There's a critical difference in how AI search methods handle information reliability. Standard models tend to generate fabricated scholarly content—a well-known limitation. Deep search approaches made some progress in addressing this, but the real breakthrough comes from anchoring data queries directly to verified scholarly articles, integrating MCPs and plugins that source exclusively from academic materials, then performing synthesis only from those curated sources. This methodology eliminates the hallucination problem at its root by establishing a verifiable source foundation before any synthesis occurs.