arXiv AI July 29, 2026 By Yuxin Zhou, Huai Zhang, S. Mostafa Mousavi

Automatic Knowledge Graph Construction and Query for Earthquake Catalogs

arXiv:2607. 24984v1 Announce Type: cross Abstract: In recent years, the number of events in earthquake catalogs has significantly increased due to the utilization of more effective deep learning based detectors and phase pickers but answering open ended questions such as what characterizes this sequence?

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 7

TRIAGE: Trustworthy Retrieval Instrumentation And Graph Evaluation

arXiv:2607. 03447v1 Announce Type: cross Abstract: Knowledge graphs (KGs) that underpin Graph-based Retrieval-Augmented Generation (Graph-RAG) are increasingly built automatically by LLM-driven extraction rather than curated by experts.

By Axel TahmasebiMoradi, Lucas Schott, Martin Royer

llms rag

arXiv Machine Learning

Jul 3

Bringing Agentic Search to Earth Observation Data Discovery

arXiv:2607. 02387v1 Announce Type: cross Abstract: NASA and its data centers hold thousands of geoscience datasets and tools like Worldview, Giovanni, the Science Discovery Engine, and Harmony.

By Minghan Yu, Youran Sun, Chugang Yi, Yixin Wen, Haizhao Yang

llms rag agents fine-tuning benchmarks

arXiv Machine Learning

Aug 11

Verifiably grounded machine interpretation of lunar geology

arXiv:2608. 09276v1 Announce Type: cross Abstract: Planetary geology relies on historical, interpretive reasoning to reconstruct past events from diverse observations.

By Tom Sander, Kay Wohlfarth, Christian W\"ohler

rag multimodal

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 2

Knowledge Graphs as the Missing Data Layer for LLM-Based Industrial Asset Operations

arXiv:2605. 26874v2 Announce Type: replace-cross Abstract: LLM-based agents for industrial asset operations show limited accuracy when reasoning over flat document stores.

By Madhulatha Mandarapu, Sandeep Kunkunuru

llms agents benchmarks

arXiv AI

Jul 28

Agentic Graph Retrieval-Augmented Generation for Auditable Commercial Registry Analysis

arXiv:2605. 18770v2 Announce Type: replace-cross Abstract: Public commercial registries are formally open, yet their practical analysis remains difficult because relevant facts are scattered across millions of records that combine structured metadata, multilingual legal notices, temporal events, and entity aliases.

By Arthur Capozzi, Dirk Helbing

llms rag agents benchmarks

Related stories

TRIAGE: Trustworthy Retrieval Instrumentation And Graph Evaluation

Bringing Agentic Search to Earth Observation Data Discovery

Verifiably grounded machine interpretation of lunar geology

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

Knowledge Graphs as the Missing Data Layer for LLM-Based Industrial Asset Operations

Agentic Graph Retrieval-Augmented Generation for Auditable Commercial Registry Analysis