Mastodawn

RE: https://researchbuzz.masto.host/@mottg/116474491390856287

Nice, the state of Information Retrieval in 2026 by Mohan Krishna. Lots of interesting references and thoughts, if you're into leaderboards and state-of-the-art performance on benchmark test collections. #InformationRetrieval

MottG Apr 27

"The State of Information Retrieval in 2026"

This is the best survey article I have seen in a long time in this niche.

The dominant retriever in 2026 is an 8-billion-parameter decoder-only language model fine-tuned on synthetic data, conditioned on natural-language instructions, often executing chain-of-thought reasoning before deciding what to retrieve.

https://medium.com/@mohankrishnagr08/the-state-of-information-retrieval-in-2026-192f125a5269

#research #informationRetrieval #RAG #LLM #SPLADE #AIbenchmark #AI

IRRJ Apr 16

Published at #IRRJ: "Simple Techniques for Efficient Top-k Batch Query Processing" by Zhixuan Li and Joel Mackenzie. #BatchProcessing, #Caching, #DynamicPruning, #EfficientRetrieval, #InformationRetrieval

https://doi.org/10.54195/irrj.23893

Simple Techniques for Efficient Top- k Batch Query Processing | Information Retrieval Research

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IRRJ Apr 13

Looking back at #ECIR2026: Jaap Kamps presented the #IRRJ paper "Effectiveness of In-Context Learning for Due Diligence": https://doi.org/10.54195/irrj.22626 #InformationRetrieval

sayzard Apr 13

PageIndex는 벡터 DB와 인위적 청킹을 배제하고 문서를 계층적 '목차(tree)'로 인덱싱해 LLM으로 추론 기반 탐색을 수행하는 벡터리스 RAG 프레임워크입니다. 인간 전문가처럼 다단계 추론으로 정확한 관련 구간을 찾아 FinanceBench에서 Mafin 2.5로 98.7% 달성. 오픈소스·API·셀프호스트 제공, LiteLLM·OpenAI Agents SDK 연동.

https://github.com/VectifyAI/PageIndex

#pageindex #rag #vectorless #reasoning #informationretrieval

GitHub - VectifyAI/PageIndex: 📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG

📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG - VectifyAI/PageIndex

GitHub

sayzard Apr 4

Akshay (@akshay_pachaar)

AI 엔지니어를 위한 8가지 RAG 아키텍처를 소개하며, Naive RAG부터 다양한 검색·생성 조합 패턴까지 용도별로 설명한다. RAG 시스템 설계와 구현을 고민하는 개발자에게 실용적인 참고 자료다.

https://x.com/akshay_pachaar/status/2040050430890405931

#rag #llm #aiengineering #informationretrieval

Akshay 🚀 (@akshay_pachaar) on X

8 RAG architectures for AI Engineers: (explained with usage) 1) Naive RAG - Retrieves documents purely based on vector similarity between the query embedding and stored embeddings. - Works best for simple, fact-based queries where direct semantic matching suffices. 2)

X (formerly Twitter)

HackerNoon Apr 3

A practical look at how search indexing is evolving to hybrid retrieval systems that support semantic search, vector search, and AI-driven query understanding. https://hackernoon.com/from-inverted-indexes-to-hybrid-retrieval-rethinking-search-architecture #informationretrieval

From Inverted Indexes to Hybrid Retrieval: Rethinking Search Architecture | HackerNoon

A practical look at how search indexing is evolving to hybrid retrieval systems that support semantic search, vector search, and AI-driven query understanding.

Kevin Salt Mar 27

Well i finally did it. I just released my test dataset for AI Evaluation. Its a simulated company, represented by 60,000 documents, the readme in the image explains it all ... If you are interested, its at https://codeberg.org/Lorenz_Systems/Company_Sim.git

#EUAIAct #DigitalSovereignty #SovereignCloud #FOSS #FLOSS #Codeberg #Forgejo #OpenSource #DataGovernance #Auditability #ForensicAI #EUTech #PrivacyByDesign #InformationRetrieval #KnowledgeManagement #DeterministicAI #EUPL

Karl Voit

Mar 18

@marlinz #Informationretrieval is depending on the current task and situation.

Sometimes #navigation is the most efficient one, sometimes it's #search. (my #TagTrees are a combination of both) And then there is teleporting, bookmarks/favorites, ...

Unfortunately, most people don't have the knowledge and experience to use multiple methods and decide which method to choose in a specific situation. 😞

#PIM

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