arXiv AI

AI-Assisted Knowledge Access for Legacy Enterprise Asset Management in Energy Operations: A Practical Retrieval System

arXiv:2607. 24792v1 Announce Type: cross Abstract: Energy utilities still run engineering work management, engineering procurement, and inventory processes on long-lived enterprise asset management platforms.

arXiv AI

Aug 11

VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge

arXiv:2608. 07994v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) is essential for enterprise knowledge question answering (QA), particularly in domains with complex product documentation like telecommunications.

By Wenqi Chen, Haofei Yang, Rui Yang, Fangming Li

rag agents nlp

arXiv AI

Jul 28

A corrective agentic hybrid RAG and an operations-grounded evaluation for a scientific facility

arXiv:2607. 24663v1 Announce Type: cross Abstract: Scientific user facilities accumulate decades of operational knowledge that no single search index covers: electronic logbooks, technical documents, internal wikis, operations chat messages, maintenance records, and live control-system data.

By Rajat Sainju, Dariusz Jarosz, Hairong Shang, Michael Prince, Ryan M. Aydelott, Mathew J. Cherukara, Yine Sun, Michael D. Borland

llms rag agents

arXiv AI

Aug 5

ISEE: Interactive Semantic Enrichment for Database Fields

arXiv:2608. 02604v1 Announce Type: new Abstract: LLM-based agents are increasingly being deployed for data-related tasks, including data sense-making, exploration, and retrieval.

By Yuan Tian, Yiru Chen, Rakesh R. Menon, Zifan Liu, Ting Cai, Fei Wu, Anudeep Chimakurthi, Prashanthi Ramamurthy, Sridevi Aishwariya Ganesan, Kun Qian, Yunyao Li

llms agents

arXiv AI

Jul 22

M-RAG: Semantic Key-Value Indexing for Retrieval-Augmented Generation

arXiv:2603. 26667v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) turns external documents into evidence for large language models.

By Xu Sun, Tongkai Xu, Baiheng Xie, Li Huang, Qiang Gao, Kunpeng Zhang

llms rag

arXiv AI

Jul 21

Natural Language Access to Domain-Specific Metadata: A Reusable Framework for LLM Query Generation

arXiv:2607. 18029v1 Announce Type: cross Abstract: Researchers need to answer ad-hoc questions about the contents of domain-specific archives but often lack the expertise to write structured queries on the metadata.

By Blake G. Fitch, Cato Elia Kurtz

llms agents fine-tuning benchmarks safety data-engineering

arXiv AI

Jul 31

IFCMemoryBench: Evaluating Long-Term Memory of LLM-Based Agents in BIM Information Retrieval

arXiv:2607. 26072v1 Announce Type: cross Abstract: Long-term memory is becoming a core capability of LLM-based agents, but existing evaluations largely test conversational recall in open-domain or persona-grounded settings.

By Changyu Du, Alexander Vosseler, Filippo Mazza, Andr\'e Borrmann

llms agents benchmarks

arXiv AI

Jun 30

Schema-First Retrieval: Embedding Catalogs for Natural Language Analytics

arXiv:2606. 28387v1 Announce Type: cross Abstract: Enterprise text-to-SQL systems often fail before SQL is generated: the model receives the wrong schema context.

By Adarsh Agrawal, Shashank Indukuri

rag data-engineering

arXiv AI

Jun 2

TechGraphRAG: An Agentic Graph-Augmented RAG Framework for Technical Literature Reasoning

arXiv:2606. 01613v1 Announce Type: cross Abstract: This paper presents an agentic retrieval-augmented generation (RAG) framework for domain-specific technical reasoning support, instantiated over a curated corpus of approximately 2,100 academic papers in intelligent tires, vehicle dynamics, and vehicle control.

By Kanwar Bharat Singh

llms rag agents

arXiv AI

Jul 28

TRACE: Business Rule-Grounded Reasoning Curriculum for Knowledge-Preserving Parametric Tool Retrieval in Enterprise LLMs

arXiv:2607. 22639v1 Announce Type: new Abstract: Parametric retrieval enables LLMs to retrieve tools implicitly by assigning each API a unique virtual token and training the model to generate it via constrained beam search.

By Sai Shruthi Sistla, Ashutosh Hathidara, Christopher Toukmaji, Mayank Shrivastava, Karthikeyan Asokkumar

llms rag fine-tuning

arXiv AI

Jun 26

How Do Tool-Augmented LLM Agents Perform on Real-World Energy Analytics Tasks?

arXiv:2606. 26346v1 Announce Type: new Abstract: Agentic benchmarks have emerged across general-purpose and domain-specific settings, including finance, coding, law, and drug discovery, yet energy-domain evaluations remain largely limited to static knowledge recall.

By David Akinpelu, Akintonde Abbas, Rereloluwa Alimi, Ayodeji Lana

llms rag agents benchmarks safety

arXiv AI

Aug 7

Tytan: Interactive Neurosymbolic Construction of Analytic Semantic Schemas from Relational Data

arXiv:2608. 06331v1 Announce Type: cross Abstract: From natural-language query interfaces to automated report generation, data analysis tools need a description of the data: the real-world entities it contains, which columns function as measures or identifiers, and how tables connect into units of analysis.

By Donna Hooshmand, Shubham Shahi, Cameron Barrie, Abhratanu Dutta, Marko Sterbentz, Harper Pack, Kristian J. Hammond

llms benchmarks

arXiv AI

Jul 29

From Naive RAG to Deep Agentic Retrieval: An Evolving Context Engineering Pipeline for Regulatory Compliance

arXiv:2607. 24791v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) is the dominant paradigm for applying large language models (LLMs) to enterprise document corpora, yet naive implementations encounter hard limits as corpus scale and query complexity grow.

By Mishca de Costa, Muhammad Saleh Anwar, Dave Mercier, Issam Hammad

llms rag agents nlp fine-tuning

Related stories