The field of integrated sensing and communication is rapidly evolving, driven by the need for more comprehensive and accurate sensing capabilities in emerging applications such as autonomous driving and immersive services. A key trend in this area is the shift from single-modal to multimodal sensing, which enables the fusion of heterogeneous data from different sensors and modalities. This approach has been shown to improve sensing accuracy and robustness, particularly in complex and dynamic environments. Another important development is the use of advanced technologies such as large AI models, semantic communication, and multi-agent systems, which can help address the challenges of multimodal sensing and communication. Notably, innovative architectures and algorithms are being designed to efficiently fuse and process multimodal data, and to enable low-power and low-cost sensing solutions. Noteworthy papers include:

• Integrated Multimodal Sensing and Communication, which introduces several enabling technologies and architectural paradigms for multimodal ISAC.
• Dynamical Multimodal Fusion with Mixture-of-Experts for Localizations, which proposes a spatial-context aware dynamic fusion network for sub-meter 6G ISAC localization.
• MmBack, which presents a low-power, clock-free backscatter tag for synchronous multi-sensor data acquisition and multiplexing.
• MULTI-SCOUT, which presents a complete signal-processing chain for multistatic integrated sensing and communications in 5G and beyond.