# Novel Approaches to Artificial Intelligence Development Based on the Nearest Neighbor Method

arXiv: https://arxiv.org/abs/2508.18953 · HTML: https://mlsanity.com/paper/2508.18953/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 39 references that are in the map.
- First posted on arXiv 26 Aug 2025.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Understanding deep learning requires rethinking generalization](https://mlsanity.com/paper/1611.03530/) (2016)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning](https://mlsanity.com/paper/1803.04765/) (2018)
- [Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Neural Networks](https://mlsanity.com/paper/2212.10822/) (2022)

## Abstract (arXiv)

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.