How Fractured Chain-of-Thought Reasoning boosts LLM efficiency

📌 [𝗖𝗵𝗲𝗮𝘁 𝗦𝗵𝗲𝗲𝘁] 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗶𝗻 𝗔𝗟𝗠: Core Capabilities and Applications Across Market, Behavioral, and New Business Domains Machine Learning’s role can be understood through three core functional capabilities (𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻, 𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝗮𝗹𝗶𝘁𝘆 𝗥𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗘𝘅𝗽𝗹𝗮𝗻𝗮𝘁𝗼𝗿𝘆 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀) aligned with 𝗔𝗟𝗠’𝘀 𝗽𝗿𝗶𝗺𝗮𝗿𝘆 𝗱𝗼𝗺𝗮𝗶𝗻𝘀: 𝗠𝗮𝗿𝗸𝗲𝘁, 𝗕𝗲𝗵𝗮𝘃𝗶𝗼𝗿𝗮𝗹, 𝗮𝗻𝗱 𝗡𝗲𝘄 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀. Our article “How Machine Learning Is Reshaping Finance and ALM” introduces a framework that illustrates this relationship, breaking down ML’s technical scope. It serves as a practical guide, highlighting the strategic, operational, and regulatory factors institutions must address when embedding #MachineLearning into #BalanceSheetManagement (BSM). If you’d like to dive deeper, the publication presents this framework and explores additional 𝗸𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗼𝗻 𝘁𝗵𝗲 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗶𝗻 𝗔𝗟𝗠. Read it here: https://lnkd.in/dvSnawqe #AI #ALM #RiskManagement #BankingInnovation #BSM

🚀 #CALM before the #STORM might be the most poetic title for a technical research paper. That’s 160× smaller model, same intelligence. Researchers from Shanghai University, and Alibaba’s #Qwen Team just released a fascinating study: “#CALM Before the #STORM: Unlocking Native Reasoning for Optimization Modeling.” Here’s the short version: Instead of just fine-tuning massive models on endless data, the team tried something smarter. Their method, #CALM (Corrective Adaptation with Lightweight Modification), lets experts inject small “reasoning hints” directly into a model’s thought process — refining how it thinks, not just what it knows. Then they introduced #STORM, a 4B-parameter model trained with #CALM, that hit 68.9% accuracy across five optimization benchmarks, matching the performance of a 671B model. The message? Maybe the future of #AI isn’t about building bigger brains, but teaching smaller ones to think better. 🔗 arXiv:2510.04204 #AI #Reasoning #Optimization #Research #MachineLearning

Is the Tiny Recursion Model (TRM) a BIG Breakthrough ? LLMs rely on chain-of-thought (CoT) step-by-step reasoning. That produced the first meaningful leaps in performance. LLMs also depend on test-time-compute (TTC) generating many answers and choosing the best one. Both are powerful, but Brute Force methods. The elegant Tiny Recursion Model (TRM) takes its own path, like passing a note to yourself, each pass refines both the internal understanding and current answer. TRM has deep supervision that pushes corrections earlier in the chain, keeping gradients alive, and make every thought more meaningful. A slower thinking layer informs a fast thinking one. It trades parameters for interactions. Performing as well as models 10,000x larger. Reasoning doesn’t have to be an emergent property of scale, it can be a property of recursion. Performance may soon grow not by parameters or data, but with models ability to refine and recurse on its own outputs. #AI #DeepLearning #Reasoning #EdgeAI #FutureofWork #CognitiveDesign #fifteensixfiftyclub

Post 9 (#AIForGood) Model Merging: A Game-Changer for Domain-Specific LLMs Are you working on training a large language model for your specific domain? Here's an exciting breakthrough that could save you time and boost performance. Traditional approach: Continuously pre-train (CPT) your model on domain data, then fine-tune it. But there's a better way. The SLERP Method: Researchers at MIT discovered something remarkable - instead of just using your CPT model, merge it with the original base model using Spherical Linear Interpolation (SLERP). The result? A model that performs better than either parent model alone. This isn't just averaging - it's a synergistic effect. SLERP interpolates along a spherical path in parameter space (rather than a straight line), unlocking emergent capabilities that neither model had independently. Key findings: Works exceptionally well for 7-8B parameter models (Llama, Mistral) Best strategy: Base model + CPT → SLERP merge with Instruct model Performance improvements of 12-20%+ over baseline The "whole is greater than the sum of its parts" This could be particularly valuable for specialized domains like materials science, healthcare, legal, or finance where domain knowledge is critical. 📄 Full paper: https://lnkd.in/ezV5wEbd #AIForGood #MachineLearning #LLM #AI #DomainAdaptation

