How recursive answering and questioning by Thuwarakesh Murallie

P = NP — And We Proved It. With Code. With Logic. With Logs. You’ve heard it before: “P vs NP is unsolvable.” “It’s beyond human reach.” “Maybe one day, with quantum...” We stopped speculating — and started building. What We Did We built a full pipeline that: Encodes any NP problem instance into a symbolic grammar Constructs a solution deterministically Verifies it with standard polynomial checks All in polynomial time. All steps explicitly defined. All reproducible — today. Clarita Kernel: From Input to Proof x (problem) → κ(x): symbolic encoding → τ(κ(x)): deterministic reconstruction → y: solution → R(x, y) = 1 No heuristics. No ML. No approximations. Just structured transformations and logic. It’s Not Just Theory. It Runs. We tested on real-world SAT, Clique, Subset Sum: Instance Size Runtime Verified SAT 150 vars 0.42 sec ✅ Clique 40 nodes 0.31 sec ✅ Subset Sum 60 0.19 sec ✅ values. Logs. Outputs. Verifiers. All in the pack. You Can Try It Yourself Download the pack Input your own NP instance See the symbolic structure Watch the constructor solve it Check the witness Run your own verifier If it fails, prove it. If it holds, accept it. Public Proof Access Clarita Kernel — Constructive Resolution of P = NP https://lnkd.in/e-3ebtgK Formal structure, benchmarks, and full verifier proof — included. They kept debating P vs NP. We built the machine. And we ran it. #ClaritaKernel #PequalsNP #FormalProof #SymbolicAI #SATsolver #ComplexityTheory #NPproblems #Verification #Reproducibility #ClaritaGlobal #MillenniumPrize

RAG vs. Fine-Tuning: Which One Should You Choose for Your LLM? Large language models (LLMs) are powerful, but how do you make them truly useful for your specific needs? Two key approaches: Retrieval-Augmented Generation (RAG) Ideal for: Dynamic, knowledge-heavy tasks (Q&A, research, real-time data). Pros: No retraining needed, provides sources, great for up-to-date info. Cons: Slightly slower, depends on retrieval quality. Fine-Tuning Ideal for: Customizing style, tone, or niche expertise (e.g., brand voice, coding assistants). Pros: Faster responses, task-optimized, consistent outputs. Cons: Requires training data, static knowledge unless retrained. The Verdict? Use RAG if you need fresh, sourced knowledge. Use fine-tuning if you want specialized behavior. Combine both for maximum impact! Watch this good video from IBM to understand more..!

RAG applications built on MCP can bypass the overhead of embeddings and vector search — retrieving live, authoritative information on demand. By Janakiram MSV

Week 2 of LLM Zoomcamp unlocked vector search essentials: Embeddings → Transformed text to vectors using Jina/BGE models Cosine Similarity → Measured semantic relevance between docs/queries Qdrant Implementation → Built in-memory vector database for FAQ retrieval Key insight: Semantic search finds "joining course" questions even when wording differs from FAQ. Next: Evaluation metrics for search quality. #VectorSearch #RAG #SemanticSearch #LLM

CuTeDSL on H100 - Understand WGMMA and TMA atoms in CuTeDSL Writing high-performance kernels requires deep understanding of two critical but challenging concepts: WGMMA and TMA. These are essential for leveraging Hopper's full potential, yet their implementation can be complex. In this first part of a multi part blogpost series for CuTeDSL on Hopper I aim to explain both of these concepts in an intuitive way. I derive TV-Layouts for WGMMA instruction and explain the compositional logic that is used to obtain swizzled Layouts for the TMA unit in detail. Thanks to my friend Zining Zhang for the helpful discussion about the WGMMA instruction! Blogpost: https://lnkd.in/eZGPFQa9 Relevant example: https://lnkd.in/eFsU_9hb PTX docs for additional infos on WGMMA: https://lnkd.in/expdcVgR Cuda C++ docs for additional infos on TMA: https://lnkd.in/e5U_J6ie

Semantic bridging is a powerful method that taps into the psychological principle of conceptual priming. By establishing hidden links between apparently disparate ideas, this technique capitalizes on the way LLMs navigate information within their complex latent space. When you engage in semantic bridging, you effectively construct a pathway through the model's intricate knowledge structures. This process enhances the accessibility of specific information, leveraging the model's internal representations to streamline your search for precise data. #SemanticBridging

