Structured and Informed Probabilistic Modeling with the Thermodynamic Kolmogorov-Arnold Model

We adapt the Kolmogorov-Arnold Representation Theorem to generative modeling by reinterpreting its inner functions as a Markov Kernel between probability spaces via inverse transform sampling. We present a generative model that is interpretable, easy to design, and efficient. Our approach couples a Kolmogorov-Arnold Network generator with independent energy-based priors, trained via Maximum Likelihood. Inverse sampling enables fast inference, while prior knowledge can be incorporated before training to better align priors with posteriors, thereby improving learning efficiency and sample quality. The learned prior is also recoverable and visualizable post-training, offering an empirical Bayes perspective. To address inflexibility and mitigate prior-posterior mismatch, we introduce scalable extensions based on mixture distributions and Langevin Monte Carlo methods, admitting a trade-off between flexibility and training efficiency. Our contributions connect classical representation theorems with modern probabilistic modeling, while balancing training stability, inference speed, and the quality and diversity of generations.

FACTS About Building Retrieval Augmented Generation-based Chatbots

Enterprise chatbots, powered by generative AI, are emerging as key applications to enhance employee productivity. Retrieval Augmented Generation (RAG), Large Language Models (LLMs), and orchestration frameworks like Langchain and Llamaindex are crucial for building these chatbots. However, creating effective enterprise chatbots is challenging and requires meticulous RAG pipeline engineering. This includes fine-tuning embeddings and LLMs, extracting documents from vector databases, rephrasing queries, reranking results, designing prompts, honoring document access controls, providing concise responses, including references, safeguarding personal information, and building orchestration agents. We present a framework for building RAG-based chatbots based on our experience with three NVIDIA chatbots: for IT/HR benefits, financial earnings, and general content. Our contributions are three-fold: introducing the FACTS framework (Freshness, Architectures, Cost, Testing, Security), presenting fifteen RAG pipeline control points, and providing empirical results on accuracy-latency tradeoffs between large and small LLMs. To the best of our knowledge, this is the first paper of its kind that provides a holistic view of the factors as well as solutions for building secure enterprise-grade chatbots."

sar reduction techniques for wearable application

The biocompatible, noninvasive, lightweight, compact size and low manufacturing cost of the wearable antenna has attracted the researchers to work more in this field to bring the use of wearable antennas to the mainstream wearable devices industry. In recent years there has been great growth in the field of wearable electronics due to their unmatched functionalities and use cases, with the increased use of wearables there comes the concern about the safety in the usage of the antenna which directly points towards the SAR value of the particular antenna in use. This paper examines a wide range of antenna based on their SAR value and the techniques such as EBG, AMC, use of metamaterials, used to reduce the SAR for safe usage