# Cognitive Engine — Behavioral, Graph & Predictive Modeling The Cognitive Engine is the analytical heart of DeciAI, where raw signals become market understanding. It leverages a suite of model families optimized for behavior prediction, network inference, and capital topology reconstruction. The goal is not merely to classify activity but to interpret and forecast intent. The engine operates along three modeling axes:\ (1) Sequence modeling for wallet behavior;\ (2) Graph modeling for wealth networks;\ (3) Density modeling for liquidity and risk fields. Together, these axes provide a deeply structural perspective on token ecosystems, enabling DeciAI to anticipate capital movement before it becomes visible in markets. #### Model Families Used in the Cognitive Engine * Sequence Models Detects accumulation/exit cycles, persistence patterns, conviction signals, timing rhythm, and mobility triggers. * Graph Neural Models Reconstructs holder networks, identifies influence clusters, maps centrality, and detects fragility or concentration points. * Liquidity Pressure Models Measures stress in pools, simulates slippage sensitivity, and projects capital absorption or outflow potential. * Anomaly Detection Systems dentifies statistically improbable behaviors across holders, clusters, or liquidity channels. * Predictive Intent Models Estimates the probability that a cohort will accumulate, rotate, hedge, or exit in the next time horizon. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.deciai.vip/system-architecture/cognitive-engine-behavioral-graph-and-predictive-modeling.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.