We address the problem of few-shot room impulse response (RIR) interpolation, where the goal is to estimate the RIR at an arbitrary receiver location given a small set of location-labeled reference RIRs from that same room. Naïve signal-domain methods, such as direct interpolation in the time domain, often produce inaccurate results. To overcome this, we leverage an audio variational autoencoder (VAE) to map RIRs into a compact and smooth latent space that is amenable to interpolation. We explore both training-free and network-based approaches in this latent domain, including distance-based kernel regression and geometry-informed attention pooling. Our results show that simple kernel methods serve as strong baselines, while lightweight neural networks further improve performance. We present a promising, flexible latent-domain framework for few-shot RIR interpolation and provide insight into the interpolation behavior of latent representations across diverse acoustic conditions.