The field of cooperative perception and vehicular communications is moving towards developing more robust and efficient methods for sharing and fusing information among agents. A key direction is the integration of prior knowledge and dynamic models to enhance perceptual accuracy and adapt to changing environments. Another important trend is the development of resilient communication systems that can operate under imperfect channel state information and other challenging conditions. Noteworthy papers in this area include: Fast2comm, which proposes a prior knowledge-based collaborative perception framework that effectively balances perception performance and bandwidth limitations. World Model-Based Learning for Long-Term Age of Information Minimization in Vehicular Networks, which introduces a novel world model-based learning framework to minimize packet-completeness-aware age of information in vehicular networks. Resilient Vehicular Communications under Imperfect Channel State Information, which proposes a two-phase framework to instill resilience into C-V2X networks under unknown imperfect channel state information. Coop-WD, which proposes a joint weighting and denoising framework to enhance cooperative perception subject to V2V channel impairments.