Alignment of Agentic Systems
Year: 2026
Type: preprint
Abstract: In this article, we present a novel bottom-up approach to aligning agentic systems. Additionally,we introduce a training algorithm called projective learning, inspired by how the brain likely learns.To start, we describe two seemingly universal neuronal models, capable of modeling a broad rangeof phenomena, with the aim of modeling consciousness in our systems.We then develop projective learning, a mechanism that utilizes the simple action of strengtheningrepeated connections and pruning neglected ones to form new concepts. Later we illustrate howprojective learning can also be used for finding causes.In the last and most central section, we outline a neural architecture for generating preference-based actions through a few tunable weights.
Author: Fossheim, Håkon
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225558116