I'm happy to share the draft of the "Semantically Composable Architectures" mini-paper. It is the culmination of approximately four years' work, which began with Coreless Architectures and has now evolved into something much bigger. LLMs are impressive, but a real breakthrough will occur once we surpass the cognitive capabilities of a single human brain. Enabling autonomous large-scale system reverse engineering and large-scale autonomous transformation with minimal to no human involvement, while still making it understandable to humans if they choose to, is a central pillar of making truly groundbreaking changes. We hope the ideas we shared will be beneficial to humanity and advance our civilization further. It is not final and will require some clarification and improvements, but the key concepts are present. Happy to hear your thoughts and feedback. Some of these concepts underpin the design of the Product X system. Part of the core team + external contribution: Andrew Barsukov Andrey Kolodnitsky Sapta Girisa N Keith E. Glendon Gurpreet Sachdeva Saurav Chandra Mike Diachenko Oleh Sinkevych | 13 comments on LinkedIn

🏯🏇 A-MEM Transforms AI Agent Memory with Zettelkasten Method, Atomic Notes, Dynamic Linking & Continuous Evolution! This Novel Memory fixes rigid structures with adaptable, evolving, and interconnected knowledge networks, delivering 2x performance in complex reasoning tasks. 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗮𝘁 𝗜 𝗹𝗲𝗮𝗿𝗻𝗲𝗱: ﹌﹌﹌﹌﹌﹌﹌﹌﹌ 》 𝗪𝗵𝘆 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗠𝗲𝗺𝗼𝗿𝘆 𝗙𝗮𝗹𝗹 𝗦𝗵𝗼𝗿𝘁 Most AI agents today rely on simplistic storage and retrieval but break down when faced with complex, multi-step reasoning tasks. ✸ Common Limitations: ☆ Fixed schemas: Conventional memory systems require predefined structures that limit flexibility. ☆ Limited adaptability: When new information arises, old memories remain static and disconnected, reducing an agent’s ability to build on past experiences. ☆ Ineffective long-term retention: AI agents often struggle to recall relevant past interactions, leading to redundant processing and inefficiencies. ﹌﹌﹌﹌﹌﹌﹌﹌﹌ 》𝗔-𝗠𝗘𝗠: 𝗔𝘁𝗼𝗺𝗶𝗰 𝗻𝗼𝘁𝗲𝘀 𝗮𝗻𝗱 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗹𝗶𝗻𝗸𝗶𝗻𝗴 A-MEM organizes knowledge in a way that mirrors how humans create and refine ideas over time. ✸ How it Works: ☆ Atomic notes: Information is broken down into small, self-contained knowledge units, ensuring clarity and easy integration with future knowledge. ☆ Dynamic linking: Instead of relying on static categories, A-MEM automatically creates connections between related knowledge, forming a network of interrelated ideas. ﹌﹌﹌﹌﹌﹌﹌﹌﹌ 》 𝗣𝗿𝗼𝘃𝗲𝗻 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 A-MEM delivers measurable improvements. ✸ Empirical results demonstrate: ☆ Over 2x performance improvement in complex reasoning tasks, where AI must synthesize multiple pieces of information across different timeframes. ☆ Superior efficiency across top foundation models, including GPT, Llama, and Qwen—proving its versatility and broad applicability. ﹌﹌﹌﹌﹌﹌﹌﹌﹌ 》 𝗜𝗻𝘀𝗶𝗱𝗲 𝗔-𝗠𝗘𝗠 ✸ Note Construction: ☆ AI-generated structured notes that capture essential details and contextual insights. ☆ Each memory is assigned metadata, including keywords and summaries, for faster retrieval. ✸ Link Generation: ☆ The system autonomously connects new memories to relevant past knowledge. ☆ Relationships between concepts emerge naturally, allowing AI to recognize patterns over time. ✸ Memory Evolution: ☆ Older memories are continuously updated as new insights emerge. ☆ The system dynamically refines knowledge structures, mimicking the way human memory strengthens connections over time. ≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣ ⫸ꆛ Want to build Real-World AI agents? Join My 𝗛𝗮𝗻𝗱𝘀-𝗼𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝟰-𝗶𝗻-𝟭 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 TODAY! 𝟰𝟴𝟬+ already Enrolled. ➠ Build Real-World AI Agents for Healthcare, Finance,Smart Cities,Sales ➠ Learn 4 Framework: LangGraph | PydanticAI | CrewAI | OpenAI Swarm ➠ Work with Text, Audio, Video and Tabular Data 👉𝗘𝗻𝗿𝗼𝗹𝗹 𝗡𝗢𝗪 (𝟰𝟱% 𝗱𝗶𝘀𝗰𝗼𝘂𝗻𝘁): https://lnkd.in/eGuWr4CH | 27 comments on LinkedIn

