Kameshwara Pavan Kumar Mantha’s Post

🚀 8 Retrieval-Augmented Generation (RAG) Architectures You Should Know in 2025 From Simple RAG to Agentic RAG — explore how next-gen RAG frameworks are revolutionizing knowledge retrieval, context reasoning and self-improving AI systems. Perfect for developers, data scientists and AI strategists building scalable LLM-powered solutions. 💡 Stay ahead of the curve with Jai Infoway — driving innovation in generative AI architectures for real-world impact. 🔗 Learn more: www.jaiinfoway.com #RetrievalAugmentedGeneration #RAG #AIArchitecture #GenerativeAI #JaiInfoway #ArtificialIntelligence #LLM #MachineLearning #AIInnovation

Why do our generative AI apps still hallucinate, lose context, or fail when the domain shifts? Because we’re still architecting LLM systems like it’s 2023. Most teams still rely on the old pattern: LLM + Prompt → Answer. The result? Inconsistent outputs. Brittle behavior. Poor scalability. The next wave of AI architecture is agentic, multimodal, and feedback-aware — systems that don’t just answer, but can reason, plan, self-correct, and act. #GenerativeAI #AIarchitecture #LLMs #AgenticAI Here’s a simplified view of how that shift looks 👇

𝗗𝗲𝗲𝗽𝘀𝗲𝗲𝗸𝘀 𝗻𝗲𝘄 𝗺𝗼𝗱𝗲𝗹 𝘃𝟯.𝟮-𝗲𝘅𝗽 has landed. The most important feature of the new experimental model is called DeepSeek Sparse Attention, the architecture allows the models to operate over long portions of context with comparatively small server loads. This means the price of a simple API call could be reduced by as much as half in long-context situations.  Great for chatbots but because of the nature of processing (scans long sentences for key words, as opposed to word pairs) it may lack accuracy needed for medical or legal requirements. Test away, its on Hugging Face #it #ai #compute https://lnkd.in/g64u7Gy7

🏆 The 2025 GigaOm Semantic Layer Radar Report made one thing clear: the semantic layer is no longer experimental; it’s mission-critical for enterprise AI. This year, AtScale was recognized as a Leader + Fast Mover for the third year in a row. But the real story is the themes GigaOm highlighted about where semantic layers are headed: 🤖 GenAI & Agentic Readiness: Supplying the business context LLMs need for explainable, enterprise-ready output ⚡ Performance & Cost Efficiency: Sub-second queries and reduced compute costs with autonomous optimization 🔗 Standards & Interoperability: Working across SQL, MDX, DAX, Python, R, BI platforms, and AI agents 🧩 Composable Modeling: Modular, reusable semantic objects governed at the component level 📈 Market Maturity: From emerging tech to established categories, semantic layers are now a cornerstone of enterprise architecture ✅ Business Impact & Trust: Ensuring metrics are consistent, governed, and explainable across every tool and AI system Over the next month, we’ll be diving into each of these themes with blogs, podcasts, and webinars. Follow along as we unpack why GigaOm sees AtScale setting the pace for the future of analytics and AI. 📥 Download the full report here:

While researching and deep-diving into the foundational challenges of AI Governance, and the limitations of traditional RAG are glaring. The core tension is clear: How can we guarantee factuality, explainability, and context integrity in high-stakes GenAI systems when the underlying memory (RAG) is a black box built on vector closeness rather than structural connection? I recently found the open-source Cognee framework, which effectively presents an enhanced, governable version of RAG via its ECL (Extract, Cognify, Load) pipeline. The difference is architectural: Cognee builds a Graph-RAG memory, replacing unreliable chunk retrieval with an auditable, fact-based knowledge network. This structural integrity is critical for compliance and multi-hop reasoning. This approach isn't just theory it's validated. Cognee's methodology was featured in the research paper, "Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning," showcasing superior performance on multi-hop QA benchmarks. If you are researching AI safety, governance, or just building complex agents, this is a must-see for pushing beyond RAG's constraints. Swipe through the slides for the architectural breakdown! 👇 🔗 Explore the Code & Architecture: https://https://lnkd.in/g4c6fXRv #AIGovernance #GraphRAG #LLMs #AIResearch #ExplainableAI #OpenSource #DataScience #GenAI #IEEE #ArtificialIntelligence #linkedinpost #trend #Cognee #github #LLM #RAG

🤯 STOP THE SCROLL: A 7 BILLION parameter open-source agent just outperformed GPT-4o on critical reasoning benchmarks. This is the new reality: architectural innovation is now eclipsing brute-force scale. The LupanTech/Stanford #AgentFlow framework, now available to demo on Hugging Face, is fundamentally rewriting the efficiency equation for agentic AI. It proves that scaling intelligence process is more effective than scaling parameter count. The Core Breakthrough: AgentFlow uses a specialized, modular architecture (Planner, Executor, Verifier, Generator) and the novel Flow-GRPO (Flow-based Group Refined Policy Optimization) training algorithm. Flow-GRPO solves the biggest challenge in agents—the long-horizon credit assignment problem—by training the Planner in-the-flow with a global success signal. The result is unprecedented strategic reliability and control. The Data (AgentFlow 7B Backbone): Using an efficient 7B backbone, AgentFlow beat the ∼200B proprietary model GPT-4o across all tested domains, achieving massive accuracy gains over top baselines : +14.9% Gain on Knowledge-Intensive Search 🔍 +14.5% Gain on Complex Mathematical Reasoning 📐 +14.0% Gain on Generalized Agentic Tasks 🤖 This means state-of-the-art performance, lower cost, lower latency, and built-in reliability for enterprise-grade, long-horizon workflows. Ready to test the future of efficient AI? 🔗 See the Live Demo: https://lnkd.in/g-8xtKAm #AIAgents #LLM #OpenSourceAI #FlowGRPO #GPT4o #MachineLearning #AIArchitecture #Stanford

Recursive Gradient Processing (RGP): A New Rhythm for Intelligence Most AI systems still rely on brute-force learning—massive data, static weights, and brittle generalization. RGP changes that. It introduces a recursive, rhythm-aware architecture that adapts at inference time, not just during training. Δ → GC → CF: a modular loop that rewires how gradients flow, how feedback is filtered, and how coherence emerges. This isn’t just a technical shift. It’s a philosophical one. From brute force to rhythm. From static to recursive. From noise to signal. 📄 Solving Navier-Stokes, Differently: What It Takes (2025) Zenodo DOI: 10.5281/zenodo.15793567 Let’s rethink what intelligence feels like.