3 papers
cs.AI2026
Working Paper: Towards a Category-theoretic Comparative Framework for Artificial General Intelligence
Pablo de los Riscos, Fernando J. Corbacho, Michael A. Arbib
AGI has become the Holly Grail of AI with the promise of level intelligence and the major Tech companies around the world are investing unprecedented amounts of resources in its pu…
cs.AI2026
Working Paper: Towards Schema-based Learning from a Category-Theoretic Perspective
Pablo de los Riscos, Fernando J. Corbacho, Michael A. Arbib
We introduce a hierarchical categorical framework for Schema-Based Learning (SBL) structured across four interconnected levels. At the schema level, a free multicategory $Sch_{syn}…
cs.AI2026
Working Paper: Active Causal Structure Learning with Latent Variables: Towards Learning to Detour in Autonomous Robots
Pablo de los Riscos, Fernando J. Corbacho
Artificial General Intelligence (AGI) Agents and Robots must be able to cope with everchanging environments and tasks. They must be able to actively construct new internal causal m…