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Ksenia Se

Kseniase

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https://www.turingpost.com/

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11 new types of RAG RAG is evolving fast, keeping pace with cutting-edge AI trends. Today it becomes more agentic and smarter at navigating complex structures like hypergraphs. Here are 11 latest RAG types: 1. InstructRAG -> https://huggingface.co/papers/2504.13032 Combines RAG with a multi-agent framework, using a graph-based structure, an RL agent to expand task coverage, and a meta-learning agent for better generalization 2. CoRAG (Collaborative RAG) -> https://huggingface.co/papers/2504.01883 A collaborative framework that extends RAG to settings where clients train a shared model using a joint passage store 3. ReaRAG -> https://huggingface.co/papers/2503.21729 It uses a Thought-Action-Observation loop to decide at each step whether to retrieve information or finalize an answer, reducing unnecessary reasoning and errors 4. MCTS-RAG -> https://huggingface.co/papers/2503.20757 Combines RAG with Monte Carlo Tree Search (MCTS) to help small LMs handle complex, knowledge-heavy tasks 5. Typed-RAG - > https://huggingface.co/papers/2503.15879 Improves answers on open-ended questions by identifying question types (a debate, personal experience, or comparison) and breaking it down into simpler parts 6. MADAM-RAG -> https://huggingface.co/papers/2504.13079 A multi-agent system where models debate answers over multiple rounds and an aggregator filters noise and misinformation 7. HM-RAG -> https://huggingface.co/papers/2504.12330 A hierarchical multi-agent RAG framework that uses 3 agents: one to split queries, one to retrieve across multiple data types (text, graphs and web), and one to merge and refine answers 8. CDF-RAG -> https://huggingface.co/papers/2504.12560 Works with causal graphs and enables multi-hop causal reasoning, refining queries. It validates responses against causal pathways To explore what is Causal AI, read our article: https://www.turingpost.com/p/causalai Subscribe to the Turing Post: https://www.turingpost.com/subscribe Read further 👇

posted an update 4 days ago

11 new types of RAG RAG is evolving fast, keeping pace with cutting-edge AI trends. Today it becomes more agentic and smarter at navigating complex structures like hypergraphs. Here are 11 latest RAG types: 1. InstructRAG -> https://huggingface.co/papers/2504.13032 Combines RAG with a multi-agent framework, using a graph-based structure, an RL agent to expand task coverage, and a meta-learning agent for better generalization 2. CoRAG (Collaborative RAG) -> https://huggingface.co/papers/2504.01883 A collaborative framework that extends RAG to settings where clients train a shared model using a joint passage store 3. ReaRAG -> https://huggingface.co/papers/2503.21729 It uses a Thought-Action-Observation loop to decide at each step whether to retrieve information or finalize an answer, reducing unnecessary reasoning and errors 4. MCTS-RAG -> https://huggingface.co/papers/2503.20757 Combines RAG with Monte Carlo Tree Search (MCTS) to help small LMs handle complex, knowledge-heavy tasks 5. Typed-RAG - > https://huggingface.co/papers/2503.15879 Improves answers on open-ended questions by identifying question types (a debate, personal experience, or comparison) and breaking it down into simpler parts 6. MADAM-RAG -> https://huggingface.co/papers/2504.13079 A multi-agent system where models debate answers over multiple rounds and an aggregator filters noise and misinformation 7. HM-RAG -> https://huggingface.co/papers/2504.12330 A hierarchical multi-agent RAG framework that uses 3 agents: one to split queries, one to retrieve across multiple data types (text, graphs and web), and one to merge and refine answers 8. CDF-RAG -> https://huggingface.co/papers/2504.12560 Works with causal graphs and enables multi-hop causal reasoning, refining queries. It validates responses against causal pathways To explore what is Causal AI, read our article: https://www.turingpost.com/p/causalai Subscribe to the Turing Post: https://www.turingpost.com/subscribe Read further 👇

reacted to fdaudens's post with 🔥 8 days ago

Just tested something this morning that feels kind of game-changing for how we publish, discover, and consume news with AI: connecting Claude directly to the New York Times through MCP. Picture this: You ask Claude about a topic, and it instantly pulls verified and trusted NYT content — no more guessing if the info is accurate. The cool part? Publishers stay in control of what they share via API, and users get fast, reliable access through the AI tools they already use. Instead of scraping random stuff off the web, we get a future where publishers actively shape how their journalism shows up in AI. It’s still a bit technical to set up right now, but this could get super simple soon — like installing apps on your phone, but for your chatbot. And you keep the brand connection, too. Not saying it solves everything, but it’s definitely a new way to distribute content — and maybe even find some fresh value in the middle of this whole news + AI shakeup. Early movers will have a head start. Curious what folks think — could MCPs be a real opportunity for journalism?

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published an article 20 days ago

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Topic 33: Slim Attention, KArAt, XAttention and Multi-Token Attention Explained – What’s Really Changing in Transformers?

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20 days ago

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What is Qwen-Agent framework? Inside the Qwen family

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Mar 20

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🌁#92: Fight for Developers and the Year of Orchestration

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Mar 18

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🦸🏻#14: What Is MCP, and Why Is Everyone – Suddenly!– Talking About It?

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Mar 17

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How to Reduce Memory Use in Reasoning Models

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🌁#91: We are failing in AI literacy

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Mar 10

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🌁#90: Why AI’s Reasoning Tests Keep Failing Us

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Mar 3

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🦸🏻#13: Action! How AI Agents Execute Tasks with UI and API Tools

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Mar 10

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🦸🏻#12: How Do Agents Learn from Their Own Mistakes? The Role of Reflection in AI

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Mar 9

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Everything You Need to Know about Knowledge Distillation

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Inside the family of Smol models

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Feb 27

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🌁#89: AI in Action: How AI Engineers, Self-Optimizing Models, and Humanoid Robots Are Reshaping 2025

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Feb 25

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🦸🏻#11: How Do Agents Plan and Reason?

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Feb 24

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Topic 28: What is Mixture-of-Mamba?

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Feb 20

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🌁#88: Can DeepSeek Inspire Global Collaboration?

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Feb 17

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🦸🏻#10: Does Present-Day GenAI Actually Reason?

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Feb 15

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Topic 27: What are Chain-of-Agents and Chain-of-RAG?

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🌁#87: Why DeepResearch Should Be Your New Hire

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Feb 10

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