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BuiDoan

BuiDoan

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reacted to Kseniase's post with 👍 5 days ago

8 Free Sources about AI Agents: Agents seem to be everywhere and this collection is for a deep dive into the theory and practice: 1. "Agents" Google's whitepaper by Julia Wiesinger, Patrick Marlow and Vladimir Vuskovic -> https://www.kaggle.com/whitepaper-agents Covers agents, their functions, tool use and how they differ from models 2. "Agents in the Long Game of AI. Computational Cognitive Modeling for Trustworthy, Hybrid AI" book by Marjorie McShane, Sergei Nirenburg, and Jesse English -> https://direct.mit.edu/books/oa-monograph/5833/Agents-in-the-Long-Game-of-AIComputational Explores building AI agents, using Hybrid AI, that combines ML with knowledge-based reasoning 3. "AI Engineer Summit 2025: Agent Engineering" 8-hour video -> https://www.youtube.com/watch?v=D7BzTxVVMuw Experts' talks that share insights on the freshest Agent Engineering advancements, such as Google Deep Research, scaling tips and more 4. AI Agents Course from Hugging Face -> https://huggingface.co./learn/agents-course/en/unit0/introduction Agents' theory and practice to learn how to build them using top libraries and tools 5. "Artificial Intelligence: Foundations of Computational Agents", 3rd Edition, book by David L. Poole and Alan K. Mackworth -> https://artint.info/3e/html/ArtInt3e.html Agents' architectures, how they learn, reason, plan and act with certainty and uncertainty 6. "Intelligent Agents: Theory and Practice" book by Michael Wooldridge -> https://www.cs.ox.ac.uk/people/michael.wooldridge/pubs/ker95/ker95-html.html A fascinating option to dive into how agents were seen in 1995 and explore their theory, architectures and agent languages 7. The Turing Post articles "AI Agents and Agentic Workflows" on Hugging Face -> https://huggingface.co./Kseniase We explore agentic workflows in detail and agents' building blocks, such as memory and knowledge 8. Our collection "8 Free Sources to Master Building AI Agents" -> https://www.turingpost.com/p/building-ai-agents-sources

reacted to Kseniase's post with 🔥 12 days ago

8 New Types of RAG RAG techniques continuously evolve to enhance LLM response accuracy by retrieving relevant external data during generation. To keep up with current AI trends, new RAG types incorporate deep step-by-step reasoning, tree search, citations, multimodality and other effective techniques. Here's a list of 8 latest RAG advancements: 1. DeepRAG -> https://huggingface.co./papers/2502.01142 Models retrieval-augmented reasoning as a Markov Decision Process, enabling strategic retrieval. It dynamically decides when to retrieve external knowledge and when rely on parametric reasoning. 2. RealRAG -> https://huggingface.co./papers/2502.00848 Enhances novel object generation by retrieving real-world images and using self-reflective contrastive learning to fill knowledge gap, improve realism and reduce distortions. 3. Chain-of-Retrieval Augmented Generation (CoRAG) -> https://huggingface.co./papers/2501.14342 Retrieves information step-by-step and adjusts it, also deciding how much compute power to use at test time. If needed it reformulates queries. 4. VideoRAG -> https://huggingface.co./papers/2501.05874 Enables unlimited-length video processing, using dual-channel architecture that integrates graph-based textual grounding and multi-modal context encoding. 5. CFT-RAG -> https://huggingface.co./papers/2501.15098 A tree-RAG acceleration method uses an improved Cuckoo Filter to optimize entity localization, enabling faster retrieval. 6. Contextualized Graph RAG (CG-RAG) -> https://huggingface.co./papers/2501.15067 Uses Lexical-Semantic Graph Retrieval (LeSeGR) to integrate sparse and dense signals within graph structure and capture citation relationships 7. GFM-RAG -> https://huggingface.co./papers/2502.01113 A graph foundation model that uses a graph neural network to refine query-knowledge connections 8. URAG -> https://huggingface.co./papers/2501.16276 A hybrid system combining rule-based and RAG methods to improve lightweight LLMs for educational chatbots

reacted to tianchez's post with 👍 12 days ago

Introducing VLM-R1! GRPO has helped DeepSeek R1 to learn reasoning. Can it also help VLMs perform stronger for general computer vision tasks? The answer is YES and it generalizes better than SFT. We trained Qwen 2.5 VL 3B on RefCOCO (a visual grounding task) and eval on RefCOCO Val and RefGTA (an OOD task). https://github.com/om-ai-lab/VLM-R1

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