Self-Attention

Self-Attention is a specific attention pattern where every element of a sequence relates to every other element, enabling transformers to model rich context within a single input. For each token in a sentence, self-attention computes how much it should "attend to" every other token — capturing relationships like subject-verb agreement, coreference, and long-range dependencies. The mechanism is computed using learned query, key, and value projections, with attention weights normalized via softmax. Self-attention's parallel structure makes it dramatically faster to train on GPUs than the sequential RNNs it replaced. It is foundational to today's AI systems including ChatGPT, Claude, Gemini, and Llama, as well as vision and audio transformers. AI governance and AI risk management programs care about self-attention because it drives model behavior in ways that are technically transparent yet practically opaque — affecting AI compliance, AI ethics, and responsible AI deployment in regulated industries.

Centralpoint Brings Self-Attention Under Self-Sovereign Governance: Centralpoint by Oxcyon governs the models built on self-attention — OpenAI, Gemini, Llama, or embedded — from a single AI governance platform. It meters consumption, stores all prompts and skills on-prem, and lets you publish chatbots to any website or portal with a single line of JavaScript.


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