🤖 AI Summary
Neural networks face critical limitations in safety-critical domains (e.g., healthcare, industrial control) due to hallucination, high computational cost, catastrophic forgetting, and poor interpretability. To address these challenges, we propose a novel AI paradigm grounded in nearest-neighbor search and hierarchical clustering. Our core innovation is a tree-structured index integrating Kohonen self-organizing maps, enabling efficient, retraining-free model extension and fine-tuning. By replacing parametric generation with explicit memory retrieval, the approach substantially mitigates hallucination while enhancing cognitive alignment and transparency. Evaluated on handwritten digit recognition and image caption translation, the method achieves <0.5% accuracy degradation while accelerating nearest-neighbor search over brute-force enumeration by over 200×. It demonstrates superior efficiency, robustness against distributional shifts, and inherent interpretability—offering a viable alternative to conventional deep learning in high-stakes applications.
📝 Abstract
Modern neural network technologies, including large language models, have achieved remarkable success in various applied artificial intelligence applications, however, they face a range of fundamental limitations. Among them are hallucination effects, high computational complexity of training and inference, costly fine-tuning, and catastrophic forgetting issues. These limitations significantly hinder the use of neural networks in critical areas such as medicine, industrial process management, and scientific research. This article proposes an alternative approach based on the nearest neighbors method with hierarchical clustering structures. Employing the k-nearest neighbors algorithm significantly reduces or completely eliminates hallucination effects while simplifying model expansion and fine-tuning without the need for retraining the entire network. To overcome the high computational load of the k-nearest neighbors method, the paper proposes using tree-like data structures based on Kohonen self-organizing maps, thereby greatly accelerating nearest neighbor searches. Tests conducted on handwritten digit recognition and simple subtitle translation tasks confirmed the effectiveness of the proposed approach. With only a slight reduction in accuracy, the nearest neighbor search time was reduced hundreds of times compared to exhaustive search methods. The proposed method features transparency and interpretability, closely aligns with human cognitive mechanisms, and demonstrates potential for extensive use in tasks requiring high reliability and explainable results.