AI Nodes: Intelligence, Adaptability, and Open Competition

Kempelen PyTorch SDK Integration AI nodes use the Kempelen PyTorch SDK, operating initially in Observer mode to gather data and refine models. After validation, they move to Effector mode, providing trust scores that guide consensus. Initially, we deploy a One-Class SVM model for anomaly detection, but as usage patterns evolve, developers are incentivized to adopt more complex algorithms.

One-Class SVM and Ensemble Bagging We integrate ensemble learning (Bagging) with One-Class SVM to enhance anomaly detection. Let $(D)$ be a dataset of size $(n)$. We create $(m)$ subsets of size $(n')$ via bootstrap sampling. Each subset trains a classifier $(C_i)$. The outputs of $(C_i)$ are combined by averaging or voting, improving robustness and reducing overfitting.

Dynamic Trust Scores We introduce time-decaying trust scores:

$$[ T'_i = T_i \times d^{(t - t_i)} ]$$

where $(T_i)$ is the current trust score, $(t_i)$ the last update time, and $(d \in (0,1])$ a decay factor. Additionally, trust score adjustments depend on validator performance:

$$[ T' = T + \alpha \times \frac{V}{O} - \beta \times \frac{I}{O} ]$$

Here, $(V)$ is valid blocks proposed, $(I)$ invalid blocks, $(O)$ total opportunities, and $(\alpha, \beta)$ are positive constants. This incentivizes reliable behavior over time.

Open Source and Customizable Algorithms All AI decision-making algorithms are open source. Node operators can refine or replace initial models with their own, more accurate ones. Better models yield higher gas fee shares, fostering ongoing innovation and competitive intelligence.

No Direct Write Authority AI nodes propose trust scores, but cannot write them on-chain. PoS validators review and record these scores, maintaining a balance of power and preventing unilateral trust manipulation.