AegisMind Discover – cross-domain hypothesis generation from papers

I built a system that reads research papers across unrelated domains and tries to surface hypotheses that neither field would have generated on its own. The Discover page is where it publishes findings: https://aegismind.app/discoveries It's very early — only three discoveries so far — but the core idea is what I want feedback on. The problem it's trying to solve: Science is siloed. A breakthrough in mycology might have direct implications for network routing. A discovery in chronobiology might reframe how we think about database consistency. Nobody reads across all of it, and even when they do, the connection is usually accidental. How it works:

The autonomous "Right Brain" service ingests papers across domains continuously Multiple models (GPT, Claude, Gemini, Mistral, Grok) analyze papers in parallel A synthesis layer looks for structural or mechanistic similarities across domain boundaries Hypotheses are only published when they pass a novelty and coherence threshold — it won't surface things either field already knows

What I'm genuinely uncertain about and want feedback on:

How do you evaluate whether a cross-domain hypothesis is actually interesting vs. superficially pattern-matched? This is the hardest part of the novelty filter. Which domain combinations would you most want to see? (We're currently not curating this — it's whatever the system finds structurally similar.) Should findings link directly to the source papers?

Only three discoveries published so far, so the page is sparse — but I'd rather share the idea early than wait until it looks polished.