The evolution from image-focused MLLMs to LLMs with the ability of video understanding (Vid-LLMs) advances multimodal reasoning and addresses challenges such as temporal dynamics and long-range context dependencies. Recent models in this area include Video-LLaMA (Zhang et al., 2023) and Video-ChatGPT (Maaz et al., 2023). According to Tang et. al, Vid-LLMs deal with three main tasks: abstract understanding, temporal understanding and spatiotemporal understanding (Tang et al., 2023, pp. 4–5). Currently, the main challenge lies in understanding long context video data (Tang et al., 2023, pp. 13–14, Yin, et al., 2023, p. 13).