February 2026

We are currently living through the AI gold rush. Companies are hiring Data Scientists by the dozen, building RAG pipelines, and fine-tuning LLMs. But while DevSecOps teams are busy building fortresses around production Kubernetes clusters, there is a massive gap in the security perimeter right at the developer’s fingertips: The Jupyter Notebook. I wanted to test a hypothesis: ML engineers are prioritizing speed over hygiene, and notebooks are leaking critical infrastructure credentials. To prove this, I didn’t hack anyone. I […]

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São Paulo city will host Latin America’s leading debate on the regulation of digital assets, stablecoins, and blockchain infrastructure with the first edition of MERGE São Paulo 2026, one of the world’s largest Web3, blockchain, and crypto events. From March 17 to 19, 2026, the event will bring together more than 5,000 participants and 300 speakers, connecting regulators, financial institutions, policymakers, and companies from Brazil and around the world within the digital asset ecosystem. The event takes place […]

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Recently I asked an AI agent about some project management tools recommendations and it suggested Asana, Monday.com and ClickUp. The agent was smart enough to explain why each tool fits my criteria and why it’s the best. However, what I couldn’t know was that did any of these companies pay for their tools to be on this list that was recommended to me? The agent showed no disclosure, so there was no way to check. And this is […]

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Robust control of mechanical systems with multiple uncertainties remains a fundamental challenge, particularly when nonlinear dynamics and operating-condition variations are intricately intertwined. While deep reinforcement learning (DRL) combined with domain randomization has shown promise in mitigating the sim-to-real gap, simultaneously handling all sources of uncertainty often leads to sub-optimal policies and poor learning efficiency. This study formulates a new curriculum-based continual learning framework for robust control problems involving nonlinear dynamical systems in which multiple sources of uncertainty are […]

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Recent work has demonstrated that transformers and linear attention models can perform in-context learning (ICL) on simple function classes, such as linear regression. In this paper, we empirically study how these two attention mechanisms differ in their ICL behavior on the canonical linear-regression task of Garg et al. We evaluate learning quality (MSE), convergence, and generalization behavior of each architecture. We also analyze how increasing model depth affects ICL performance. Our results illustrate both the similarities and limitations […]

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Learning to Defer (L2D) enables a classifier to abstain from predictions and defer to an expert, and has recently been extended to multi-expert settings. In this work, we show that multi-expert L2D is fundamentally more challenging than the single-expert case. With multiple experts, the classifier’s underfitting becomes inherent, which seriously degrades prediction performance, whereas in the single-expert setting it arises only under specific conditions. We theoretically reveal that this stems from an intrinsic expert identifiability issue: learning which […]

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In a pacy crypto market, few projects stand out for the right reasons. Mutuum Finance (MUTM) is gaining attention not from hype, but from visible progress, growing adoption, and clear long-term utility. As investors seek the cheapest crypto with strong upside, MUTM is emerging as a serious contender in today’s top crypto narratives. With early funding momentum and real protocol development underway, it is shaping itself as a sustainable DeFi platform rather than a short-term speculative play. What […]

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Nonstationary time series forecasting suffers from the distribution shift issue due to the different distributions that produce the training and test data. Existing methods attempt to alleviate the dependence by, e.g., removing low-order moments from each individual sample. These solutions fail to capture the underlying time-evolving structure across samples and do not model the complex time structure. In this paper, we aim to address the distribution shift in the frequency space by considering all possible time structures. To […]

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Graph neural networks (GNNs) work remarkably well in semi-supervised node regression, yet a rigorous theory explaining when and why they succeed remains lacking. To address this gap, we study an aggregate-and-readout model that encompasses several common message passing architectures: node features are first propagated over the graph then mapped to responses via a nonlinear function. For least-squares estimation over GNNs with linear graph convolutions and a deep ReLU readout, we prove a sharp non-asymptotic risk bound that separates […]

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We investigate the recently introduced model of learning with improvements, where agents are allowed to make small changes to their feature values to be warranted a more desirable label. We extensively extend previously published results by providing combinatorial dimensions that characterize online learnability in this model, by analyzing the multiclass setup, learnability in a bandit feedback setup, modeling agents’ cost for making improvements and more.

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