EvolvingScholar DNN

EvolvingScholar is an AI "research trainee" that learns the way a real medical trainee does — mentor-guided and improving across a *sequence* of gene–disease projects. Given a gene and a disease, the Scholar generates research questions, reviews the literature, analyzes synthetic EMR data to test gene–disease associations, and proposes testable hypotheses. At checkpoints it "meets its PI" and receives structured, rubric-based feedback that drives its growth before the next project. The ultimate goal is to evaluate and compare AI Scholar Agent evolving trajectory growth, rather than autonomous co-scientist. The system is built as three nested loops: **Loop A** does the research for one gene–disease pair; **Loop B** injects structured PI-mentor feedback; **Loop C** — the actual research contribution — rewrites the Scholar between projects. The core design bet is that **growth is not prompt accumulation**: cross-project learning happens only through updates to external, typed, versioned artifacts (the concept model, question-design logic, method repertoire, and earned EPAs), never through an ever-swelling prompt. This means "the Scholar evolved" is a recorded git diff, competency is read out on the clinical Entrustable Professional Activity (EPA) entrustment ladder (observe → independent → supervise), and the evolution measurement is free of the context-length confound that grow-the-prompt systems suffer. **What we found (working pilot, v0.1.0):** two scholars — one on the Claude Agent SDK with rich tools, one on the raw Messages API with minimal tools — each completed a full A→B→C run on the first project (TTR / hereditary transthyretin amyloidosis). The API scholar reached entrustment level 2 with 4 earned EPAs on run 1. Both share one review-and-evolve harness but keep separate, endowment-coupled experience stores with no cross-pollination. **Why it matters:** the AI Scholar is a controllable model system for a developmental process — how research questioning and disease conceptualization evolve — that is knowledge-entangled and nearly impossible to isolate in humans. Longer term it's a first step toward a self-taught AI clinical expert, the EPA based AI application in medicine, and a testbed for how AI can serve in medical education and research. PS: slides https://dlmp-niu.github.io/EvolvingScholar/demo/talk/index.public.html