An Open and Reproducible Deep Research Agent for Long-Form Question Answering

2025/12/15 by Ikuya Yamada, Yamada, Ikuya, Wataru Ikeda +11
Computer Science · #Computation and Language (cs.CL) #Expert finding and Q&A systems #FOS: Computer and information sciences #Information Retrieval and Search Behavior #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2512.13059

openalex publication_date 2025/12/15 · openalex created_date 2025/12/17 · openalex updated_date 2026/07/28

Abstract

We present an open deep research system for long-form question answering, selected as a winning system in the text-to-text track of the MMU-RAG competition at NeurIPS 2025. The system combines an open-source large language model (LLM) with an open web search API to perform iterative retrieval, reasoning, and synthesis in real-world open-domain settings. To enhance reasoning quality, we apply preference tuning based on LLM-as-a-judge feedback that evaluates multiple aspects, including clarity, insightfulness, and factuality. Our experimental results show that the proposed method consistently improves answer quality across all three aspects. Our source code is publicly available at https://github.com/efficient-deep-research/efficient-deep-research.

Citations

• DR Tulu: Reinforcement Learning with Evolving Rubrics for Deep Research
• SFR-DeepResearch: Towards Effective Reinforcement Learning for Autonomously Reasoning Single Agents
• Beyond Ten Turns: Unlocking Long-Horizon Agentic Search with Large-Scale Asynchronous RL
• Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models
• DeepResearchGym: A Free, Transparent, and Reproducible Evaluation Sandbox for Deep Research
• SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis
• Qwen3 Technical Report
• ZeroSearch: Incentivize the Search Capability of LLMs without Searching
• WebThinker: Empowering Large Reasoning Models with Deep Research Capability
• DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments
• Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
• R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning
• Chain-of-Retrieval Augmented Generation
• Search-o1: Agentic Search-Enhanced Large Reasoning Models
• Long²RAG: Evaluating Long-Context & Long-Form Retrieval-Augmented Generation with Key Point Recall
• Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Related

• Harnessing Multi-Role Capabilities of Large Language Models for Open-Domain Question Answering
• Long-form Question Answering: An Iterative Planning-Retrieval-Generation Approach
• Comparing the Performance of LLMs in RAG-based Question-Answering: A Case Study in Computer Science Literature
• RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering
• ELI5: Long Form Question Answering