Seshat AI
Unravel the Rationale
Standing on the shoulders of giants' CoT.
Breakthroughs rarely start from scratch. Coulomb did not rediscover electricity from first principles — he borrowed Newton's chain of thought about gravity, and the inverse-square law followed. Holland did not invent search from nothing — he borrowed Darwin's chain of thought about natural selection, and genetic algorithms followed. What transfers between fields is not the answer, but the reasoning that produced it.
Isaac: A Multi-domain Knowledge-inspired Logical Reasoning Framework
Isaac gives models the same advantage. At its core is an ultra-large multi-domain CoT base — a growing library of reasoning chains distilled from finance, science, coding, healthcare, law, education, robotics, etc. When a hard problem arrives, a rationale matching module searches this base for chains of thought that share the same underlying logic, distills them into compact inspirations, and injects them into the foundation model at inference time. The model keeps its own weights and its own voice; what changes is that it no longer reasons alone, but on the shoulders of every domain that has already solved a problem of the same shape.
How good are we?
MetaEvo lifts models it never touched — same weights, same decoding, just better inspiration at inference time.