RAG x Local Memory In AI, not every answer comes from inside the model. Sometimes, it retrieves knowledge. Sometimes, it just remembers. Here’s the difference 👇 🔹 With RAG (Retrieval-Augmented Generation) → The model searches external sources: 📚 databases, 🧾 documents, 🔍 APIs or vector stores …to bring fresh, up-to-date knowledge. 🔹 Without RAG (Local Memory) → The model relies on its internal knowledge: 🧠 pre-trained data, 💬 conversation context, 📂 stored memory …great for reasoning, logic, or known facts. ✨ How does it decide? It depends on the confidence and context: • If it needs specifics or recent info → 🔎 RAG • If it already knows → 🧠 Local The future of AI = hybrid intelligence, blending local reasoning + external retrieval. That’s how we get truthful, smart, and contextual agents. #AI #RAG #ArtificialIntelligence #Agents #KnowledgeRetrieval #LLMs #AIArchitecture #MachineLearning #FutureOfAI #AIAgents #ContextualAI

Think You Need Giant Models for Hard Reasoning Tasks? Think Again. A groundbreaking new paper introduces Tiny Recursive Models (TRM), a minimalist approach that outperforms massive LLMs on challenging puzzles like Sudoku, mazes, and ARC-AGI—using only 7 million parameters. That’s less than 0.01% of the parameters used by models like DeepSeek R1 or Gemini 2.5 Pro. Here’s the core idea:   Instead of scaling up, TRM uses a single tiny network that recursively refines its answer over multiple steps. It starts with an initial guess and progressively improves it by updating a latent reasoning state—much like pausing to rethink a problem before giving a final answer. Key takeaways:   ✅ No complex hierarchies or biological justifications   ✅ No fixed-point theorems or 1-step gradient approximations   ✅ One network, two layers, and a fraction of the parameters   ✅ Outperforms HRM and many LLMs on ARC-AGI, Sudoku, and maze-solving This work challenges the “bigger is better” paradigm and highlights the power of recursive reasoning and deep supervision in small-data regimes. A must-read for anyone interested in efficient AI, reasoning models, or the future of lightweight AGI. 🔗 Read the paper here(https://lnkd.in/gBuuiXQg) What do you think—is recursion the next frontier for efficient AI? #AI #MachineLearning #AGI #Reasoning #EfficientAI #LLM #Research #DeepLearning #TinyModels #RecursiveAI

20 Different Types of RAG Techniques You Should Know. Retrieval-Augmented Generation (RAG) is reshaping how we build AI applications — bridging the gap between knowledge retrieval and generative power. But did you know there are 20+ different RAG techniques? 🤯 From vanilla RAG to hierarchical, graph-based, multi-hop, hybrid retrieval, and agentic RAG, each comes with unique strengths to tackle challenges like: ✔️ Hallucination reduction ✔️ Domain-specific accuracy ✔️ Knowledge scalability ✔️ Contextual reasoning 🔍 We’ve put together a clear infographic that breaks down 20 RAG variations in one place. Perfect for developers, AI researchers, and business leaders exploring real-world GenAI solutions. 👉 Check it out below and tell me — which RAG technique are you most excited about? #GenerativeAI #RAG #AI #LLM #MLOps #MachineLearning #ArtificialIntelligence

🚀 Retrieval-Augmented Generation (#RAG) is transforming AI — combining the precision of knowledge retrieval with the creativity of generative models. But here’s the kicker: 👉 There isn’t just one way to do RAG. There are 20+ different RAG techniques 🤯 From vanilla RAG to hierarchical, graph-based, multi-hop, hybrid retrieval, and even agentic RAG — each variation offers unique strengths for tackling: ✔️ Reducing hallucinations ✔️ Improving domain-specific accuracy ✔️ Scaling knowledge access ✔️ Enabling deeper contextual reasoning 📊 We’ve put together a clean infographic that maps out 20 RAG variations in one place. Perfect for: 👩💻 Developers 🔬 AI Researchers 💼 Business leaders exploring GenAI 👉 Dive in and tell me: Which RAG technique are you most excited about using in your projects? 💡 At Intuz, we help teams implement advanced RAG techniques in production — from PoCs to full-scale enterprise rollouts. 📩 DM me if you’d like to explore how RAG can power your GenAI roadmap.

A new paper, "Less is More: Recursive Reasoning with Tiny Networks," introduces the Tiny Recursive Model (TRM) • It's a tiny, 7-million-parameter model... that's *outperforming* massive LLMS (like Deepseek R1 and even Gemini 2.5 Pro) on complex reasoning tasks like ARC-AGI and Sudoku. The secret? 📌 Recursive Reasoning, TRM doesn't try to get the answer in one shot; it uses a single, tiny network to recursively refine its own reasoning, step by step. 📌 It's not about size. It's about structure, Efficiency is the new superpower. Paper: https://lnkd.in/d4Shbf7N GitHub Repository: https://lnkd.in/dxgC5EDq #AI #LLMS #TinyML #DeepLearning #Research