arXiv:2509.12760v5 Announce Type: replace Abstract: We introduce the Similarity-Distance-Magnitude (SDM) activation function, a more robust and interpretable formulation of the standard softmax activation function, adding Similarity (i.e., correctly predicted depth-matches into training) awareness and Distance-to-training-distribution awareness to the existing output Magnitude (i.e., decision-boundary) awareness, and enabling interpretability-by-exemplar via dense matching. We further introduce the SDM estimator, based on a data-driven partitioning of the class-wise empirical CDFs via the SDM
The continuous push for more robust, interpretable, and safer AI systems drives the development of novel activation functions like SDM.
This new activation function could improve the reliability and transparency of AI models, which is crucial for their deployment in sensitive applications and for building public trust.
The interpretability-by-exemplar via dense matching may lead to more explainable AI decisions, moving beyond black-box models.
- · AI safety researchers
- · Developers of explainable AI systems
- · Industries requiring high-assurance AI
- · Developers relying solely on traditional softmax models
AI models become more transparent, allowing better debugging and validation.
Increased adoption of AI in regulatory-heavy sectors due to improved interpretability and robustness.
New standards for AI model transparency emerge, impacting AI development and deployment practices globally.
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