CAA

CAA training uses pairs of positive and negative examples to run an AI model; it captures internal activation levels at a certain layer; averaging those activations across all positives and negatives produces vectors; finally subtraction yields a "guidance vector" that reflects mathematically the distinction between good and poor performance. CCA training works with sets of positive and negative examples and runs these through the AI model. Capturing internal activations from a particular layer follows next. Averaging activations for all positives and negatives results in vectors; finally subtraction creates a guidance vector which is based on mathematics regarding distinctions among high

During inference, this steering vector is directly concatenated with activations from the corresponding layer; it gets multiplied by a strength factor and is especially targeted for the penultimate element of a sequence to guide future generation by the AI. Optionally such vector may also use L2 normalization to regulate its size and after learning process ends this resulting tensor file becomes straightforward to load and reuse immediately without further fine tuning.

Conceptually CAA is straightforward; it averages neuronal patterns and adds or subtracts them during inference and then combines them into a specific layer.