Seshat AI — Unravel the Rationale

Standing on the shoulders of giants' CoT.

Newton's law of gravitation inspiring Coulomb's law of electrostatics — Newton's CoT → Coulomb's law

Darwin's theory of evolution inspiring Holland's genetic algorithms — Darwin's CoT → Genetic algorithm

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?

Isaac lifts models it never touched — same weights, same decoding, just better inspiration at inference time.

HumanEval results: every model gains with Isaac over its zero-shot baseline — **HumanEval** — code generation, from 8B open models to frontier systems. Isaac adds up to +8.5 points, and still helps where the baseline is already above 95.

SciCode results: Isaac improves problem-pass rate across all evaluated models — **SciCode** — research-level scientific programming. Gains hold on the hardest split, where zero-shot pass rates sit in the single digits.

ScienceQA results: Isaac improves multimodal science question answering — **ScienceQA** — multimodal science questions. Text-only reasoning chains still transfer to image-grounded problems, adding up to +12.7 points.

StrategyQA results: Isaac improves multi-hop commonsense reasoning across 13 models — **StrategyQA** — implicit multi-hop reasoning. Chains distilled from other domains transfer here too, with gains up to +13.5 points.

What we offer?

Coming soon...