Optimist Deep Research：开源迭代式文献研究智能体
Optimist Deep Research: An Iterative, State-Centric Agent for Autonomous Literature Research
开源的文献综述智能体，能把一个问题拆成子问题树，自动去 arXiv、PubMed 等六个数据源检索并滚雪球扩展，3 分半读了 28 篇文献，每条引用都能溯源。
Optimist Deep Research 是一个开源文献研究智能体，把检索式问答改成对可变研究状态的迭代证据采集流程。系统将研究问题拆成子问题树，向 arXiv、OpenAlex、Crossref、PubMed、Semantic Scholar 等数据源发起查询，经四层去重合并进带溯源信息的文档注册表，并通过引文滚雪球扩展到饱和。一个 critic 沿 8 个维度审查研究状态，可增删、拆分或关闭子问题。最终报告逐节从结构化证据库生成，每条引用都对应已检索文档。案例研究中智能体在约 3.5 分钟内阅读了 237 篇检索文档中的 28 篇。
Optimist Deep Research: An Iterative, State-Centric Agent for Autonomous Literature Research
Most retrieval-augmented systems treat literature search as a single retrieval step followed by summarization. Real literature research works differently: the investigator's understanding of the question changes as they read, new subquestions appear, and evidence is weighed, not just collected. We present Optimist Deep Research, an open-source agent that runs literature research as an iterative evidence-acquisition process over an explicit, mutable research state. A research question is broken into a tree of subquestions, each served by search intents that are translated into source-specific queries over arXiv, OpenAlex, Crossref, PubMed, Semantic Scholar and the web. Results are merged into a canonical, provenance-preserving document registry through a conservative four-tier deduplication hierarchy. The registry is expanded by citation snowballing until a saturation criterion is met, and then screened, ranked and allocated to subquestions round-robin. Documents are read in parallel into a structured evidence store of claims with support, location, type and strength. A critic reviews the state along eight dimensions and returns research actions rather than a binary verdict: it can add, split, rephrase, deprioritize or close subquestions, so the research plan is an output of the investigation as well as an input. The final report is written section by section from the evidence store, and every citation resolves to a retrieved document. We describe the design, the deterministic machinery that keeps a language-model pipeline auditable, and a case study that read 28 of 237 retrieved documents in about three and a half minutes. We also report weaknesses the case study exposed.
