This page shows the most relevant public items for Information Retrieval, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Blog • Feb 17, 2026 • Towardsai • Florian June
Traditional Retrieval-Augmented Generation (RAG) is becoming a commodity; the next frontier is 'Cog-RAG.' This post details a new architecture where an agentic 'brain' evaluates a query, identifies...
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 16, 2021 • arXiv • Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, John Schulman
We introduce a method for fine-tuning language models to interact with a text-based web browser to answer open-ended questions. This model, WebGPT, searches the web, navigates through links, and sy...
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 • 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 • Feb 22, 2026 • bioRxiv • Folkers, A., Basile, I., Wicky, M.
Detecting remote protein homology across billions of sequences using high-dimensional embeddings is computationally prohibitive. We introduce an agentic approach that utilizes contrastive learning ...
It ranks public content for Information Retrieval using recent discussion, review, and engagement signals so you can triage faster. This guidance is specific to Information Retrieval 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 Information Retrieval 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 Information Retrieval 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 Information Retrieval topic page on Attendemia and is written so it still makes sense without reading other sections on the page.