arXiv Machine Learning By Xuemin Yu, Ankur Garg, Samira Ebrahimi Kahou, Hassan Sajjad

Vector Quantized Latent Concepts: A Scalable Alternative to Clustering-Based Concept Discovery

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arXiv:2602. 02726v2 Announce Type: replace Abstract: Large language models (LLMs) encode rich semantic information in their hidden states, yet it remains difficult to understand what information these internal representations capture.

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arXiv Machine Learning
Aug 26

How to Achieve the Intended Aim of Deep Clustering Now, without Deep Learning

The paper examines whether Deep Embedded Clustering (DEC) truly overcomes the fundamental limitations of k‑means clustering, such as handling clusters of arbitrary shapes, varied sizes, and densities. Through analysis, it finds that DEC does not exploit the underlying data distribution and therefore fails to address these limitations. Instead, a non‑deep learning approach that leverages distributional information of clusters can achieve the intended goals of deep clustering.

By Kai Ming Ting, Wei-Jie Xu, Hang Zhang
arXiv AI
Jun 3

ReaLM: Residual Quantization Bridging Knowledge Graph Embeddings and Large Language Models

arXiv:2510. 09711v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have recently emerged as a powerful paradigm for Knowledge Graph Completion (KGC), offering strong reasoning and generalization capabilities beyond traditional embedding-based approaches.

By Wenbin Guo, Xin Wang, Jiaoyan Chen, Lingbing Guo, Zhao Li, Zirui Chen