arXiv AI July 29, 2026 By Mishca de Costa, Muhammad Saleh Anwar, Dave Mercier, Issam Hammad

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.

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 AI.

arXiv AI

Jun 30

CAMI: Cost-Aware Agent-Guided Multi-Indexing for Semantic Retrieval

arXiv:2606. 28365v1 Announce Type: cross Abstract: RAG ingestion pipelines frequently augment search corpus index with semantic enrichment indices (e.

By Adnan Qidwai, Anand Eswaran, Sonam Mishra, Jaydeep Sen, Sachindra Joshi

rag agents safety

arXiv AI

Jun 11

NightFeats @ MMU-RAGent NeurIPS 2025: A Context-Optimized Multi-Agent RAG System for the Text-to-Text Track

arXiv:2606. 11199v1 Announce Type: cross Abstract: We present NightFeats, a structured multi-agent retrieval-augmented generation (RAG) system submitted to the MMU-RAGent competition at NeurIPS 2025, where it was awarded Best Dynamic Evaluation in the text-to-text track.

By Quentin Fever, Naziha Aslam

llms rag agents benchmarks

arXiv AI

Jun 10

Agentic Hybrid RAG for Evidence-Grounded Muon Collider Analysis

arXiv:2606. 10381v1 Announce Type: cross Abstract: Muon collider research spans accelerator physics, detector instrumentation, and high-energy phenomenology, with relevant evidence scattered across a rapidly expanding and heterogeneous body of scientific literature.

By Ruobing Jiang, Dawei Fu, Cheng Jiang, Tianyi Yang, Zijian Wang, Youpeng Wu, Yong Ban, Yajun Mao, Qiang Li

rag agents nlp benchmarks

arXiv AI

Jul 14

GRASP: GRanularity-Aware Search Policy for Agentic RAG

arXiv:2607. 10463v1 Announce Type: new Abstract: Agentic retrieval-augmented generation (RAG) extends static RAG by allowing language models to iteratively reason, generate search queries, retrieve evidence, and predict answers.

By Varun Gandhi, Jaewook Lee, Shantanu Todmal, Franck Dernoncourt, Ryan Rossi, Zichao Wang, Andrew Lan

llms rag agents nlp reinforcement-learning benchmarks

arXiv AI

Aug 5

ACE-GraphRAG: Agentic Context Engineering for Hierarchical GraphRAG

arXiv:2608. 01269v2 Announce Type: replace-cross Abstract: Hierarchical Graph Retrieval-Augmented Generation (GraphRAG) organizes corpus knowledge at multiple levels of granularity, yet fixed context construction may fail to translate these multi-resolution representations into a context suited to the current query.

By Yongfeng Huang, Yuren Lai, Ruiying Chen, Haoyu Huang, Mingming Zhao, James Cheng

rag agents nlp benchmarks

arXiv AI

Jul 24

GRADRAG: Cross-Component Prompt Adaptation for Coordinated Multi-Agent RAG

arXiv:2607. 21324v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems increasingly employ multiple LLM agents.

By Paolo Pedinotti, Enrico Santus

llms rag agents benchmarks

One email a morning, machine-written

Related stories

CAMI: Cost-Aware Agent-Guided Multi-Indexing for Semantic Retrieval

NightFeats @ MMU-RAGent NeurIPS 2025: A Context-Optimized Multi-Agent RAG System for the Text-to-Text Track

Agentic Hybrid RAG for Evidence-Grounded Muon Collider Analysis

GRASP: GRanularity-Aware Search Policy for Agentic RAG

ACE-GraphRAG: Agentic Context Engineering for Hierarchical GraphRAG

GRADRAG: Cross-Component Prompt Adaptation for Coordinated Multi-Agent RAG