This page shows the most relevant public items for RAG, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • Jul 28, 2025 • arXiv • Sarah Jenkins, Esq., Wei Chen, Marcus T. Holden
The legal discovery process involves manually cross-referencing millions of pages of depositions and emails to find logical contradictions. Standard search algorithms fail to connect multi-document...
Paper • Apr 11, 2025 • arXiv • Minh N. Nguyen, Sarah J. Conner, Wei Lin
Standard Retrieval-Augmented Generation (RAG) relies on static, single-pass semantic searches that often fail on complex, multi-hop queries. We introduce Agentic-RAG, an autonomous framework where ...
Paper • Dec 8, 2021 • arXiv • Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George van den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, Diego de Las Casas, Aurelia Guy, Jacob Menick, Roman Ring, Tom Hennigan, Saffron Huang, Loren Maggiore, Chris Jones, Albin Cassirer, Andy Brock, Michela Paganini, Geoffrey Irving, Oriol Vinyals, Simon Osindero, Karen Simonyan, Jack W. Rae, Erich Elsen, Laurent Sifre
We enhance auto-regressive language models by conditioning on document chunks retrieved from a large corpus, based on local similarity with preceding tokens. With a 2 trillion token database, our R...
Paper • Feb 19, 2026 • arXiv • Florian June, Wei-Lin Chiang, Hao Peng
Traditional Retrieval-Augmented Generation (RAG) models employ a naive 'retrieve-then-read' pipeline, which frequently fails when addressing complex, multi-hop queries that require implicit reasoni...
Paper • Mar 9, 2026 • arXiv • J. Smith, A. Doe, L. Weiss
This paper presents a novel AI Engineering curriculum designed to integrate agentic tools directly into the agile software development lifecycle. It advocates for moving beyond simple AI lectures t...
Paper • Mar 11, 2026 • USC Viterbi • Luca Luceri, Emilio Ferrara
This study explores how networked LLM agents can autonomously coordinate complex information operations without explicit human direction. By modeling agents as strategic actors in a social network,...
Paper • Mar 11, 2026 • arXiv • Y. Yuan, H. Xu, Z. Liu
This research introduces a multi-agent framework designed to automate the generation and orchestration of rApp policies in Open Radio Access Networks (Open RAN). The system utilizes three specializ...
Blog • Mar 20, 2026 • Microsoft Security Blog • Vasu Jakkal
Published during RSAC 2026, this article outlines a comprehensive security framework for autonomous AI agents. It covers the necessity of continuous adaptive access, safeguarding data across agenti...
Blog • Mar 16, 2026 • Google Cloud • Mark Lohmeyer
Announced during GTC 2026, this post details Google Cloud's latest AI infrastructure, including G4 VMs powered by NVIDIA Blackwell GPUs. It introduces fractional GPU configurations and new integrat...
Blog • Mar 16, 2026 • Build Fast with AI • Satvik Paramkusham
This comprehensive roundup catalogs a massive wave of AI model releases during the first two weeks of March 2026, including OpenAI's GPT-5.4 and NVIDIA's Nemotron 3 Super. It analyzes the shrinking...
Blog • Mar 13, 2026 • AMD • AMD Editorial Staff
This article explores the shifting hardware requirements necessitated by the rise of autonomous agentic systems in enterprise data centers. It specifically examines how the complex logic and multi-...
Paper • Mar 7, 2026 • arXiv • Junming Liu, Yuqi Li, Shiping Wen, Zhigang Zeng, Tingwen Huang
Hit-RAG addresses a key challenge in long-context retrieval systems: attention dilution caused by large volumes of retrieved evidence. The framework introduces a multi-stage preference alignment pi...
Paper • Feb 7, 2026 • arXiv • Xingliang Hou, Yuyan Liu, Qi Sun, Haoxiu Wang, Hao Hu, Shaoyi Du, Zhiqiang Tian
IGMiRAG introduces a cognitive-inspired retrieval framework that simulates intuition-driven reasoning over hierarchical knowledge graphs. The system constructs a heterogeneous hypergraph to represe...
Paper • Feb 14, 2026 • arXiv • Rui Yu, Tianyi Wang, Ruixia Liu, Yinglong Wang
PT-RAG proposes a structure-aware retrieval framework designed specifically for long academic documents. Traditional RAG pipelines flatten documents into chunks, which destroys the natural hierarch...
Paper • Nov 20, 2025 • arXiv • Hao Hu, Yifan Feng, Ruoxue Li, Rundong Xue
Cog-RAG introduces a cognitive-inspired dual-hypergraph architecture designed to capture both thematic and entity-level relationships within large document collections. The framework constructs two...
Paper • Jan 19, 2026 • arxiv.org • Chen et al.
Despite recent advancements in Large Language Models (LLMs), complex Software Engineering (SE) tasks require more collaborative and specialized approaches. This concept paper systematically reviews...
Paper • Mar 8, 2026 • arXiv • M. Özcan, L. Ferrari, S. Gupta, A. Hernandez
This research addresses the escalating debate between open-weight and closed-source AI development in the context of agentic capabilities. The authors propose a tiered-release framework that balanc...
Paper • Jan 27, 2026 • arxiv.org • Débora Souza, Patrícia Machado
LLM-based agents are becoming central to software engineering tasks, yet evaluating them remains fragmented and largely model-centric. Existing studies overlook how architectural components, such a...
Paper • Jan 27, 2026 • arxiv.org • Yuheng Tang, Kaijie Zhu, Bonan Ruan, Chuqi Zhang, Michael Yang, Hongwei Li, Suyue Guo, Tianneng Shi, Zekun Li, Christopher Kruegel, Giovanni Vigna, Dawn Song, William Yang Wang, Lun Wang, Yangruibo Ding, Zhenkai Liang, Wenbo Guo
Even though demonstrating extraordinary capabilities in code generation and software issue resolving, AI agents' capabilities in the full software DevOps cycle are still unknown. Different from pur...
Paper • Jan 27, 2026 • arxiv.org • Shamse Tasnim Cynthia, Joy Krishan Das, Banani Roy
Autonomous AI agents are transforming software development and redefining how developers collaborate with AI. Prior research shows that the adoption and use of AI-powered tools differ between core ...
It ranks public content for RAG using recent discussion, review, and engagement signals so you can triage faster. This guidance is specific to RAG topic page on Attendemia and is written so it still makes sense without reading other sections on the page.
Use weekly for fast-moving updates, monthly for stable trend confirmation, and all-time for evergreen references. This guidance is specific to RAG topic page on Attendemia and is written so it still makes sense without reading other sections on the page.
Use the linked entities section to jump to labs, companies, and experts connected to this topic and explore their timelines. This guidance is specific to RAG topic page on Attendemia and is written so it still makes sense without reading other sections on the page.
Yes. Use the follow button on this page to subscribe and track new high-signal activity. This guidance is specific to RAG topic page on Attendemia and is written so it still makes sense without reading other sections on the page.