LLMs generate possibilities; knowledge graphs remember what works. Together, they forge the recursive memory and creative engine that enables AI systems to truly evolve themselves. Combining neural components (like large language models) with symbolic verification creates a powerful framework for self-evolution that overcomes limitations of either approach used independently. AlphaEvolve demonstrates that self-evolving systems face a fundamental tension between generating novel solutions and ensuring those solutions actually work. The paper shows how AlphaEvolve addresses this through a hybrid architecture where: Neural components (LLMs) provide creative generation of code modifications by drawing on patterns learned from vast training data Symbolic components (code execution) provide ground truth verification through deterministic evaluation Without this combination, a system would either generate interesting but incorrect solutions (neural-only approach) or be limited to small, safe modifications within known patterns (symbolic-only approach). The system can operate at multiple levels of abstraction depending on the problem: raw solution evolution, constructor function evolution, search algorithm evolution, or co-evolution of intermediate solutions and search algorithms. This capability emanates directly from the neurosymbolic integration, where: Neural networks excel at working with continuous, high-dimensional spaces and recognizing patterns across abstraction levels Symbolic systems provide precise representations of discrete structures and logical relationships This enables AlphaEvolve to modify everything from specific lines of code to entire algorithmic approaches. While AlphaEvolve currently uses an evolutionary database, a knowledge graph structure could significantly enhance self-evolution by: Capturing evolutionary relationships between solutions Identifying patterns of code changes that consistently lead to improvements Representing semantic connections between different solution approaches Supporting transfer learning across problem domains Automated, objective evaluation is the core foundation enabling self-evolution: The main limitation of AlphaEvolve is that it handles problems for which it is possible to devise an automated evaluator. This evaluation component provides the "ground truth" feedback that guides evolution, allowing the system to: Differentiate between successful and unsuccessful modifications Create selection pressure toward better-performing solutions Avoid hallucinations or non-functional solutions that might emerge from neural components alone. When applied to optimize Gemini's training kernels, the system essentially improved the very LLM technology that powers it. | 12 comments on LinkedIn

NodeRAG restructures knowledge into a heterograph: a rich, layered, musical graph where each node plays a different role. It’s not just smarter retrieval. It’s structured memory for AI agents. 》 Why NodeRAG? Most Retrieval-Augmented Generation (RAG) methods retrieve chunks of text. Good enough — until you need reasoning, precision, and multi-hop understanding. This is how NodeRAG solves these problems: 》 🔹Step 1: Graph Decomposition NodeRAG begins by decomposing raw text into smart building blocks: ✸ Semantic Units (S): Little event nuggets ("Hinton won the Nobel Prize.") ✸ Entities (N): Key names or concepts ("Hinton", "Nobel Prize") ✸ Relationships (R): Links between entities ("awarded to") ✩ This is like teaching your AI to recognize the actors, actions, and scenes inside any document. 》 🔹Step 2: Graph Augmentation Decomposition alone isn't enough. NodeRAG augments the graph by identifying important hubs: ✸ Node Importance: Using K-Core and Betweenness Centrality to find critical nodes ✩ Important entities get special attention — their attributes are summarized into new nodes (A). ✸ Community Detection: Grouping related nodes into communities and summarizing them into high-level insights (H). ✩ Each community gets a "headline" overview node (O) for quick retrieval. It's like adding context and intuition to raw facts. 》 🔹 Step 3: Graph Enrichment Knowledge without detail is brittle. So NodeRAG enriches the graph: ✸ Original Text: Full chunks are linked back into the graph (Text nodes, T) ✸ Semantic Edges: Using HNSW for fast, meaningful similarity connections ✩ Only smart nodes are embedded (not everything!) — saving huge storage space. ✩ Dual search (exact + vector) makes retrieval laser-sharp. It’s like turning a 2D map into a 3D living world. 》 🔹 Step 4: Graph Searching Now comes the magic. ✸ Dual Search: First find strong entry points (by name or by meaning) ✸ Shallow Personalized PageRank (PPR): Expand carefully from entry points to nearby relevant nodes. ✩ No wandering into irrelevant parts of the graph. The search is surgical. ✩ Retrieval includes fine-grained semantic units, attributes, high-level elements — everything you need, nothing you don't. It’s like sending out agents into a city — and they return not with everything they saw, but exactly what you asked for, summarized and structured. 》 Results: NodeRAG's Performance Compared to GraphRAG, LightRAG, NaiveRAG, and HyDE — NodeRAG wins across every major domain: Tech, Science, Writing, Recreation, and Finance. NodeRAG isn’t just a better graph. NodeRAG is a new operating system for memory. ≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣ ⫸ꆛ Want to build Real-World AI agents? Join My 𝗛𝗮𝗻𝗱𝘀-𝗼𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 TODAY! ➠ Build Real-World AI Agents + RAG Pipelines ➠ Learn 3 Tools: LangGraph/LangChain | CrewAI | OpenAI Swarm ➠ Work with Text, Audio, Video and Tabular Data 👉𝗘𝗻𝗿𝗼𝗹𝗹 𝗡𝗢𝗪 (𝟯𝟰% 𝗱𝗶𝘀𝗰𝗼𝘂𝗻𝘁): https://lnkd.in/eGuWr4CH | 20 comments on LinkedIn

