Hey yall, I started working on this https://github.com/jimmie-jams/SilksongRL a while ago and finally managed to train an agent to beat one (1) boss. So, I figured it was time to share my glorious creation with the world. Jokes aside, I’d love to hear your thoughts! There are more environments/bosses already configured and it’s very easy to add new ones as well but I just don’t have the time/compute to train agents at a faster rate than I currently […]

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Google AI Studio is rolling out an ‘import from GitHub’ feature inside its Build mode. It takes a repo and transforms it into a runtime-compatible format. You can then keep iterating on it, deploy it, and more. The update was shared by the Google AI Studio account and by Logan Kilpatrick, who leads the product. bringing your code from @github into ai studio build is as easy as clicking ‘import from github’ pic.twitter.com/lIyZ1lnpQQ — Google AI Studio (@GoogleAIStudio) […]

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arXiv:2602.12413v1 Announce Type: new Abstract: If LLM training data is polluted with benchmark test data, then benchmark performance gives biased estimates of out-of-distribution (OOD) generalization. Typical decontamination filters use n-gram matching which fail to detect semantic duplicates: sentences with equivalent (or near-equivalent) content that are not close in string space. We study this soft contamination of training data by semantic duplicates. Among other experiments, we embed the Olmo3 training corpus and find that: 1) contamination remains widespread, e.g. […]

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For most of the internet’s history, our online identity has never truly belonged to us. Instead, it lives inside the platforms we use every day. Social media accounts, gaming profiles, email addresses, and even work tools are all controlled by centralized companies. If one of those platforms decides to suspend an account or shut down a service, years of digital history can disappear overnight. This is one of the problems that Web3 is trying to rethink. Web3 is […]

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Modern forward electrocardiogram (ECG) computational models rely on an accurate representation of the torso domain. The lead-field method enables fast ECG simulations while preserving full geometric fidelity. Achieving high anatomical accuracy in torso representation is, however, challenging in clinical practice, as imaging protocols are typically focused on the heart and often do not include the entire torso. In addition, the computational cost of the lead-field method scales linearly with the number of electrodes, limiting its applicability in high-density […]

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The era of passive AI chatbots is ending. We are now entering the age of Agentic AI: systems that actively reason, plan, and execute tasks. For organizations, this represents a potential leap in productivity, but it also introduces new engineering challenges. Moving from a simple prompt to a reliable agent ecosystem requires a new, robust architecture. In this article, we’ll explore the anatomy of AI agents, how the Model Context Protocol (MCP) has finally solved the integration bottleneck, and how you […]

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Transfer learning is a crucial concept within deep learning that allows artificial neural networks to benefit from a large pre-training data basis when confronted with a task of limited data. Despite its ubiquitous use and clear benefits, there are still many open questions regarding the inner workings of transfer learning and, in particular, regarding the understanding of when and how well it works. To that extent, we perform a rigorous study focusing on audio-to-audio transfer learning, in which […]

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In this paper, we present Federated Robust Curvature Optimization (FedRCO), a novel second-order optimization framework designed to improve convergence speed and reduce communication cost in Federated Learning systems under statistical heterogeneity. Existing second-order optimization methods are often computationally expensive and numerically unstable in distributed settings. In contrast, FedRCO addresses these challenges by integrating an efficient approximate curvature optimizer with a provable stability mechanism. Specifically, FedRCO incorporates three key components: (1) a Gradient Anomaly Monitor that detects and mitigates […]

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