collaborators

5 papers

cs.LG2026

Generalised Bellman recurrence and three dualities in sequential decision-making

Fernando E. Rosas, David Hyland, Daniel Polani

What gives the Bellman equation its form? We show that the recursive properties of optimal value functions follow from three conditions: that the dynamics decomposes through suffic…

cs.AI2026

Safety from Honesty in a Disinterested AI Predictor

Yoshua Bengio, Oliver Richardson, Tomáš Gavenčiak +13

As AI systems become more capable, training procedures that optimize for downstream outcomes risk introducing implicit agency: goal-directed behavior that designers never specified…

cs.AI2026

From monoliths to modules: Decomposing transducers for efficient world modelling

Alexander Boyd, Franz Nowak, David Hyland +2

World models have been recently proposed as sandbox environments in which AI agents can be trained and evaluated before deployment. While realistic world models often have high com…

cs.LG2026

Learning POMDP World Models from Observations with Language-Model Priors

Valentin Six, Frederik Panse, Mathis Fajeau +7

Whether navigating a building, operating a robot, or playing a game, an agent that acts effectively in an environment must first learn an internal model of how that environment wor…

cs.AI2025

Possible Principles for Aligned Structure Learning Agents

Lancelot Da Costa, Tomáš Gavenčiak, David Hyland +5

This paper offers a roadmap for the development of scalable aligned artificial intelligence (AI) from first principle descriptions of natural intelligence. In brief, a possible pat…