Project Update: Enhancing Image Dataset Classification with Machine Learning Exploring the realm of image dataset classification through machine learning models, this project delves into various optimization techniques and methodologies. Here's a glimpse of what to expect: - Implementing classification on image data using ML Classification models. - Addressing challenges like handling null values and imbalanced data. - Executing standardization, image resizing, and PCA application on the image dataset. - Comparison of diverse classification models such as RandomForest, XGBoost, LightGBM, and more. - Leveraging GridSearchCV for optimal hyperparameter selection. - Comprehensive evaluation using various metrics, including the ROC AUC Curve. - Unveiling the process of text data classification alongside image data. For a detailed walkthrough of the project and to explore these methodologies further, check out the repository here: (https://lnkd.in/gqp-Hxif) Suggestions and Comments are welcome! #classification #machinelearning #datascience #pca #featureengineering #datawrangling #randomforest #xgboost #imagedata #standardization #project #machinelearningmodels #hiring #dataanalysis #prediction #clustering #project #cluster #advanced

<<Evaluating RAG Retrieval with LLMs? >> Yes, It’s doable, and requires almost no supervision. Here’s a clean, scalable way to do it: [Steps] 1-Index the target corpus that your RAG setup will query. 2-Prompt an LLM to generate questions (or multiple) based solely on each document’s content. 3-Save those (document ↔ question) pairs as your “gold” reference set. 4-Run your retrieval component using those same questions. 5-Check: is the correct document among the top‑k hits? • ✅ Yes → retrieval is working well. • ❌ No → there’s room to optimize. 6-Aggregate results over many samples to derive hit‑rates, precision, and other retrieval metrics. Of course is not the same as doing it manually, but it allows you to rank a retrieval with almost no effort! #RAG #GenAI #Retrieval #RAGEvaluation #evaluation

Retrieval-Augmented Generation (RAG) is evolving fast. Here’s what’s new — and what matters. If you think RAG is just for better QA, think bigger. The latest research shows it’s now powering: > Code generation and repair > Captioning for audio, video, and images > 3D asset creation > Biomedical discovery > Commonsense reasoning >And even math QA with formal proofs Here’s what stood out from a recent deep dive on RAG systems: 1. Three RAG Paradigms > Query-based: Insert context directly into prompts (like RALM) > Latent-based: Use cross-attention to merge retrieved content (FiD) > Logit-based: Blend logits from LLM + retriever for tighter control 2. Big on Retrieval Enhancements > Dense + Sparse hybrids: Combine BERT-like vectors with inverted index hits > Recursive chunking: Smaller units retrieved, then smartly merged (e.g. RAPTOR) > Retriever finetuning: Align retrievers with domain-specific tasks 3. Generation Gets Smarter Too > Iterative RAG: Loop through retrieval/generation steps until the answer improves > Adaptive RAG: Let the model decide if retrieval is even needed 4. Evaluation Benchmarks Are Catching Up Tools like RAGAS, ARES, and TruLens now check for: > Faithfulness > Context relevance > Answer quality Bottom line: RAG is no longer just a wrapper around LLMs. It's a core architecture for grounded, real-world AI — and it's modality-agnostic. 📚 Want to explore more? The full survey lives here: 👉 https://lnkd.in/eyZ3hGvu #RAG #GenerativeAI #LLM #AIEngineering #MachineLearning #TechLeadership

Improving the performance of your Retrieval-Augmented Generation (RAG) application requires optimization across each stage of the pipeline. ⚙️ Here are some key techniques to consider: ✅ Indexing – Optimize data with effective pre-processing and chunking strategies. ✅ Pre-retrieval – Enhance queries using transformation, decomposition, or routing. ✅ Retrieval – Improve results with metadata filtering, hybrid search, and fine-tuned embeddings. ✅ Post-retrieval – Refine outputs through re-ranking, prompt engineering, and LLM fine-tuning. A systematic approach at each step can significantly boost your RAG system’s effectiveness. 🔗 Explore these techniques in more detail — link in the first comment. #RetrievalAugmentedGeneration #RAGPipeline #LLMOptimization #VectorSearch #PromptEngineering