GRAIL: Gini/Graph-Routed Adaptive, Interpretable Learning for Accurate and Efficient Tabular & Mixed-Modality Inference
Year: 2025
Type: article
Abstract: I present , a neural architecture that couples an oblique decision router regularized by Gini impurity, a graph-aware feature router, and calibrated early exits for efficient inference. That yields rule-like decision paths and cluster-level rationales while remaining competitive on accuracy with millisecond-level CPU latency. We provide a practical training recipe---stability-safe Gini annealing, route-consistency regularization, knowledge distillation from strong tabular teachers, and validation-calibrated early exits---that delivers strong accuracy without sacrificing transparency. On standard tabular benchmarks we observe competitive accuracy with sub-millisecond p50 latency, near-one-hot routing purity, and human-readable explanations.
Author: K K, Prasanth
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7083630470
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