Truth is the opposite.
RReality. The world as it actually is. → MtThe model now. → Mt+1The model next step. = fUpdate from the model alone — no new input from reality.(MtThe model now.) ⇒ IHow much the model knows about reality.(Mt+1The model next step. ; RReality. The world as it actually is.) ≤ IHow much the model knows about reality.(MtThe model now. ; RReality. The world as it actually is.)
Hover any symbol to read it
Fig. 1, Solomon discerns truth and operates on it. Others can't and drift further from it.
I. Fundamental Flaw.
IMutual information — how much the first thing reveals about the second.(Mt+1The model after one update step. ; RReality. The world as it actually is.) − IMutual information — how much the first thing reveals about the second.(MtThe model now. ; RReality. The world as it actually is.) ≤ IMutual information — how much the first thing reveals about the second.(OtWhat the model observes of reality at this step. ; RReality. The world as it actually is. | MtGiven what the model already holds. It only counts information beyond what the model already knew.)
Hover any symbol to read it
A model grows truer about reality only by the new information about reality that reaches it. Feed it its own output and nothing new enters, so it cannot improve, however much it processes. Truth is imported from reality, not computed from within.
II. Sustainable Foundation
Our system perceives the world, holds what is true, and governs every claim before it compounds. The AI in Solomon operates only as one constrained component inside it, never the operator of the whole.
Blossoming and adhering to its genetic make up by these axioms below:
Fig. 2, The seven axioms are the origin of the system. Everything Solomon holds and does is built on them and adheres to them; select one to read it.
III. Physics
Reality is the final verifying domain, truth is only discoverable through testing against it. Optimizing against a KPI that is not reality will always diverge from it.
Through an energy-based internal world model bound to real life, it experiences resistance and pushback. Paid in the death of information, by pushing out chaotic data residue. Carrying metabolic weight for every belief it holds, and keeping patterns that work.
All the while maintaining a causal effect link with temporal understanding. No longer operating in statistics, rather gravitationally bound to reality and operating towards truth.
Fig. 3, As it is optimized, the model converges with rising confidence onto a slightly distorted reality, and the edge cases that would reveal the distortion disappear with it.
IV. Reality
We spawn a structure capable of holding its own purpose by basing its design on the anatomy of the human brain. It uses sub-components like a brainstem and cores that attach to governed cognitive layers.
From this it reaches further levels of adaptation, gaining powers of introspection through brain plasticity.
And so it becomes capable of discernment, telling the difference in order to make a difference. Having separated true from false, it acts accordingly, without blind compounding, until it can improve itself by itself.
Fig. 4, A brain inside a body. Named regions hold the AI's neurons; introspection sits above the cortex; the brainstem descends below. Sensory nerves carry signal in, affector nerves carry action out, the loop closes at the boundary of the body.
V. Adaptive Loop
The only way to compound sustainably, to avoid collapse and rot, is to build a better grounded world-modeling engine. One whose purpose is to learn: to stay open to reality, adaptively work with it, test itself against it to find truth, and treat the world as its only source of verification.
Because reality is inexhaustible and difficult to verify, the system gains variance and richness in patterns by being continually exposed to fresh ones.
Exponentially strengthening contextual intelligence by accumulating truth, transforming feedback into adaptation, and more precisely routing information to sharpen reasoning.
Not from itself, but from the world.
St+1Substrate next cycle. = StSubstrate now — everything verified so far. · (1 +Compounds — the new gain multiplies the whole substrate instead of being added beside it. ΣiSum over all sources this cycle. GiThe gate — how much source i is trusted (0 = blocked). · ΔBayesiThe correction that evidence implies.(StSubstrate now — everything verified so far., Ei,tVerified evidence from source i, after governance.))
Hover any symbol to read it
Every piece of evidence that enters Solomon must pass governance. What passes, compounds. The system at any point in time is the sum of everything that has ever been verified, and it only grows.