Encode
Extract concepts, emotion, causality, salience, and uncertainty.
MindAssembly makes belief formation visible. Fork one synthetic mind, expose each copy to a different experience, then watch the same question produce two explainably different answers.
Curiosity 92 · Ambition 86 · Risk tolerance 50
Both future copies begin with the exact same memories, connection weights, and beliefs—a controlled counterfactual experiment.
A weighted cognitive graph turns experiences into episodic traces, co-activated ideas into connections, and recurring patterns into revisable beliefs.
Extract concepts, emotion, causality, salience, and uncertainty.
Strengthen co-activated links with salience-weighted updates.
Compress recurring pathways while preserving contradictions.
Measure graph, belief, response, and decision divergence.
wᵢⱼ(t+1) = λwᵢⱼ(t) + η · activationᵢ · activationⱼ · salienceA deterministic graph simulation in this prototype; designed for future LLM experience encoding.MindAssembly helps students explore how experience shapes belief and gives AI builders a transparent way to audit personalization. Every change carries provenance: what happened, which links changed, and why a decision followed.