GAIA: General Artificial Intelligence Architecture (UniversalPartitionNet) A Universal Text-to-Vector Network with π-Driven Partition Regularization

GAIA (General Artificial Intelligence Architecture) is an alternative to Transformers, built on the UniversalPartitionNet framework with word hashing and π-driven partition regularization. Unlike Transformers and RNNs, GAIA eliminates costly self-attention and trainable tokenizers, enabling a lightweight, end-to-end, and universal model. Experiments show GAIA can be trained in seconds on CPU using standard text classification datasets (e.g., AG News), reaching competitive accuracy with throughput in the thousands of examples per second. Thanks to its linear scalability and emergent properties from controlled hashing collisions, GAIA can serve as a universal building block for NLP, vision, audio, and multimodal learning, offering a simpler, faster, and more flexible alternative to the Transformer paradigm.