GD‑Attention: A Nonlinear Selection Mechanism with Unique Coherence\ Guarantees — Grounded in the Structure of Meaning
GD‑Attention: A Nonlinear Selection Mechanism with Unique Coherence\ Guarantees — Grounded in the Structure of Meaning
This paper introduces GD-Attention, a nonlinear selection mechanism derived from Ghost Drift theory. Unlike traditional Softmax attention that blends values probabilistically, GD-Attention deterministically selects a single coherent key via energy minimization. We mathematically prove the uniqueness of this selection based on a semantic energy landscape with strong convexity guarantees. This framework offers a new paradigm for attention mechanisms, emphasizing semantic integrity, non-additivity, and interpretability. This work was conducted at the GhostDrift Mathematical Institute (https://www.ghostdriftresearch.com).
Machine Learning, Semantic Energy Function, Non-additive Coherence, Artificial Intelligence, Attention Mechanisms, Ghost Drift Theory, Nonlinear Selection, GD-Attention, Natural Language Processing
Machine Learning, Semantic Energy Function, Non-additive Coherence, Artificial Intelligence, Attention Mechanisms, Ghost Drift Theory, Nonlinear Selection, GD-Attention, Natural Language Processing
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