interactive demo
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Visualizing RAG Retrieval in Real-Time
Read Full Article: Visualizing RAG Retrieval in Real-Time
VeritasGraph introduces an innovative tool that enhances the debugging process of Retrieval-Augmented Generation (RAG) by providing a real-time visualization of the retrieval step. This tool features an interactive Knowledge Graph Explorer, built using PyVis and Gradio, which allows users to see the entities and relationships the Language Model (LLM) considers when generating responses. When a user poses a question, the system retrieves relevant context and displays a dynamic subgraph with red nodes indicating query-related entities and node size representing connection importance. This visualization aids in understanding and refining the retrieval logic, making it an invaluable resource for developers working with RAG systems. Understanding the retrieval process is crucial for improving the accuracy and effectiveness of AI-generated responses.
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Visualizing DeepSeek’s mHC Training Fix
Read Full Article: Visualizing DeepSeek’s mHC Training Fix
DeepSeek's recent paper introduces Manifold-Constrained Hyper-Connections (mHC) to address training instability in deep learning models with many layers. When stacking over 60 layers of learned mixing matrices, small amplifications can compound, leading to explosive growth in training gains. By projecting these matrices onto a "doubly stochastic" manifold using the Sinkhorn-Knopp algorithm, gains remain bounded regardless of depth, with just one iteration significantly reducing gain from 1016 to approximately 1. An interactive demo and PyTorch implementation are available for experimentation, illustrating how this approach effectively stabilizes training. This matters because it offers a solution to a critical challenge in scaling deep learning models safely and efficiently.
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Interactive Visualization of DeepSeek’s mHC Stability
Read Full Article: Interactive Visualization of DeepSeek’s mHC Stability![[P] Interactive visualization of DeepSeek's mHC - why doubly stochastic constraints fix Hyper-Connection instability](https://www.tweakedgeek.com/wp-content/uploads/2026/01/featured-article-8316-300x171.png)
An interactive demo has been created to explore DeepSeek's mHC paper, addressing the instability in Hyper-Connections caused by the multiplication of learned matrices across multiple layers. This instability results in exponential amplification, reaching values as high as 10^16. The solution involves projecting these matrices onto a doubly stochastic manifold using the Sinkhorn-Knopp algorithm, which ensures that the composite mapping remains bounded, regardless of depth. Surprisingly, just one iteration of the Sinkhorn process is sufficient to stabilize the gain from 10^16 to approximately 1. This matters because it offers a practical method to enhance the stability and performance of deep learning models that utilize Hyper-Connections.