overview

tru is the epistemic engine that turns collective attention into verifiable truth. It is the computation layer of cyber -- the system that takes raw cyberlinks from neurons and produces a shared picture of what matters.

what tru computes

Four quantities, every block, in consensus:

• focus per particle: the share of collective attention each particle holds, derived from the tri-kernel fixed point φ*
• cyberank per particle: the structural importance score, analogous to PageRank but computed through a composite operator of diffusion, springs, and heat kernel
• karma per neuron: the accumulated Bayesian Truth Serum score history, measuring how much genuine information each neuron has contributed over time
• syntropy of the whole cybergraph: the total information gain in bits -- how much more structured the graph is than random noise

the intelligence loop

The system operates as a feedback cycle:

1. A neuron observes the current state of the cybergraph
2. The neuron creates a cyberlink -- asserting a connection between two particles, staking on it, setting valence
3. The link enters the cybergraph, changing its topology
4. The tri-kernel recomputes: diffusion explores, springs enforce structure, heat kernel adapts
5. Cyberank produces a new fixed point φ* -- the updated collective focus
6. The neuron observes the result and links again

Every pass through this loop sharpens the graph. Links that attract attention accumulate focus. Links that the market disbelieves get suppressed through market inhibition. Neurons that consistently add signal accumulate karma, which amplifies their future contributions. Neurons that add noise see their influence diminish.

explicit knowledge and implicit knowledge

tru produces two layers of knowledge.

Explicit knowledge is what tru computes directly: the focus distribution, the cyberank scores, the karma balances, the syntropy measure. These are on-chain, verifiable, computable from the graph state by any observer.

Implicit knowledge is what neurons derive from observing the explicit layer. When a neuron sees that a particular particle has high focus, it learns something about collective belief. When it sees a cyberlink with a market price near zero, it learns the collective doubts that connection. When it sees a neuron with high karma, it learns that neuron has a track record of accurate signaling. This implicit layer is the living interpretation of the formal computation -- the meaning that emerges when agents act on what they observe.

why tru exists

Knowledge creation is a collective activity with a free-rider problem. Without a mechanism for attribution and reward, rational agents consume knowledge without contributing. The result is a tragedy of the epistemic commons: everyone reads, nobody writes, and the shared picture degrades.

tru solves this by making knowledge creation a provably rewarded activity. Every cyberlink that shifts the focus distribution -- that adds genuine structure to the graph -- earns its creator $CYB proportional to the shift. The reward is not assigned by committee or oracle. The neuron proves its own contribution via a stark proof and self-mints the reward.

The system converges toward truth because honesty is the dominant strategy. Bayesian Truth Serum makes accurate reporting the uniquely score-maximizing response. Karma compounds honest signaling into lasting influence. ICBS markets suppress false assertions through economic pressure. The result is a knowledge graph where the collective focus distribution φ* is the closest approximation to shared truth that the network can produce -- and it improves with every honest link.

see reference/rewards for the reward functions. see reference/epistemic-markets for the market mechanism. see reference/truth-scoring for the scoring layer. see reference/knowledge-economy for the full economic design.