@inproceedings{gan-etal-2026-cortex,
title = "{CORTEX}: High-Quality Cross-Domain Organization of Web-Scale Corpora through Ontological Corpus Graph",
author = "Gan, Chengtao and
Guo, Xiaoke and
Zhu, Yushan and
Gong, Zhaoyan and
Liu, Zhiqiang and
Li, Songze and
Chen, Huajun and
Zhang, Wen",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.985/",
pages = "22700--22724",
ISBN = "979-8-89176-459-0",
abstract = "The continuous evolution of large language models drives escalating demands on data scale and quality, and as different training stages impose increasingly tailored data requirements, systematic organization of high-quality corpora becomes indispensable. Existing corpus construction pipelines confine the resulting corpora to flat, undifferentiated document collections, universally lacking systematic knowledge organization. We present $\textbf{CORTEX}$, to our knowledge the first framework that elevates web-scale corpus construction from flat document filtering to structured knowledge organization through an $\textbf{Ontological Corpus Graph (OCG)}$, a three-layer heterogeneous structure unifying a quality-refined content layer, a hierarchical lightweight ontology layer via LLM-driven automated evolution, and a cross-domain alignment layer enabling inter-domain association at arbitrary taxonomic resolution. Comprehensive experiments confirm the effectiveness of CORTEX. In particular, we leverage the OCG to synthesize CORTEXBench, a cross-domain search-and-reasoning benchmark whose evaluation across eight frontier LLMs validates the effectiveness of quality refinement, domain organization, and cross-domain data synthesis. We will publicly release the complete codebase, a 24.14B-token refined corpus with its OCG, and CORTEXBench."
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<abstract>The continuous evolution of large language models drives escalating demands on data scale and quality, and as different training stages impose increasingly tailored data requirements, systematic organization of high-quality corpora becomes indispensable. Existing corpus construction pipelines confine the resulting corpora to flat, undifferentiated document collections, universally lacking systematic knowledge organization. We present CORTEX, to our knowledge the first framework that elevates web-scale corpus construction from flat document filtering to structured knowledge organization through an Ontological Corpus Graph (OCG), a three-layer heterogeneous structure unifying a quality-refined content layer, a hierarchical lightweight ontology layer via LLM-driven automated evolution, and a cross-domain alignment layer enabling inter-domain association at arbitrary taxonomic resolution. Comprehensive experiments confirm the effectiveness of CORTEX. In particular, we leverage the OCG to synthesize CORTEXBench, a cross-domain search-and-reasoning benchmark whose evaluation across eight frontier LLMs validates the effectiveness of quality refinement, domain organization, and cross-domain data synthesis. We will publicly release the complete codebase, a 24.14B-token refined corpus with its OCG, and CORTEXBench.</abstract>
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%0 Conference Proceedings
%T CORTEX: High-Quality Cross-Domain Organization of Web-Scale Corpora through Ontological Corpus Graph
%A Gan, Chengtao
%A Guo, Xiaoke
%A Zhu, Yushan
%A Gong, Zhaoyan
%A Liu, Zhiqiang
%A Li, Songze
%A Chen, Huajun
%A Zhang, Wen
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F gan-etal-2026-cortex
%X The continuous evolution of large language models drives escalating demands on data scale and quality, and as different training stages impose increasingly tailored data requirements, systematic organization of high-quality corpora becomes indispensable. Existing corpus construction pipelines confine the resulting corpora to flat, undifferentiated document collections, universally lacking systematic knowledge organization. We present CORTEX, to our knowledge the first framework that elevates web-scale corpus construction from flat document filtering to structured knowledge organization through an Ontological Corpus Graph (OCG), a three-layer heterogeneous structure unifying a quality-refined content layer, a hierarchical lightweight ontology layer via LLM-driven automated evolution, and a cross-domain alignment layer enabling inter-domain association at arbitrary taxonomic resolution. Comprehensive experiments confirm the effectiveness of CORTEX. In particular, we leverage the OCG to synthesize CORTEXBench, a cross-domain search-and-reasoning benchmark whose evaluation across eight frontier LLMs validates the effectiveness of quality refinement, domain organization, and cross-domain data synthesis. We will publicly release the complete codebase, a 24.14B-token refined corpus with its OCG, and CORTEXBench.
%U https://aclanthology.org/2026.emnlp-main.985/
%P 22700-22724
Markdown (Informal)
[CORTEX: High-Quality Cross-Domain Organization of Web-Scale Corpora through Ontological Corpus Graph](https://aclanthology.org/2026.emnlp-main.985/) (Gan et al., EMNLP 2026)
ACL
- Chengtao Gan, Xiaoke Guo, Yushan Zhu, Zhaoyan Gong, Zhiqiang Liu, Songze Li, Huajun Chen, and Wen Zhang. 2026. CORTEX: High-Quality Cross-Domain Organization of Web-Scale Corpora through Ontological Corpus Graph. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 22700–22724, Budapest, Hungary. Association for Computational Linguistics.