Background: Paper V introduced the .causal binary format, achieving 1.90x fact amplification through deterministictransitive inference. However, the system remained dependent on manual paper selection—a bottleneck that limitedscalability and introduced selection bias. Innovation: I present the Sovereign Gap-Driven Discovery Engine—an autonomous system that identifies knowledgegaps in the existing graph, generates targeted PubMed queries, and selectively downloads papers predicted tofill those gaps. The system includes a reasoning logger that documents why each decision is made, enabling fullreproducibility. Results: Starting from 5,084 triplets (Paper V), the autonomous engine expanded the knowledge base to 8,500+explicit triplets (+67%) and 25,000 total facts with inference (+191% amplification). The system identified 11,198knowledge gaps across six taxonomic categories and 1,135 convergence points, yielding 37 strong hypotheses with3+ independent supporting paths. Significance: This work transforms the Sovereign Discovery system from a signal amplification tool into a closed-loop autonomous research assistant. The gap-driven approach ensures that new papers are selected based on epistemicneed rather than keyword matching, while the reasoning logger provides the transparency required for scientificreproducibility.
David Tom Foss (Thu,) studied this question.