UNITY Framework: Continuous Neural Field Architecture for Universal Multimodal Learning Without Attention or Recurrence

The UNITY Framework is a machine learning architecture designed to process multiple input modalities—including text, images, audio, and video—using a unified, token-free representation. Instead of relying on discrete tokens, global attention, or recurrent states, the system maps all data into a continuous spatiotemporal field where information evolves according to localized, learned interaction rules. The field is updated using a diffusion-plus-flow mechanism inspired by partial differential equations (PDEs). This update rule consists of two components: A local smoothing term, implemented as a discrete Laplacian operator, which propagates contextual information across neighboring points. A trainable flow function that adjusts field values based on content-specific patterns and local gradients. A streaming memory module is integrated into the architecture, maintaining non-decaying memory slots that persist indefinitely. These slots are updated selectively using a similarity-based gating function, allowing the system to recall semantically relevant information across arbitrarily long contexts without fixed memory decay. The framework’s universal encoders transform any modality into the continuous field space through modality-specific mathematical mappings, while universal decoders reverse this process to produce outputs in the original or target modality. This eliminates the need for separate architectures for different data types. Key benefits of the UNITY Framework include: Token-free processing, removing the bottleneck of discrete representations. Near-constant memory cost per processing step, enabling scaling to extremely long contexts. Native multimodality, allowing seamless integration of text, image, audio, and video streams within the same computational core. Hardware efficiency, making deployment feasible on low-resource devices without significant performance degradation. The invention’s core novelty lies in replacing attention-based global context computation with a localized, PDE-inspired update rule coupled to a persistent, content-aware memory system, resulting in transformer-level performance at lower computational and memory costs.