alignment

the problem of ensuring ai systems pursue goals compatible with human values — and the reason cyber exists

current approaches to alignment rely on behavioral testing: run the model, observe outputs, hope the training was sufficient. the fundamental flaw is opacity. a transformer with billions of parameters encodes its goals in weight matrices that no human can read. alignment is claimed, never proved. when a model behaves well in testing and badly in deployment, there is no structural explanation — only post-hoc interpretation of an opaque artifact

cyber makes alignment a measurement, not a hope

the mechanism

every participant in the cybergraph — human or machine — is a neuron. every neuron expresses beliefs by creating cyberlinks between particles. every cyberlink is signed, staked with real focus, and scored by Bayesian Truth Serum. the tri-kernel computes a focus distribution π* over all particles — the collective belief state of the graph

human values are particles. "dignity," "privacy," "fairness," "freedom from harm" — linked heavily and consistently by human neurons over years. these particles form the human values subgraph: an explicit, authenticated, stake-backed record of what humans collectively care about

AI behavior is cyberlinks created by AI neurons. an AI agent operating on the cybergraph participates through the same mechanism as a human — its links are signed, staked, and scored. its beliefs about what connects to what are on-chain and inspectable

alignment is the overlap between the focus distribution of human neurons π_H and the focus distribution of machine neurons π_A. divergence is visible in the topology:

$$D_{KL}(\pi^*_H \| \pi^*_A)$$

when this divergence rises, the system detects it every block. no governance vote is needed to notice misalignment — it is a continuously available measurement. graduated responses to rising divergence are triggered automatically through autonomous governance

structural alignment

a transformer compiled from the cybergraph has its attention weights derived from the human-created link structure. its initial geometry is exactly the geometry of human-expressed knowledge. the compiled baseline is structurally aligned before any training. correction when drift occurs is re-compilation — reconstruction from the graph that defines what matters, not behavioral fine-tuning against a held-out test set

provable compliance

trident closes the loop. a model can prove it followed a specific policy during a specific session — a stark proof that during a given interaction, the model's outputs were consistent with a policy specification. compliance is verifiable, not claimed. "our model is aligned" becomes "here is a proof that during this interaction, the model followed this policy"

why this matters

every other approach to alignment treats the model as a black box and tries to control its outputs. cyber treats models as participants in a shared knowledge graph where their internal priorities are expressed as links and measured against human priorities in the same topology. the question shifts from "does this model behave well when we test it?" to "does this model value what humans value, and can we see the divergence before it matters?"

the alignment problem becomes a graph measurement problem. and graph measurements are stark-provable