dorsal/arxiv
View SchemaLLMs Meet Isolation Kernel: Lightweight, Learning-free Binary Embeddings for Fast Retrieval
| Authors | Zhibo Zhang, Yang Xu, Kai Ming Ting, Cam-Tu Nguyen |
|---|---|
| Categories | |
| ArXiv ID | 2601.09159vv2 |
| URL | https://arxiv.org/abs/2601.09159 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
Abstract
Large language models (LLMs) have recently enabled remarkable progress in text representation. However, their embeddings are typically high-dimensional, leading to substantial storage and retrieval overhead. Although recent approaches such as Matryoshka Representation Learning (MRL) and Contrastive Sparse Representation (CSR) alleviate these issues to some extent, they still suffer from retrieval accuracy degradation. This paper proposes \emph{Isolation Kernel Embedding} or IKE, a learning-free method that transforms an LLM embedding into a binary embedding using Isolation Kernel (IK). IKE is an ensemble of diverse (random) partitions, enabling robust estimation of ideal kernel in the LLM embedding space, thus reducing retrieval accuracy loss as the ensemble grows. Lightweight and based on binary encoding, it offers low memory footprint and fast bitwise computation, lowering retrieval latency. Experiments on multiple text retrieval datasets demonstrate that IKE offers up to 16.7x faster retrieval and 16x lower memory usage than LLM embeddings, while maintaining comparable or better accuracy. Compared to CSR and other compression methods, IKE consistently achieves the best balance between retrieval efficiency and effectiveness.
{
"annotation_id": "de6d8a48-cbe0-4a04-bc53-4a03bb59b6cc",
"date_created": "2026-02-17T05:53:20.075000Z",
"date_modified": "2026-02-17T05:53:20.075000Z",
"file_hash": "44ea4e5737f3d19e44b14742e022b76b4858f0c93c01d842cf7d3874d6c63a31",
"private": false,
"record": {
"abstract": "Large language models (LLMs) have recently enabled remarkable progress in text representation. However, their embeddings are typically high-dimensional, leading to substantial storage and retrieval overhead. Although recent approaches such as Matryoshka Representation Learning (MRL) and Contrastive Sparse Representation (CSR) alleviate these issues to some extent, they still suffer from retrieval accuracy degradation. This paper proposes \\emph{Isolation Kernel Embedding} or IKE, a learning-free method that transforms an LLM embedding into a binary embedding using Isolation Kernel (IK). IKE is an ensemble of diverse (random) partitions, enabling robust estimation of ideal kernel in the LLM embedding space, thus reducing retrieval accuracy loss as the ensemble grows. Lightweight and based on binary encoding, it offers low memory footprint and fast bitwise computation, lowering retrieval latency. Experiments on multiple text retrieval datasets demonstrate that IKE offers up to 16.7x faster retrieval and 16x lower memory usage than LLM embeddings, while maintaining comparable or better accuracy. Compared to CSR and other compression methods, IKE consistently achieves the best balance between retrieval efficiency and effectiveness.",
"arxiv_id": "2601.09159",
"authors": [
"Zhibo Zhang",
"Yang Xu",
"Kai Ming Ting",
"Cam-Tu Nguyen"
],
"categories": [
"cs.IR"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "LLMs Meet Isolation Kernel: Lightweight, Learning-free Binary Embeddings for Fast Retrieval",
"url": "https://arxiv.org/abs/2601.09159",
"version": "v2"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "ebec0f3c-674a-4d42-a1e3-ee4938df4bd2",
"id": "arXiv Dataset",
"type": "Model",
"variant": "snapshot-2026-01-17",
"version": "0.1.0"
},
"user_id": 1000002
}