A survey of multimodal agentic frameworks has been published on arXiv. These systems combine large language models with diverse modalities such as images, audio, and video to improve their real-world applicability. The authors analyze the impact of multimodality across various functional modules and review existing work through a modality-centric taxonomy. They also discuss performance and efficiency-scalability trade-offs in different application domains. --- Why it matters: This research matters because it aims to identify key gaps and chart a roadmap toward robust and general-purpose intelligent systems that can integrate multiple modalities and perform complex tasks. Source: https://arxiv.org/abs/2608.20379