Choosing the Right Format: How Knowledge Graph Layouts Impact AI Reasoning ... 👉 Why This Matters Most AI systems blend knowledge graphs (structured data) with large language models (flexible reasoning). But there’s a hidden variable: "how" you translate the graph into text for the AI. Researchers discovered that the formatting choice alone can swing performance by up to "17.5%" on reasoning tasks. Imagine solving 1 in 5 more problems correctly just by adjusting how you present data. 👉 What They Built KG-LLM-Bench is a new benchmark to test how language models reason with knowledge graphs. It includes five tasks: - Triple verification (“Does this fact exist?”) - Shortest path finding (“How are two concepts connected?”) - Aggregation (“How many entities meet X condition?”) - Multi-hop reasoning (“Which entities linked to A also have property B?”) - Global analysis (“Which node is most central?”) The team tested seven models (Claude, GPT-4o, Gemini, Llama, Nova) with five ways to “textualize” graphs, from simple edge lists to structured JSON and semantic web formats like RDF Turtle. 👉 Key Insights 1. Format matters more than assumed:   - Structured JSON and edge lists performed best overall, but results varied by task.   - For example, JSON excels at aggregation tasks (data is grouped by entity), while edge lists help identify central nodes (repeated mentions highlight connections). 2. Models don’t cheat: Replacing real entity names with fake ones (e.g., “France” → “Verdania”) caused only a 0.2% performance drop, proving models rely on context, not memorized knowledge. 3. Token efficiency:   - Edge lists used ~2,600 tokens vs. JSON-LD’s ~13,500. Shorter formats free up context space for complex reasoning.   - But concise ≠ always better: structured formats improved accuracy for tasks requiring grouped data. 4. Models struggle with directionality:   Counting outgoing edges (e.g., “Which countries does France border?”) is easier than incoming ones (“Which countries border France?”), likely due to formatting biases. 👉 Practical Takeaways - Optimize for your task: Use JSON for aggregation, edge lists for centrality. - Test your model: The best format depends on the LLM—Claude thrived with RDF Turtle, while Gemini preferred edge lists. - Don’t fear pseudonyms: Masking real names minimally impacts performance, useful for sensitive data. The benchmark is openly available, inviting researchers to add new tasks, graphs, and models. As AI handles larger knowledge bases, choosing the right “data language” becomes as critical as the reasoning logic itself. Paper: [KG-LLM-Bench: A Scalable Benchmark for Evaluating LLM Reasoning on Textualized Knowledge Graphs] Authors: Elan Markowitz, Krupa Galiya, Greg Ver Steeg, Aram Galstyan

Terminology Augmented Generation (TAG)? Recently some fellow terminologists have proposed the new term "Terminology-Augmented Generation (TAG)" to refer to… | 29 comments on LinkedIn

What is really Graph RAG? Inspired by "From Local to Global: A Graph RAG Approach to Query-Focused Summarization" paper from Microsoft! How do you combine… | 12 comments on LinkedIn

Ashley Faith hosted Amy Hodler and me on a recent episode about “2025 Predictions” — here are notes I prepared, to review in 2026…

SimGRAG is a novel method for knowledge graph driven RAG, transforms queries into graph patterns and aligns them with candidate subgraphs using a graph…

✨ Attention Information Extraction Enthusiasts ✨ I am excited to announce the release of our latest paper and model family, ReLiK, a cutting-edge… | 33 comments on LinkedIn

Unlocking the Secrets of Scientific Discovery with AI and Knowledge Graphs ... Have you ever wondered how AI could revolutionize the way we conduct scientific… | 17 comments on LinkedIn

Graph Neural Prompting with Large Language Models