For the 5% of readers who already know about Media over Quic, you almost certainly have your own opinion on this question, so feel free to skip the article and jump straight to the comments to explain why I’m wrong. For the other 95%, you’re probably like this right now: Not to worry my friends, we’ll get into Quic, how you send Media over it, and how that’s different from putting RTC on the Web. What is WebRTC? […]
There’s a question that quietly predicts whether an AI agent will feel sharp or strangely distant: How long does the agent remain misaligned when a new domain term enters the conversation? It sounds almost trivial — a glossary issue, a maintenance task. But in practice, this single latency window reveals more about an agent’s reliability than most teams realize. Because language doesn’t evolve politely. It arrives mid‑conversation. A finance analyst introduces a new internal metric. A product team […]
Although cross-domain few-shot learning (CDFSL) for hyper-spectral image (HSI) classification has attracted significant research interest, existing works often rely on an unrealistic data augmentation procedure in the form of external noise to enlarge the sample size, thus greatly simplifying the issue of data scarcity. They involve a large number of parameters for model updates, being prone to the overfitting problem. To the best of our knowledge, none has explored the strength of the foundation model, having strong generalization […]
Group-relative policy optimization methods train language models by generating multiple rollouts per prompt and normalizing rewards with a shared mean reward baseline. In resource-constrained settings where the rollout budget is small, accuracy often degrades. We find that noise in the shared baseline induces advantage sign flips, where some rollouts receive an incorrect advantage sign, and the update direction is reversed. To address this, we propose Median-Centered Group Relative Policy Optimization (MC-GRPO), a simple and effective solution for small-rollout […]
Most engineering teams treat documentation as a manual chore, leading to “knowledge silos” and grueling onboarding cycles. While tools like Notion and Confluence are great for general notes, they fail to capture the “why” behind code automatically. To scale without losing context, teams need a system that treats documentation as a byproduct of work, linking technical decisions, meeting insights, and code commits into a living knowledge graph. The Problem: Your Wiki is Where Knowledge Goes to Die We’ve all […]
Integrating Java and .NET components is a common requirement in enterprise platforms, especially within regulated domains such as digital healthcare. While API-based integration is often treated as the default approach, it is not always the most appropriate architectural choice. Selecting between API-based integration and JAR-based execution is an architectural decision that depends on system boundaries, operational constraints, and long-term maintainability—not simply developer convenience. This article examines when each execution model is most effective, with a particular focus on […]
Organizations that have become more integrated into data science are changing from analytics to intelligence. Economic intelligence does not consist of building more models or dashboards; it is embedding a structured way of reasoning into decision systems. An econometrics-first approach provides a strong roadmap for designing those systems and to ensure that data-driven insights are communicated to action that is sound, and coherent. From Analytics Outputs to Decision Systems Conventional analytics pipelines usually have outputs of predictions or […]
arXiv:2601.20399v2 Announce Type: replace-cross Abstract: Randomized subspace methods reduce per-iteration cost; however, in nonconvex optimization, most analyses are expectation-based, and high-probability bounds remain scarce even under sub-Gaussian noise. We first prove that randomized subspace SGD (RS-SGD) admits a high-probability convergence bound under sub-Gaussian noise, achieving the same order of oracle complexity as prior in-expectation results. Motivated by the prevalence of heavy-tailed gradients in modern machine learning, we then propose randomized subspace normalized SGD (RS-NSGD), which integrates direction normalization […]
arXiv:2601.15036v2 Announce Type: replace-cross Abstract: Factorizable joint shift (FJS) represents a type of distribution shift (or dataset shift) that comprises both covariate and label shift. Recently, it has been observed that FJS actually arises from consecutive label and covariate (or vice versa) shifts. Research into FJS so far has been confined to the case of categorical labels. We propose a framework for analysing distribution shift in the case of a general label space, thus covering both classification and […]
arXiv:2512.15605v2 Announce Type: replace-cross Abstract: Autoregressive models (ARMs) currently constitute the dominant paradigm for large language models (LLMs). Energy-based models (EBMs) represent another class of models, which have historically been less prevalent in LLM development, yet naturally characterize the optimal policy in post-training alignment. In this paper, we provide a unified view of these two model classes. Taking the chain rule of probability as a starting point, we establish an explicit bijection between ARMs and EBMs in function […]