[2603.04741] CONE: Embeddings for Complex Numerical Data Preserving Unit and Variable Semantics

Computer Science > Artificial Intelligence arXiv:2603.04741 (cs) [Submitted on 5 Mar 2026] Title:CONE: Embeddings for Complex Numerical Data Preserving Unit and Variable Semantics Authors:Gyanendra Shrestha, Anna Pyayt, Michael Gubanov View a PDF of the paper titled CONE: Embeddings for Complex Numerical Data Preserving Unit and Variable Semantics, by Gyanendra Shrestha and 2 other authors View PDF HTML (experimental) Abstract:Large pre-trained models (LMs) and Large Language Models (LLMs) are typically effective at capturing language semantics and contextual relationships. However, these models encounter challenges in maintaining optimal performance on tasks involving numbers. Blindly treating numerical or structured data as terms is inadequate -- their semantics must be well understood and encoded by the models. In this paper, we propose CONE, a hybrid transformer encoder pre-trained model that encodes numbers, ranges, and gaussians into an embedding vector space preserving distance. We introduce a novel composite embedding construction algorithm that integrates numerical values, ranges or gaussians together with their associated units and attribute names to precisely capture their intricate semantics. We conduct extensive experimental evaluation on large-scale datasets across diverse domains (web, medical, finance, and government) that justifies CONE's strong numerical reasoning capabilities, achieving an F1 score of 87.28% on DROP, a remarkable improvement of up to 9.3...