arXiv:2602.08597v3 Announce Type: replace 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 data
This paper addresses a critical challenge in AI development concerning the robustness and reliability of multimodal systems, an area of increasing academic and industrial focus.
Improving multimodal integration robustness directly impacts the reliability and deployability of advanced AI systems in real-world, uncertain environments, particularly for agentic applications.
The proposed attention mechanism, grounded in Global Workspace Theory, offers a novel approach to build more resilient multimodal AI without relying solely on end-to-end co-adaptation.
- · AI researchers
- · Robotics sector
- · Autonomous systems developers
- · AI agents developers
- · Developers of brittle multimodal systems
More robust multimodal AI models become practical for deployment in diverse, unpredictable scenarios.
Increased adoption of multimodal AI in critical applications where reliability and noise tolerance are paramount.
Acceleration of research into cognitive architectures inspired by GWT for creating more human-like, adaptive AI agents.
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Read at arXiv cs.AI