From Signal Amplification to Autonomous Discovery: The Sovereign Gap-Driven Knowledge Engine

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.