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Benchmarking PNW Model for MedMNIST to 100% Accuracy

arXiv 2604.18916 ↗First posted 20 Apr 2026cs.AIcited by 0

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Abstract (arXiv)

In this paper, we introduce a new concept called Artificial Special Intelligence by which Machine Learning models for the classification problem can be trained error-free, thus acquiring the capability of not making repeated mistakes. The method is applied to 18 MedMNIST biomedical datasets. Except for three datasets, which suffer from the double-labeling problem, all are trained to perfection.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 20 references that are in the map. First posted on arXiv 20 Apr 2026.

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References · 8

  1. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,509
  2. 2021MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationcited by 690
  3. 2024Rethinking model prototyping through the MedMNIST+ dataset collectioncited by 9
  4. 2025Benchmarking MedMNIST dataset on real quantum hardwarecited by 6
  5. 2025Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNISTcited by 5
  6. 2023Error-free Training for Artificial Neural Networkcited by 2
  7. 2026Toward Aristotelian Medical Representations: Backpropagation-Free Layer-wise Analysis for Interpretable Generalized Metric Learning on MedMNISTcited by 2
  8. 2025Toward Errorless Training ImageNet-1kcited by 1

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