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arXiv:2606. 12289v1 Announce Type: cross Abstract: As Artificial Intelligence models grow in complexity, interpretability has become an indispensable tool for understanding, debugging, and controlling their computations.
arXiv:2508.08966v2 Announce Type: replace Abstract: The attention mechanism lies at the core of the transformer architecture, providing an interpretable model-internal signal that has motivated a gro...
arXiv:2510. 12201v2 Announce Type: replace Abstract: As AI becomes more common in everyday living, there is an increasing demand for intelligent systems that are both performant and understandable.
arXiv:2512. 21066v4 Announce Type: replace Abstract: Explainable artificial intelligence (XAI) reveals how explanatory variables relate to a response variable, yet communicating XAI outputs to laypersons remains difficult, limiting trust in AI-based predictions.
arXiv:2608.21449v1 Announce Type: new Abstract: Time series arise in a wide range of application domains and are analyzed using machine learning in decision-critical settings. Time series classificat...
Enterprise Representation Simplification (ERS) proposes reducing unnecessary representational complexity in enterprise information while preserving essential data within a defined scope. The paper introduces Enterprise Representation Complexity (ERC), a representation‑neutral model that measures complexity across four dimensions—Objects, Interactions, Behaviors, and Supporting Sources—at both representation and task levels. ERC enables comparison of architectural simplification versus retrieval optimization, supports an economic model of maintenance costs, and demonstrates that lower task‑level ERC can improve AI reasoning accuracy, as shown in Text‑to‑SQL research.