Attentionor

Attentionor is a term sometimes used in theoretical discussions of attention-based computation to denote a primitive or module that selects among competing inputs to determine which should receive processing resources. The name blends attention with the idea of a selector or gate, implying a mechanism that can steer information flow in a neural architecture.

Concept and operation. An attentionor would assign context-sensitive weights to input streams and route activations accordingly, acting as a differentiable gate. In practice, it resembles an attention mechanism or a gating unit, but with an emphasis on selection among alternatives rather than continuous weighting alone. It could be implemented via attention scores, soft or hard selection, or as a learned controller within a modular network, potentially working alongside transformers, recurrent units, or multimodal fusion components.

Potential applications. The idea is relevant to problems requiring dynamic resource allocation, such as long-range dependency modeling, efficient inference, or interpretable decision making. By emphasizing relevant signals while suppressing noise, attentionor concepts aim to improve accuracy, efficiency, and transparency in model behavior.

Relation to existing concepts. Attentionor is not a standard architectural term in mainstream literature; rather, it functions as a conceptual shorthand for planning selective routing mechanisms. In many discussions, it is used to illustrate how attention can be extended beyond weighting to granular control of information flow.