MedResearch: A Screening-Guided Agent for Medical Deep Research

Medical deep research collects evidence that meets predefined eligibility criteria and synthesizes it into reports to support medical decision-making. A well-supported report, however, does not ensure comprehensive coverage of eligible evidence. Task-trained methods require additional supervision, while training-free methods reuse existing models but often use screening mainly to select sources rather than guide subsequent actions. Acting on screening judgments poses two challenges: exclusions may be incorrect, and the amount of undiscovered eligible evidence is unknown. We introduce MedResearch, a training-free agent built around Screen-to-Steer: screening judgments guide both evidence retention and subsequent agent actions. Its Protocol Reader reassesses first-pass exclusions with additional text from the same source; Eligibility Memory preserves the resulting judgments and search progress; and the Search Controller uses this state to direct further retrieval, trigger one-shot recovery, or stop. The retained evidence is then synthesized into a structured research report, with all model weights kept frozen throughout execution. Experiments on MetaSyn and CLEF show higher evidence-selection recall and F1 than the evaluated RAG and deep research baselines, together with strong report-level conclusion alignment. Ablations and evidence-flow analysis further highlight the complementary roles of reassessment, memory, and action control in collecting and retaining evidence for synthesis. Code is available in an anonymous repository.