{"type":"ScholarlyArticle","title":"Context-Adaptive World Models for Visual Out-of-Distribution Generalization","authors":[{"name":"Shubham Subhnil","slug":"shubham-subhnil","given":"Shubham","family":"Subhnil","affiliation":""}],"abstract":"A vision-based robot can be faced with changing friction, gravity, or motor behavior, whose effects are observable only from pixels mid-episode. Visual model-based reinforcement learning struggles in such settings because most world models assume the latent factors driving these changes stay fixed during interaction, or fold them into a single context embedding. My thesis is developing a context-adaptive Mamba-based world model that infers K context vectors from short pixel-trajectory windows. Each vector steers a disjoint slice of the Mamba backbone through block-diagonal feature-wise linear modulation (FiLM), so distinct dynamics signals travel along parallel pathways rather than collapse into a single channel. The policy trains on imagined rollouts conditioned on the inferred context, and I show that its suboptimality is bounded by √K times the context-inference error along a Lipschitz chain through FiLM, the SSM, and the decoder. Evaluation spans within-episode shifts on DeepMind Control (Walker, Quadruped) and mode/difficulty switching on Atari, against an inferred-context baseline and a privileged-context oracle.","doi":"10.24963/ijcai.2026/949","url":"https://ijcai.org/proceedings/2026/949","pdf":"https://ijcai.org/proceedings/2026/0949.pdf","markdown":"https://ijcai.org/proceedings/2026/949.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/949","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":949,"pages":{"first":8341,"last":8342},"keywords":["Machine Learning: Theory of deep learning","Machine Learning: Model-based and model learning reinforcement learning","Machine Learning: Reinforcement learning","Machine Learning: Representation learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}