Optimist Deep Research: An Iterative, State-Centric Agent for Autonomous Literature Research

arXiv:2610.23299v1 Announce Type: new Abstract: 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 in

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