

arXiv: https://arxiv.org/abs/2510.20168 · HTML: https://mlsanity.com/paper/2510.20168/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 23 of its 31 references that are in the map.
- First posted on arXiv 23 Oct 2025.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 23 of 23)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [GAIA: a benchmark for General AI Assistants](https://mlsanity.com/paper/2311.12983/) (2023)
- [BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents](https://mlsanity.com/paper/2504.12516/) (2025)
- [DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents](https://mlsanity.com/paper/2506.11763/) (2025)
- [WebSailor: Navigating Super-human Reasoning for Web Agent](https://mlsanity.com/paper/2507.02592/) (2025)
- [ZeroSearch: Incentivize the Search Capability of LLMs without Searching](https://mlsanity.com/paper/2505.04588/) (2025)
- [A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence](https://mlsanity.com/paper/2507.21046/) (2025)
- [BrowseComp-ZH: Benchmarking Web Browsing Ability of Large Language Models in Chinese](https://mlsanity.com/paper/2504.19314/) (2025)
- [OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation](https://mlsanity.com/paper/2505.23885/) (2025)
- [WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization](https://mlsanity.com/paper/2507.15061/) (2025)
- [Multi-Agent Design: Optimizing Agents with Better Prompts and Topologies](https://mlsanity.com/paper/2502.02533/) (2025)
- [WideSearch: Benchmarking Agentic Broad Info-Seeking](https://mlsanity.com/paper/2508.07999/) (2025)
- [PaSa: An LLM Agent for Comprehensive Academic Paper Search](https://mlsanity.com/paper/2501.10120/) (2025)
- [A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challenges](https://mlsanity.com/paper/2508.05668/) (2025)
- [WebEvolver: Enhancing Web Agent Self-Improvement with Coevolving World Model](https://mlsanity.com/paper/2504.21024/) (2025)
- [RLVMR: Reinforcement Learning with Verifiable Meta-Reasoning Rewards for Robust Long-Horizon Agents](https://mlsanity.com/paper/2507.22844/) (2025)
- [DeepShop: A Benchmark for Deep Research Shopping Agents](https://mlsanity.com/paper/2506.02839/) (2025)
- [Cognitive Kernel-Pro: A Framework for Deep Research Agents and Agent Foundation Models Training](https://mlsanity.com/paper/2508.00414/) (2025)
- [AgentRM: Enhancing Agent Generalization with Reward Modeling](https://mlsanity.com/paper/2502.18407/) (2025)
- [SPAR: Scholar Paper Retrieval with LLM-based Agents for Enhanced Academic Search](https://mlsanity.com/paper/2507.15245/) (2025)

## Built on by (most cited first, 21 of 21)

- [A Survey of Agentic Reasoning for Large Language Models: Towards Recursively Self-Improving and Collective Agents](https://mlsanity.com/paper/2601.12538/) (2026)
- [MMDeepResearch-Bench: A Benchmark for Multimodal Deep Research Agents](https://mlsanity.com/paper/2601.12346/) (2026)
- [Marco DeepResearch: Unlocking Efficient Deep Research Agents via Verification-Centric Design](https://mlsanity.com/paper/2603.28376/) (2026)
- [Time to REFLECT: Can We Trust LLM Judges for Evidence-based Research Agents?](https://mlsanity.com/paper/2605.19196/) (2026)
- [WideSeek-R1: Exploring Width Scaling for Broad Information Seeking via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2602.04634/) (2026)
- [LocalSearchBench: Benchmarking Agentic Search in Real-World Local Life Services](https://mlsanity.com/paper/2512.07436/) (2025)
- [DR-Arena: an Automated Evaluation Framework for Deep Research Agents](https://mlsanity.com/paper/2601.10504/) (2026)
- [GISA: A Benchmark for General Information-Seeking Assistant](https://mlsanity.com/paper/2602.08543/) (2026)
- [A-MapReduce: Executing Wide Search via Agentic MapReduce](https://mlsanity.com/paper/2602.01331/) (2026)
- [Table-as-Search: Formulate Long-Horizon Agentic Information Seeking as Table Completion](https://mlsanity.com/paper/2602.06724/) (2026)
- [Towards Verifiable Multimodal Deep Research: A Multi-Agent Harness for Interleaved Report Generation](https://mlsanity.com/paper/2605.29861/) (2026)
- [VistaHop: Benchmarking Long-Horizon Visual DeepSearch](https://mlsanity.com/paper/2606.03273/) (2026)
- [When Search Agents Should Ask: DiscoBench for Clarification-Aware Deep Search](https://mlsanity.com/paper/2606.27669/) (2026)
- [WebSwarm: Recursive Multi-Agent Orchestration for Deep-and-Wide Web Search](https://mlsanity.com/paper/2607.08662/) (2026)
- [WANDR: A Benchmark for Wide and Deep Research](https://mlsanity.com/paper/2608.14747/) (2026)
- [CHIME: Credit-Aware Hierarchical Memory Evolution for Long-Horizon Agentic Planning](https://mlsanity.com/paper/2609.02074/) (2026)
- [SGR-Bench: Benchmarking Search Agents on State-Gated Retrieval](https://mlsanity.com/paper/2605.22219/) (2026)
- [SciDataSailor: Deep Scientific Data Exploring](https://mlsanity.com/paper/2607.28098/) (2026)
- [CRISP: Critical Step Perception for Training Efficient Deep Search Agents](https://mlsanity.com/paper/2608.01867/) (2026)
- [Mr.LHDR: A Benchmark for Multimodal Real-World Long-Horizon Deep Research Agents](https://mlsanity.com/paper/2609.11318/) (2026)
- [Efficiently Linking Unstructured Data for Multi-step Reasoning](https://mlsanity.com/paper/2609.19491/) (2026)

## Abstract (arXiv)

Current search agents fundamentally lack the ability to simultaneously perform textitdeep reasoning over multi-hop retrieval and textitwide-scale information collection-a critical deficiency for real-world applications like comprehensive market analysis and business development. To bridge this gap, we introduce DeepWideSearch, the first benchmark explicitly designed to evaluate agents to integrate depth and width in information seeking. In DeepWideSearch, agents must process a large volume of data, each requiring deep reasoning over multi-hop retrieval paths. Specifically, we propose two methods to converse established datasets, resulting in a curated collection of 220 questions spanning 15 diverse domains. Extensive experiments demonstrate that even state-of-the-art agents achieve only 2.39% average success rate on DeepWideSearch, highlighting the substantial challenge of integrating depth and width search in information-seeking tasks. Furthermore, our error analysis reveals four failure modes: lack of reflection, overreliance on internal knowledge, insufficient retrieval, and context overflow-exposing key limitations in current agent architectures. We publicly release DeepWideSearch to catalyze future research on more capable and robust information-seeking agents.