We address vision-language-driven indoor navigation to assist visually impaired individuals in reaching a target location using images and natural language guidance. Traditional navigation systems are ineffective indoors due to the lack of precise location data. Our approach integrates vision and language models to generate step-by-step navigational instructions, enhancing accessibility and independence. We fine-tune the BLIP-2 model with Low Rank Adaptation (LoRA) on a manually annotated indoor navigation dataset. We propose an evaluation metric that refines the BERT F1 score by emphasizing directional and sequential variables, providing a more comprehensive measure of navigational performance. After applying LoRA, the model significantly improved in generating directional instructions, overcoming limitations in the original BLIP-2 model.

We introduce CAESAR, a new framework for scientific data reduction that stands for Conditional AutoEncoder with Super-resolution for Augmented Reduction. The baseline model, CAESAR-V, is built on a standard variational autoencoder with scale hyperpriors and super-resolution modules to achieve high compression. It encodes data into a latent space and uses learned priors for compact, information-rich representations. The enhanced version, CAESAR-D, begins by compressing keyframes using an autoencoder and extends the architecture by incorporating conditional diffusion to interpolate the latent spaces of missing frames between keyframes. This enables high-fidelity reconstruction of intermediate data without requiring their explicit storage. By distinguishing CAESAR-V (variational) from CAESAR-D (diffusion-enhanced), we offer a modular family of solutions that balance compression efficiency, reconstruction accuracy, and computational cost for scientific data workflows. Additionally, we develop a GPU-accelerated postprocessing module which enforces error bounds on the reconstructed data, achieving real-time compression while maintaining rigorous accuracy guarantees. Experimental results across multiple scientific datasets demonstrate that our framework achieves up to 10× higher compression ratios compared to rule-based compressors such as SZ3. This work provides a scalable, domain-adaptive solution for efficient storage and transmission of large-scale scientific simulation data.