NOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and Forecasting
Paper • 2609.09140 • Published • 5
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ContextBias: Controlled Evaluation of Bias Persistence Under Context Shift in Text-to-Image Models
Gated Recurrent Transformers: Expressive Depth through Recurrent Modulation
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157
Welcome Researcher and Developers!
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.
❤ CHECK OUT :
SPACE : SKT-NRS/RD
EXPERIMENT : https://huggingface.co/sKT-Ai-Labs/SKT-SURYA-H
DIRECT TO MAIN DISCUSSION : SKT-NRS/RD#1
🤝 Your Feedback Shapes the Future :
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.
☄️Let's innovate and build together! 💡
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.
❤ CHECK OUT :
SPACE : SKT-NRS/RD
EXPERIMENT : https://huggingface.co/sKT-Ai-Labs/SKT-SURYA-H
DIRECT TO MAIN DISCUSSION : SKT-NRS/RD#1
🤝 Your Feedback Shapes the Future :
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.
☄️Let's innovate and build together! 💡
Post
7177
Hello AI Community! 👋
We are thrilled to announce the release of **NRS_QWEN_MYTHOS_1M**, a high-performance reasoning model built on the powerful **Qwen 3.5 9B** base. At **SKT AI LABS**, we’ve applied our proprietary **Neural Reasoning System (NRS)** to push the boundaries of what a 9B model can do.
🔥 **Why this model is a Game-Changer:**
✅ **100x High Reasoning Capacity:** Deep logical thinking and complex problem-solving via NRS Boosting.
✅ **1 Million Token Context:** Handle massive codebases, long documents, and multi-turn agentic tasks with ease (YaRN Scaling).
✅ **Advanced Thinking Mode:** Native
✅ **Tool-Use Ready:** Optimized for Python execution and Web Search with self-correction.
✅ **Blazing Fast:** Efficient 9B architecture that runs smoothly on consumer hardware (RTX 3090/4090).
🛠️ **Technical Highlights:**
* **Base:** Qwen 3.5 9B
* **Tuning:** NRS Specific Tuning high-quality samples.
* **License:** NRS DOCS
Whether you are a developer building coding agents, a researcher dealing with long-context data, or just someone who loves deep reasoning, this model is built for you.
👇 **Try it now on Hugging Face:**
SKT-NRS/NRS_QWEN_MYTHOS_1M
We are thrilled to announce the release of **NRS_QWEN_MYTHOS_1M**, a high-performance reasoning model built on the powerful **Qwen 3.5 9B** base. At **SKT AI LABS**, we’ve applied our proprietary **Neural Reasoning System (NRS)** to push the boundaries of what a 9B model can do.
🔥 **Why this model is a Game-Changer:**
✅ **100x High Reasoning Capacity:** Deep logical thinking and complex problem-solving via NRS Boosting.
✅ **1 Million Token Context:** Handle massive codebases, long documents, and multi-turn agentic tasks with ease (YaRN Scaling).
✅ **Advanced Thinking Mode:** Native
<think> tags for step-by-step Chain-of-Thought reasoning.✅ **Tool-Use Ready:** Optimized for Python execution and Web Search with self-correction.
✅ **Blazing Fast:** Efficient 9B architecture that runs smoothly on consumer hardware (RTX 3090/4090).
🛠️ **Technical Highlights:**
* **Base:** Qwen 3.5 9B
* **Tuning:** NRS Specific Tuning high-quality samples.
* **License:** NRS DOCS
Whether you are a developer building coding agents, a researcher dealing with long-context data, or just someone who loves deep reasoning, this model is built for you.
👇 **Try it now on Hugging Face:**
SKT-NRS/NRS_QWEN_MYTHOS_1M
Post
10458
Hello everyone,
We are excited to share that SKT-NRS is now live on Hugging Face.
We’ve developed a Neural Reasoning System (NRS) designed to enhance the capabilities of foundation models — giving them stronger reasoning, improved performance, and more reliable outputs across a wide range of tasks.
Our goal is to bring meaningful quality improvements to both new and existing models. You’ll start seeing boosted versions of various models released here soon, each refined with our NRS approach.
**What to Expect* ❤️🩹
Regular releases of Neural Reasoning-enhanced models
Clear focus on better reasoning and overall model quality
Ongoing improvements based on community feedback
If you’d like to stay updated, feel free to follow this space — we’ll be posting the first boosted models very soon.
**Community Requests**
Have a specific model you’d like us to work on? Looking for improvements on an existing model, or have any other requests?
We’re happy to hear from you. Please share your suggestions here:
## Community Requests → SKT-NRS/README#1
**Thank you for your support! We look forward to building better models together.**
We are excited to share that SKT-NRS is now live on Hugging Face.
We’ve developed a Neural Reasoning System (NRS) designed to enhance the capabilities of foundation models — giving them stronger reasoning, improved performance, and more reliable outputs across a wide range of tasks.
Our goal is to bring meaningful quality improvements to both new and existing models. You’ll start seeing boosted versions of various models released here soon, each refined with our NRS approach.
**What to Expect* ❤️🩹
Regular releases of Neural Reasoning-enhanced models
Clear focus on better reasoning and overall model quality
Ongoing improvements based on community feedback
If you’d like to stay updated, feel free to follow this space — we’ll be posting the first boosted models very soon.
**Community Requests**
Have a specific model you’d like us to work on? Looking for improvements on an existing model, or have any other requests?
We’re happy to hear from you. Please share your suggestions here:
## Community Requests → SKT-NRS/README#1
**Thank you for your support! We look forward to building better models together.**
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