"Building AI Agents with LLMs, RAG, and Knowledge Graphs — A practical guide to autonomous and modern AI agents" See it at http://
amzn.to/4622k2h via @PacktDataML
LLMS
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Building AI Agents with LLMs, RAG, and Knowledge Graphs
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Residual Attention and DeepSeek V4 Developments
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Yes, next to residual attention as hot new candidate. And, I am also of course curious to see what DeepSeek V4 is up to haha
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Cross Attention in Separate Articles on Multi-Modal Models
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Yeah, I have cross attention in my separate articles (the multi-modal and understanding attention from-scratch) ones. This article was focused on text LLMs to keep the scope reasonable.
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XSA Attention Mechanism: Future Research Candidate for Follow-up Study
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Nice one! XSA from https://
arxiv.org/abs/2603.09078? To keep the scope reasonable, I only focused on those that already made it into the flagship architectures. But maybe that's an interesting candidate for a follow-up on attention research candidates. -
ARO Framework Dramatically Accelerates LLM Training via Gradient Rotation
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Can LLM training be dramatically accelerated beyond current methods? Microsoft Research, The Chinese University of Hong Kong, Shenzhen, and University of Wisconsin-Madison introduce ARO. This new matrix optimization framework pioneers "gradient rotation" as a core principle.
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LangChain and NVIDIA Launch Enterprise Agentic AI Platform
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And that's a wrap on GTC week! – We announced our enterprise agentic AI Platform built with NVIDIA. LangGraph and Deep Agents plug directly into NVIDIA's tooling. You can build agents with the latest Nemotron 3 models deployed with NIM microservices, apply NeMo Guardrails for
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Top 1% LLM Users: Advanced Strategies Beyond Chatbots
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99% of people use LLMs like Google.
That’s why they get average results.
The top 1% do this instead: → Build context
→ Force reasoning
→ Iterate, don’t restart
→ Design workflows (not prompts)
→ Optimize for execution, not answers LLMs aren’t chatbots.
They’re systems.
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LangChain Academy: Building Reliable Production Agents Course
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💫 New LangChain Academy Course: Building Reliable Agents 💫
— LangChain (@LangChain) 22 mars 2026
Shipping agents to production is hard. Traditional software is deterministic – when something breaks, you check the logs and fix the code. But agents rely on non-deterministic models.
Add multi-step reasoning, tool… pic.twitter.com/cBmDKF4T2nNew LangChain Academy Course: Building Reliable Agents Shipping agents to production is hard. Traditional software is deterministic – when something breaks, you check the logs and fix the code. But agents rely on non-deterministic models. Add multi-step reasoning, tool
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MiniMax M2.7 Model Weights to Be Open-Sourced Soon
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Here we go Confirmation from MiniMax’s Head of Engineering on what I have been saying: MiniMax-M2.7 weights will be opensourced within the next couple of weeks
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Can AI Agents Reach Agreement? New Research Paper
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Can AI Agents Agree? Frédéric Berdoz, Leonardo Rugli, Roger Wattenhofer: https://
arxiv.org/abs/2603.01213 #ArtificialIntelligence #AIAgents