[2602.08597] An Attention Mechanism for Robust Multimodal Integration in a Global Workspace Architecture

Computer Science > Artificial Intelligence arXiv:2602.08597 (cs) [Submitted on 9 Feb 2026 (v1), last revised 30 Mar 2026 (this version, v2)] Title:An Attention Mechanism for Robust Multimodal Integration in a Global Workspace Architecture Authors:Roland Bertin-Johannet, Lara Scipio, Leopold Maytié, Rufin VanRullen View a PDF of the paper titled An Attention Mechanism for Robust Multimodal Integration in a Global Workspace Architecture, by Roland Bertin-Johannet and 3 other authors View PDF HTML (experimental) Abstract:Robust multimodal systems must remain effective when some modalities are noisy, degraded, or unreliable. Existing multimodal fusion methods often learn modality selection jointly with representation learning, making it difficult to determine whether robustness comes from the selector itself or from full end-to-end co-adaptation. Motivated by Global Workspace Theory (GWT), we study this question using a lightweight top-down modality selector operating on top of a frozen multimodal global workspace. We evaluate our method on two multimodal datasets of increasing complexity: Simple Shapes and MM-IMDb 1.0, under structured modality corruptions. The selector improves robustness while using far fewer trainable parameters than end-to-end attention baselines, and the learned selection strategy transfers better across downstream tasks, corruption regimes, and even to a previously unseen modality. Beyond explicit corruption settings, on the MM-IMDb 1.0 benchmark, we sh...