The recent advancements in reconfigurable intelligent surfaces (RIS) and cell-free massive MIMO systems are significantly shaping the trajectory of 6G networks. Innovations are primarily focused on enhancing communication reliability, throughput, and energy efficiency under challenging conditions such as dynamic blockages, beam misalignment, and electromagnetic interference. Novel techniques integrating RIS with UAVs and multi-agent reinforcement learning are being developed to mitigate these challenges, offering robust solutions for maintaining high data rates and reliability. Additionally, the use of simultaneous transmitting and reflecting RIS (STAR-RIS) in cell-free massive MIMO systems is showing promise in improving spectral efficiency despite interference and phase errors. The field is also witnessing a shift towards low-complexity channel state information acquisition methods, leveraging the channel-altering capabilities of multiple RISs to streamline the estimation process. These developments collectively underscore the need for adaptive, intelligent, and energy-efficient communication strategies to meet the demands of next-generation wireless systems.

Noteworthy papers include one proposing a mixed-criticality superposition coding scheme for RIS-assisted THz systems, significantly reducing queuing delays for critical data while maintaining high throughput. Another paper introduces a novel transmissive RIS-enabled distributed cooperative ISAC network, enhancing coverage and wireless environment understanding through a consensus ADMM framework. Lastly, a study on joint precoding and AP selection for energy-efficient RIS-aided cell-free massive MIMO using multi-agent reinforcement learning demonstrates an 85% enhancement in energy efficiency over conventional methods.