arXiv Machine Learning July 27, 2026 By Miaobo Hu, Xiaobo Guo, Shuhao Hu, Bokun Wang, Rui Chen, Xin Wang, Daren Zha, Jun Xiao

SCOPE and SCION: A Benchmark and an Auditable Reference Pipeline for Schema Induction and Fusion from Text

Read the original on arXiv Machine Learning →

arXiv:2607. 21610v1 Announce Type: cross Abstract: Schema graphs are an upstream bottleneck of schema-grounded information extraction and knowledge graph construction, yet most extraction systems assume the schema is already available.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Aug 3

ExtractBench: A Benchmark for Schema-Guided Enterprise Document Extraction

arXiv:2607. 29677v1 Announce Type: new Abstract: Enterprise workflows increasingly rely on agents for \emph{schema-guided extraction}: given a document and a user-defined schema, the agent faithfully follows the schema to produce the correct output with source evidence as grounding metadata.

By Boyang Zhang, Adrian Lyjak, Eli Stewart, Zhaoqi Li, Simon Suo

llms agents benchmarks

arXiv AI

Jun 30

Database Context Compression for Text-to-SQL on Real-World Large Databases

arXiv:2606. 28601v1 Announce Type: cross Abstract: Recent progress in Text-to-SQL has been driven by stronger language models and prompting strategies, yet performance on real enterprise benchmarks such as Spider 2.

By Jingwen Liu, Weibin Liao, Xin Gao, Junfeng Zhao, Yasha Wang

llms benchmarks data-engineering

arXiv Machine Learning

Aug 17

AutoSchema: Live Schema Grounding for Agentic Text-to-Sparql over Heterogeneous Knowledge Graphs

arXiv:2608. 14228v1 Announce Type: new Abstract: Life science knowledge graphs make large collections of structured data available through SPARQL, but each resource uses its own schema, identifiers, and links.

By Yiming Zhang, Koji Tsuda

llms agents

arXiv AI

Jul 21

Generative Ontology Induction: Domain-Agnostic Schema Discovery from Document Corpora Using Large Language Models

arXiv:2607. 16201v1 Announce Type: new Abstract: Ontology engineering remains a critical bottleneck in knowledge-intensive AI systems.

By Sergei Sergienko

llms benchmarks

arXiv Machine Learning

Jun 5

Executable Schema Contracts: From Automatic Ingestion to Multi-Source Retrieval

arXiv:2606. 05415v1 Announce Type: cross Abstract: Real-world data spans tables, documents, and semi-structured files with implicit semantics.

By Padmaja Jonnalagedda, Yuguang Yao, Xiang Gao, Hilaf Hasson, Kamalika Das

llms rag agents benchmarks

arXiv AI

Jun 16

Beyond Predefined Schemas: TRACE-KG for Context-Enriched Knowledge Graph Generation

arXiv:2604. 03496v2 Announce Type: replace Abstract: Knowledge graph generation typically relies either on predefined ontologies or on schema-free extraction.

By Mohammad Sadeq Abolhasani, Yang Ba, Yixuan He, Rong Pan

One email a morning, machine-written

Related stories

ExtractBench: A Benchmark for Schema-Guided Enterprise Document Extraction

Database Context Compression for Text-to-SQL on Real-World Large Databases

AutoSchema: Live Schema Grounding for Agentic Text-to-Sparql over Heterogeneous Knowledge Graphs

Generative Ontology Induction: Domain-Agnostic Schema Discovery from Document Corpora Using Large Language Models

Executable Schema Contracts: From Automatic Ingestion to Multi-Source Retrieval

Beyond Predefined Schemas: TRACE-KG for Context-Enriched Knowledge Graph Generation