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arXiv · 2605.16401

CADS: Conformal Adaptive Decision System for Cost-Efficient Image Classification

Mikael Turkoglu·Tim Bary·Vincent Thielens·Manon Dausort·Benoît Macq
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Abstract

While high-capacity AI models have advanced state-of-the-art performance, their practical deployment is often hindered by high inference costs, environmental impact, and a "one-size-fits-all" approach that ignores varying sample complexity. In clinical settings for instance, the waste of computational resources on routine cases is a significant barrier to sustainable AI. In this paper, we introduce the Conformal Adaptive Decision System (CADS), a sequential multi-model algorithm designed to optimize resource allocation by efficiently sampling models based on the estimated data complexity. CADS leverages conformal prediction to quantify image uncertainty at runtime. CADS provides a mathematically grounded framework for balancing the cost-accuracy dilemma that dynamically routes samples through a model cascade, ranging from lightweight "Scout" models to high-capacity "Oracle" architectures. Validated on two datasets, CADS demonstrated superior efficiency and accuracy at a computational cost that can be up to 12 times lower than heavy-model inference. By accurately routing samples based on real-time complexity, CADS ensures high diagnostic reliability while drastically reducing the economic and environmental footprint of AI.

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Mikael Turkoglu, Tim Bary, Vincent Thielens, Manon Dausort, Benoît Macq. 2026-05-12. CADS: Conformal Adaptive Decision System for Cost-Efficient Image Classification. https://arxiv.org/abs/2605.16401

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