Scientific discovery
Results a language model cannot produce on its own.
What can reasoning outside the model find that a summary hides?
We apply our reasoning algorithms to scientific questions. The reasoning is built outside the AI: it holds a whole field at once, keeps every claim with its source and can be checked step by step — so it finds what a fluent summary hides.
A language model predicts the most plausible next sentence. It cannot hold thousands of sources at once, keep what it concluded, show which steps are supported or give the same answer twice. Discovery needs all four.
Scientific discovery, in four points
A whole field at once
The claims of thousands of sources sit in one structure, so a question is answered against all of them — not against the few passages retrieval returned.
Contradictions no paper states
Two results that cannot both be true often live in different journals and vocabularies. The disagreement exists only between them, and it is shown with both sides.
Hypotheses with evidence attached
A hypothesis arrives as an explicit, sourced path with its weakest step named — reviewable in minutes instead of trusted or re-checked by hand.
The next experiment, on purpose
With competing hypotheses explicit, the system picks the experiment whose outcome would separate them most, and every check can be replayed.
Where it applies
Scientific discovery outside the language model
Why a model that predicts text cannot do discovery alone, and what reasoning outside it produces: a whole field at once, checkable steps, replayable results.
Read the article →The contradictions no paper states
Disagreements and hidden links that exist only between papers — found with the sources and conditions on both sides, where a summary would say “mixed results”.
Read the article →Hypotheses you can check step by step
Drug repurposing and target ranking as explicit, sourced paths with the weakest step named — and the next experiment chosen to separate competing ideas.
Read the article →GitMir publishes what its work produces and how it is measured. It does not publish the representation or the algorithms behind this line. This is work in progress: when there is a result worth reporting, it will appear here as a measurement, with method, scope and limitations.