Trusted Convergence and Knowing What We Know Together: Privacy-Preserving Knowledge Discovery Across Neurodegenerative Disease Institutes

The rapid growth and disciplinary diversification of the neuroscience literature make it increasingly difficult for research institutes to identify where their expertise converges with that of peer institutions, a critical barrier to accelerating therapies for conditions such as Alzheimer's disease. Existing AI-assisted discovery tools rely on unconstrained, unverified web sources with no mechanism for secure, quality-controlled knowledge sharing. We present Giovanna, a domain-adapted retrieval-augmented generation (RAG) framework built on curated institutional corpora from Institute A and Institute B, designed to surface latent connections, support grounded hypothesis generation, and reveal cross-institutional research convergence within a trusted research environment. We contribute: (i) a neuroscience-specific embedding model fine-tuned on institutional corpora, achieving a recall@1 of 0.678 and MRR@20 of 0.780 superior to baseline; (ii) a privacy-preserving embedding-sharing approach that identifies shared research themes without exchanging raw text; and (iii) an empirical comparison of reasoning and non-reasoning language models across query-complexity tiers, showing RAG over trusted institutional knowledge is essential for complex queries, while selective generative fine-tuning yields gains only on domain-specific synthesis tasks. We release Giovanna as a lightweight application for faster insight discovery in neuroscience both within and across research institutions.