KNWM: A Knowledge-Neuron Architecture for World Modeling

This paper introduces the Knowledge-Neuron World Model (KNWM), an explicit world model inspired by synaptic plasticity and neural signal transmission. KNWM organizes object properties, scientific laws, events, and problems using recursively nested knowledge neurons, stored in corresponding knowledge layers and connected within and across layers through shared knowledge elements. During learning, KNWM proposes candidate relations from changes in object properties over time and through interaction, and tests the influence of variables through active interventions in controlled simulations. Validated knowledge is stored and updated as new evidence becomes available. During problem solving, task-driven knowledge activation combines scientific computation with event simulation to support prediction, counterfactual reasoning, and multi-step planning. Basic knowledge and selected composite knowledge structures serve as semantically explicit signals, distinct from the text tokens of language models. Scientific knowledge neurons provide executable relations and a basis for calculation; event knowledge neurons describe state transitions and resource requirements; and problem knowledge neurons organize objectives and relevant knowledge. Controlled experiments demonstrate law learning, knowledge reuse, and numerical reasoning within planning, while revealing how knowledge validation affects reliability and task completion. By connecting knowledge accumulation with world simulation, KNWM offers an approach to building inspectable, compositional, and continually extensible world models.