# Toward Active World Modeling Presenter: Lancelot Da Costa Contribution: Pre-recorded Video presentation Public ID: talk-3642a535b44d3e9c ## Submitted abstract World models allow an agent to represent, predict, and act upon its environment. Yet humans, biological organisms, and artificial agents never observe the world directly: they encounter it only through a limited sensorimotor interface. How, then, can an agent acquire a useful model of a complex, partially observed world through active inference? In this talk, I outline a research program for active world modeling: the joint process of learning a model of the world and selecting actions that improve that model. I argue that four ingredients are central to this program. First, world-model learning should be formulated as probabilistic inference under uncertainty, coupled to actions that pursue expected free energy. Second, agents require sufficiently expressive, yet computationally searchable, spaces of possible world models. Third, sample-efficient learning depends on strong inductive biases — the core knowledge systems observed in human children. I illustrate these ideas with recent work on universal structured model spaces and language-model inductive biases in partially observable environments. Finally, I discuss the unresolved challenge of integrating these components into scalable agents capable of open-ended exploration and model discovery. Together, these results suggest a path toward understanding and emulating how humans and machines learn world models through active inference. Source: [submitted source]() Abstract SHA256: ae95e50ae8a5129b2b7cd8952a6a2e79d8d39a7c11c9642f37f59c6cdf0740ee ## Organizer review checklist - [ ] Confirm the submitted title, presenter, and abstract with the presenter. - [ ] Confirm contribution format and scheduling needs. - [ ] Confirm public permissions for any additional material before publication. - [ ] Prepare discussion questions grounded in the submitted abstract. ## Public learning resources These approved event resources are starting points; topic relevance and author identity have not been inferred. - Active Inference Ontology: https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology - Cognitive Agent Modeling: https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations - GEO-INFER: https://github.com/ActiveInferenceInstitute/GEO_INFER - Generalized Notation Notation: https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation - pymdp: https://github.com/infer-actively/pymdp - Reproducible publication template: https://github.com/docxology/template OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=Toward%20Active%20World%20Modeling