Same structure, different words

A computer scientist develops temporal difference learning. Decades later, a neuroscientist discovers dopamine neurons fire in the same pattern. Same mathematical structure. No shared vocabulary. Nobody noticed for years.

This keeps happening across fields that share no terminology but describe overlapping structures. The question driving this project: can that recognition stop being accidental?

Analytical intelligence is becoming infrastructure. When cognitive capability is abundant, the bottleneck moves from generating answers to verifying them. Every AI breakthrough in science credits the verifier. Cross domain scientific comparison has had no verifier.

Anuja Khatri. The Consilience started from a precise question: when two fields independently describe the same structural pattern in entirely different vocabularies, is there a way to check computationally whether the correspondence is real or projected? No existing tool could answer it. Embedding search found vocabulary matches. Language models produced eloquent confirmations. Neither could show where the correspondence breaks, where two descriptions actually disagree, or whether the disagreement is testable.

So she built the instrument. A neurosymbolic verification engine where models handle reading and deterministic code handles all judgment. Typed subgraph isomorphism for structural matching. A four rung grading ladder where every verdict carries a ceiling on what it does not license. A versioned instrument capability map that checks whether each disagreement is testable with real equipment. Published rejections with structural reasons. An architecture designed so that no language model ever gets the last word on any scientific verdict.

One person. One year. The engine blind tested against an international expert collaboration and matched what eleven laboratories found over nineteen months.