: Foundational AI architecture for building verifiable, persistent and revisable AI systems. Не: compliance application; business solver; замена ChatGPT; узкий отраслевой SaaS. Первые категории потенциальных партнёров: AI laboratories; sovereign AI infrastructure; robotics and autonomy teams; enterprise AI engineering organisations; research institutions...
: Adaptive resolution +0.0182 Recall@10 vs best L2 CI95 [+0.0022; +0.0388] Complexity control non-inferior to full arity with fewer states Persistent Atlas 6.857 → 6.357 candidate evaluations saving = 0.500 CI95 [+0.143; +0.929] utility unchanged Supporting domain evidence UMLS / Kinship external learning; morpheme learning; KOTE learning; Nations and phrase-level negatives retained...
We are not claiming that GALO is AGI or that it outperforms LLMs. We are claiming something more precise and already testable: GALO is a working prototype of a different AI architecture—one that adaptively selects the resolution of its reasoning, stores verified experience in explicit Atlases, and makes formal decisions replayable. Our external results now show that adaptive resolution can beat a fixed low-dimensional representation, and that persistent Atlas memory can reduce future search without reducing utility. Hub71 will allow us to turn that architecture into a pilot-ready foundational AI platform from Abu Dhabi...
: GALO Runtime Движок для: adaptive state resolution; formal route execution; explicit uncertainty; persistent Atlas memory; receipt-bound replay. GALO Atlas SDK SDK для разработки агентов, которым необходимы: долговременная явная память; несколько моделей ситуации; контролируемые revisions; проверяемые переходы; on-premise/private execution. GALO Agent API API, где внешняя LLM или parser может быть proposal/perception layer, но не authority:...
GALO is building a foundational AI architecture rather than a vertical application. Current AI systems are powerful at prediction and generation, but their authoritative reasoning state, persistent memory changes, competing hypotheses and revisions are often difficult to represent explicitly and replay deterministically. GALO introduces Atlas-native Verifiable Multipolar AI: a system that adaptively selects the minimum sufficient state model for a task, stores verified experience in persistent domain Atlases, preserves uncertainty explicitly and executes formal decisions through a frozen lawful kernel...
Сегодняшние AI-системы чрезвычайно сильны, но их рабочая модель ситуации, изменения памяти и причины пересмотра решения часто остаются неявными. GALO предлагает другую архитектуру. Он вычисляет, какая сложность представления действительно нужна задаче, хранит проверенный опыт в явных Атласах и воспроизводит каждое формальное решение через замороженное ядро. На внешних данных мы уже показали два узких эффекта: адаптивное разрешение...
