Serenity S.E.R.A — Semantic Experience Reasoning Agent: A Novel Architecture for Persistent Learning, Causal Memory, and Autonomous Agency

This paper presents Serenity, a personal AI agent framework built around the Neural Node Network (NNN) — a novel semantic memory architecture that encodes distilled experience as vectors in high-dimensional space, forms emergent abstractions through geometric centroids of co-activated concept bundles, and builds predictive world models from accumulated causal experience. Unlike retrieval-augmented generation systems that store and retrieve text, NNN stores meaning as position in semantic space, allowing genuine cross-domain generalisation to emerge from the geometry of co-activation rather than from explicit programming. Serenity wraps a pluggable language model in five integrated subsystems: a four-layer memory architecture, an autonomous heartbeat engine, a persistent emotional dynamics system, a reinforcement learning layer, and a sensory integration stack. The result is an agent that accumulates genuine understanding across sessions, pursues goals autonomously between interactions, and develops increasingly accurate predictive models of both its user and the world. This paper also documents a critical constraint identified through empirical testing: the language model itself is the primary bottleneck in the architecture, and proposes Low-Rank Adaptation Mixture-of-Experts (LoRA-MoE) specialist adapters as a future direction toward making Serenity model-agnostic without sacrificing capability. The full source code is available at https://github.com/Malicedp/serenity