This document features screenshots from a quick live demo intended to potential investors and clients interested in seeing how our production platform works. Contact the author for a live presentation and discussion. Here I run a typical short session on the Nvidia corpus, a repository of public financial documents (PDFs). Overview Figures 1–3 show the chat mode, common to all LLMs. Ours is specialized for your input corpus(es), and thus requires much less infrastructure (tokens, GPUs) while being […]

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arXiv:2602.15045v1 Announce Type: new Abstract: Digital mapping of semantic features is essential for achieving interoperability between semantic communication and practical digital infrastructure. However, current research efforts predominantly concentrate on analog semantic communication with simplified channel models. To bridge these gaps, we develop a robust vector quantized-enabled digital semantic communication (VQ-DSC-R) system built upon orthogonal frequency division multiplexing (OFDM) transmission. Our work encompasses the framework design of VQ-DSC-R, followed by a comprehensive optimization study. Firstly, we design a Swin […]

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The electric vehicle routing problem with time windows (EVRPTW) is a complex optimization problem in sustainable logistics, where routing decisions must minimize total travel distance, fleet size, and battery usage while satisfying strict customer time constraints. Although deep reinforcement learning (DRL) has shown great potential as an alternative to classical heuristics and exact solvers, existing DRL models often struggle to maintain training stability-failing to converge or generalize when constraints are dense. In this study, we propose a curriculum-based […]

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The management of today’s optical networks is highly dependent on the correct estimation of Quality of Transmission (QoT). The current analytical approach requires exact physical values, which are often not available, resulting in inefficient management of the network. This paper proposes an Adaptive Machine Learning Framework that aims to address the analytical approach’s limitations using a new and innovative data-driven approach. The proposed framework combines linklevel embeddings with an Artificial Neural Network (ANN) to process the unique sequence […]

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arXiv:2601.21942v1 Announce Type: new Abstract: Transformers have revolutionized deep learning across various domains but understanding the precise token dynamics remains a theoretical challenge. Existing theories of deep Transformers with layer normalization typically predict that tokens cluster to a single point; however, these results rely on deterministic weight assumptions, which fail to capture the standard initialization scheme in Transformers. In this work, we show that accounting for the intrinsic stochasticity of random initialization alters this picture. More precisely, we […]

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Introduction Disclaimer: This article is a cross post from Pytorch Medium Blog Post. One of the most exciting advancements, that has pushed the frontier of the Artificial Intelligence (AI) in recent years, is Deep Reinforcement Learning (DRL). DRL belongs to the family of machine learning algorithms. It assumes that intelligent machines can learn from their actions similar to the way humans learn from experience. Over the recent years we could witness some impressive real-world applications of DRL. The […]

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Deep reinforcement learning agents are often misaligned, as they over-exploit early reward signals. Recently, several symbolic approaches have addressed these challenges by encoding sparse objectives along with aligned plans. However, purely symbolic architectures are complex to scale and difficult to apply to continuous settings. Hence, we propose a hybrid approach, inspired by humans’ ability to acquire new skills. We use a two-stage framework that injects symbolic structure into neural-based reinforcement learning agents without sacrificing the expressivity of deep […]

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City-scale, in-the-wild Augmented Reality (AR) learning paths must remain operable under Bring Your Own Device (BYOD) heterogeneity, outdoor tracking degradation, public-space safety, and interruption recovery. This study conceptualizes the Art Nouveau Path as an AR learning service and makes a theoretical contribution by proposing a Determinant-driven Requirements traceability model that treats implementation Determinants as Requirements signals and links them to testable Requirements, transfer Artefacts, and evidence anchors for replication. Methods combined 8 Points of Interest (POIs) and 36 […]

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