# LiveResearchBench: A Live Benchmark for User-Centric Deep Research in the Wild

arXiv: https://arxiv.org/abs/2510.14240 · HTML: https://mlsanity.com/paper/2510.14240/

## Key facts

- Cited by 37 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.
- First posted on arXiv 16 Oct 2025.

## Placed in

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

## Built on by (most cited first, 30 of 37)

- [A Survey of Agent Memory in the Second Half: Towards Self-Evolving and Long-Horizon Agents](https://mlsanity.com/paper/2602.06052/) (2026)
- [τ-Knowledge: Evaluating Conversational Agents over Unstructured Knowledge](https://mlsanity.com/paper/2603.04370/) (2026)
- [DeepResearch Bench II: Diagnosing Deep Research Agents via Rubrics from Expert Reports](https://mlsanity.com/paper/2601.08536/) (2026)
- [DRACO: a Cross-Domain Benchmark for Deep Research Accuracy, Completeness, and Objectivity](https://mlsanity.com/paper/2602.11685/) (2026)
- [DEER: A Benchmark for Evaluating Deep Research Agents on Expert Report Generation](https://mlsanity.com/paper/2512.17776/) (2025)
- [QUEST: Training Frontier Deep Research Agents with Fully Synthetic Tasks](https://mlsanity.com/paper/2605.24218/) (2026)
- [AutoResearch AI: Towards AI-Powered Research Automation for Scientific Discovery](https://mlsanity.com/paper/2605.23204/) (2026)
- [DREAM: Deep Research Evaluation with Agentic Metrics](https://mlsanity.com/paper/2602.18940/) (2026)
- [MiroEval: Benchmarking Multimodal Deep Research Agents in Process and Outcome](https://mlsanity.com/paper/2603.28407/) (2026)
- [Where Do Deep-Research Agents Go Wrong? Span-Level Error Localization in Agent Trajectories](https://mlsanity.com/paper/2606.02060/) (2026)
- [Why Your Deep Research Agent Fails? On Hallucination Evaluation in Full Research Trajectory](https://mlsanity.com/paper/2601.22984/) (2026)
- [Deep Research, Shallow Evaluation: A Case Study in Meta-Evaluation for Long-Form QA Benchmarks](https://mlsanity.com/paper/2603.06942/) (2026)
- [Beyond Single-shot Writing: Deep Research Agents are Unreliable at Multi-turn Report Revision](https://mlsanity.com/paper/2601.13217/) (2026)
- [Don't Stop Early: Scalable Enterprise Deep Research with Controlled Information Flow and Evidence-Aware Termination](https://mlsanity.com/paper/2604.24978/) (2026)
- [ScholarGym: Benchmarking Large Language Model Capabilities in the Information-Gathering Stage of Deep Research](https://mlsanity.com/paper/2601.21654/) (2026)
- [R2-Write: Reflection and Revision for Open-Ended Writing with Deep Reasoning](https://mlsanity.com/paper/2604.03004/) (2026)
- [OpenJarvis: Personal AI, On Personal Devices](https://mlsanity.com/paper/2605.17172/) (2026)
- [IntentRL: Training Proactive User-intent Agents for Open-ended Deep Research via Reinforcement Learning](https://mlsanity.com/paper/2602.03468/) (2026)
- [Rethinking Reasoning-Intensive Retrieval: Evaluating and Advancing Retrievers in Agentic Search Systems](https://mlsanity.com/paper/2605.04018/) (2026)
- [DuMate-DeepResearch: An Auditable Multi-Agent System with Recursive Search and Rubric-Grounded Reasoning](https://mlsanity.com/paper/2606.07299/) (2026)
- [FinanceHarness: Autonomous Financial Deep Research Framework](https://mlsanity.com/paper/2607.27853/) (2026)
- [Self-Manager: Parallel Agent Loop for Long-form Deep Research](https://mlsanity.com/paper/2601.17879/) (2026)
- [Wiki Live Challenge: Challenging Deep Research Agents with Expert-Level Wikipedia Articles](https://mlsanity.com/paper/2602.01590/) (2026)
- [LiveNewsBench: Evaluating LLM Web Search Capabilities with Freshly Curated News](https://mlsanity.com/paper/2602.13543/) (2026)
- [Evaluating Stochasticity in Deep Research Agents](https://mlsanity.com/paper/2602.23271/) (2026)
- [DR3-Eval: Towards Realistic and Reproducible Deep Research Evaluation](https://mlsanity.com/paper/2604.14683/) (2026)
- [DRACULA: Hunting for the Actions Users Want Deep Research Agents to Execute](https://mlsanity.com/paper/2604.23815/) (2026)
- [DeepWeb-Bench: A Deep Research Benchmark Demanding Massive Cross-Source Evidence and Long-Horizon Derivation](https://mlsanity.com/paper/2605.21482/) (2026)
- [Personalized Deep Research Query Refinement with Graph-Scaffolded Evidence Grounding](https://mlsanity.com/paper/2608.05876/) (2026)
- [Search-to-World: Evaluation of 3D World Delivery from User Request through Web Search](https://mlsanity.com/paper/2609.07605/) (2026)

## Abstract (arXiv)

Deep research -- producing comprehensive, citation-grounded reports by searching and synthesizing information from hundreds of live web sources -- marks an important frontier for agentic systems. To rigorously evaluate this ability, four principles are essential: tasks should be (1) user-centric, reflecting realistic information needs, (2) dynamic, requiring up-to-date information beyond parametric knowledge, (3) unambiguous, ensuring consistent interpretation across users, and (4) multi-faceted and search-intensive, requiring search over numerous web sources and in-depth analysis. Existing benchmarks fall short of these principles, often focusing on narrow domains or posing ambiguous questions that hinder fair comparison. Guided by these principles, we introduce LiveResearchBench, a benchmark of 100 expert-curated tasks spanning daily life, enterprise, and academia, each requiring extensive, dynamic, real-time web search and synthesis. Built with over 1,500 hours of human labor, LiveResearchBench provides a rigorous basis for systematic evaluation. To evaluate citation-grounded long-form reports, we introduce DeepEval, a comprehensive suite covering both content- and report-level quality, including coverage, presentation, citation accuracy and association, consistency and depth of analysis. DeepEval integrates four complementary evaluation protocols, each designed to ensure stable assessment and high agreement with human judgments. Using LiveResearchBench and DeepEval, we conduct a comprehensive evaluation of 17 frontier deep research systems, including single-agent web search, single-agent deep research, and multi-agent systems. Our analysis reveals current strengths, recurring failure modes, and key system components needed to advance reliable, insightful deep research. Our code is available at: https://github.com/SalesforceAIResearch/LiveResearchBench.