SKT AI Labs, we are pushing the boundaries of AI architecture and research—and today, we are thrilled to open our doors to the global research community!
We warmly welcome researchers, developers, and AI enthusiasts to join us and contribute to our R&D efforts.
🧪 What You Can Explore:
We invite you to experiment with our WMF (Weight Manifold Fusion) technology. You can test this high-dimensional fusion technique on smaller models to gain a deeper understanding of its behavior and token convergence.
If it works: Fantastic! Share your results with us and contribute directly to the core vision of SKT AI Labs.
If it doesn't work: No problem at all! Your critical feedback is just as valuable to us. Every experiment and anomaly helps us refine this architecture to make it more stable and robust.
We firmly believe that true innovation stems from community collaboration and transparent testing. Let's build the future of advanced AI together. Your ideas, test results, and feedback are always welcome!
You Can Still Research and Development On WMF Only SKT-SURYA-H Model is Dismissed.
We’re excited to release NRS_QWEN_MYTHOS_1M — a powerful reasoning model built on Qwen 3.5 9B! At SKT AI LABS, we’ve supercharged this 9B model with our proprietary Neural Reasoning System (NRS) to deliver next-level performance.
🔥 Why This Model is a Game-Changer: ✅ 100x Reasoning Capacity — Exceptional deep logical thinking and complex problem-solving ✅ 1 Million Token Context — Perfect for massive codebases, long documents, and multi-turn agentic workflows ✅ Advanced Thinking Mode — Native <think> tags for true step-by-step Chain-of-Thought reasoning ✅ Tool-Use Ready — Optimized for Python execution, Web Search, and self-correction ✅ Blazing Fast — Runs smoothly on consumer GPUs like RTX 3090/4090
Whether you’re a developer building coding agents, a researcher working with long-context data, or someone who loves powerful reasoning — this model is built for you.
🌐 We crawled the entirety of Hugging Face to help the community! Huge thanks to the Hugging Face API 🌐 🤖 2.91M model repos (file names included), 📚 1.02M dataset repos, 🚀 1.31M Space repos 🤗 617,501 committers (datasets and models), we’ll share Hugging Face statistics with you in the coming days..
❗ Dating apps do not allow us to control the profiles suggested to us based on our mutual search criteria ❗ 🧬 If you want to see if your soulmate has already existed, I have published a dataset of 59k anonymized public profiles
Are you looking for a female ML engineer who is looking for a male ML engineer and you can't find it on the apps ? You need to look for her, but more importantly, she needs to look for you. Personally, I'm looking for a physicist I'm encountering the same problem. I can't find it My answer : Paradox of choice of dating apps solved by patent ⚡ WO2026082672 ⚡ https://patentscope.wipo.int/search/en/detail.jsf?docId=WO2026082672
J'ai du breveté pour te trouver et on se trouvera bientôt !
🤖 >_ Can an LLM execute logic gates and boolean arithmetic ?
We need to create datasets : - Neural Arithmetic and Logic Unit (NALU) 32 bits - Neural Application Binary Interface (NABI) 32 bits
🎯 Optimal Instruction Set = RV32IMAF
This opens the way for code writing and execution by the LLMs themselves without an external CLI.
The more of us who want it, the more possible it will become ...
PhysiQuanty/Binary-Addition-LLM-POC (10-bits binary addition : binary carry propagation, sampling no longer has any effect on the logits due to the fact that it is deterministic next token.)
We are thrilled to announce the launch of SKT-OMNI-CORPUS-2T, a massive-scale, high-quality dataset designed to power the next generation of Foundation Models (LLMs) from scratch. Developed at SKT AI LABS, this corpus is not just a collection of data; it’s a mission to decentralize high-grade AI training for regional languages and global knowledge.
💎 Key Highlights:
•• Massive Scale: Targeting a multi-terabyte architecture for 2T-level tokenization.
•• Pure Quality: Curated from 500+ Elite Sources
•• Structured for MoE: Perfectly sharded into 3.5GB standardized units (SKT-𝕻 series) for seamless distributed training.
🤝 Open for Collaboration!
We are looking for AI researchers, CUDA engineers, and data scientists to join us in this journey of building Project Surya and the ST-X Series models. Whether it's optimization, custom tokenization, or architecture design—let’s build the future together.