Systems and methods for latent hyperspace navigation in spatiotemporal media

A computer system for spatiotemporal media compression and navigation, comprising:

• a hardware memory, wherein the computer system is configured to execute software instructions stored on nontransitory machine-readable storage media that:
• obtain a plurality of spatiotemporal media input data sets comprising video data organized as three-dimensional tensors;
• compress the input data sets into compressed mini-Lorentzian representations using a plurality of Lorentzian autoencoders that preserve tensor structure and spatiotemporal relationships through three-dimensional convolutional operations;
• establish a latent hyperspace with geometric manifold structure organizing the compressed representations as navigable geodesic trajectories;
• compute optimal navigation paths through the latent hyperspace using differential geometry principles that solve geodesic equations accounting for manifold curvature;
• position symbolic anchors at locations within the latent hyperspace determined by content analysis algorithms to identify at least one of decision points, semantic boundaries, navigation waypoints, and temporal references;
• implement spatiotemporal routing protocols configured to coordinate navigation decisions across multiple temporal scales;
• train a correlation network using sets of cross-correlated training data sets;
• decompress the compressed data sets using a plurality of three-dimensional convolutional decoders corresponding to the plurality of Lorentzian autoencoders;
• and
• restore data lost during compression using the trained correlation network to generate output data sets;
• cache successful navigation strategies by extracting navigation patterns and storing them as reusable templates;
• and
• generate synthetic content during navigation using generative algorithms to support exploration beyond original media boundaries while maintaining temporal consistency across consecutive frames.