Validate each piece
Small scale, the same proof discipline as the rest of MOUHN: measure before announcing.
MH-AI is the AI architecture we're building toward genuine scientific reasoning: a model that keeps what it already knows while it learns more, uses its full capacity, runs real research today, and — the hardest part — knows when it doesn't know instead of guessing.
Some of this is measured and shipped. Some is today's roadmap. We label each one honestly instead of blurring the line — the same discipline as every proof page on this site.
Continual learning with zero deviation in prior knowledge — measured and reproduced across multiple domains.
Adaptive Core Experts — zero experts wasted during training, measured across model scales, integrated with DAS.
The law-discovery engine (mouhn_agentics) — not a demo, a real research tool that rediscovers published laws and finds real physics in real data.
Not a separate claim — the same evidence as Discovery, read for what it means: the engine abstains instead of confabulating (8 of 9 low-N/high-noise cells stay silent, a superconductor law correctly refused at held-out R²=0.58, zero forced fits across 11 real black-box benchmark sets). Confidence follows the proof, never the tone of the answer.
The next piece we're validating: a model that recognizes what it doesn't know and, instead of guessing, closes the gap itself — then resumes, without forgetting anything it already knew. Not yet measured at scale; we don't publish a number until we've proven it the way we proved DAS and ACE.
Durable weight-level memory — early research into the substrate under "no forgetting, ever." Not public yet.
The long-term goal is ambitious: scale this architecture to thousands of H100 GPUs, aiming for a system capable of investigating like a real scientist — not just answering questions.
We're in the small-scale validation phase — every piece proven on its own before it's combined, every result published here already measured, not projected.
Small scale, the same proof discipline as the rest of MOUHN: measure before announcing.
Thousands of H100 GPUs, the same architecture, without swapping out what's already validated.
A system that investigates, not just answers — MH-AI's end goal.
Absolute zero deviation in prior knowledge, measured and reproduced across multiple domains.
See DAS →Zero experts wasted during training, measured across model scales — integrated with DAS.
See ACE →The research engine, and the same proof behind the hallucination-reduction claim above.
See Discovery →