开源系统实现长文本问答，迭代检索推理提升答案质量
An Open and Reproducible Deep Research Agent for Long-Form Question Answering
- 用开源大模型+网络搜索，循环执行查、想、合三步
- 通过AI评分优化后，清晰度、洞察力、事实性全面提升
- 适合研究者复现，代码公开可直接使用
我们提出一个用于长文本问答的开源深度研究系统，该系统在NeurIPS 2025的MMU-RAG竞赛文本到文本赛道中被评为优胜方案。系统结合开源大语言模型（LLM）与开放网络搜索API，实现在真实开放领域下的迭代式检索、推理与综合。为提升推理质量，我们采用基于大模型作为评判者的偏好微调方法，从清晰度、洞察力和事实性等多个维度评估并优化回答。实验结果表明，该方法在所有三个维度上均持续提升答案质量。源代码已公开，地址为https://github.com/efficient-deep-research/efficient-deep-research。
原文摘要 · Abstract (English)
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.
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