YouTube has built a perfectly elegant economy where corporations pay to interrupt you, and you pay to not be interrupted. It’s extortion. It’s genius. It’s somehow both. By A Deeply Concerned Human Companies pay YouTube billions to force ads in front of viewers, while viewers pay YouTube monthly fees to avoid seeing those same ads. At what point does that business model become concerning? CONGRATULATIONS, YOU’RE FUNDING BOTH SIDES OF A WAR YOU NEVER AGREED TO FIGHT: Let […]

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The rapid expansion of Industrial IoT (IIoT) systems has amplified security challenges, as heterogeneous devices and dynamic traffic patterns increase exposure to sophisticated and previously unseen cyberattacks. Traditional intrusion detection systems often struggle in such environments due to their reliance on extensive labeled data and limited ability to detect new threats. To address these challenges, we propose MI$^2$DAS, a multi-layer intrusion detection framework that integrates anomaly-based hierarchical traffic pooling, open-set recognition to distinguish between known and unknown attacks […]

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We propose risk-sensitive reinforcement learning algorithms catering to three families of risk measures, namely expectiles, utility-based shortfall risk and optimized certainty equivalent risk. For each risk measure, in the context of a finite horizon Markov decision process, we first derive a policy gradient theorem. Second, we propose estimators of the risk-sensitive policy gradient for each of the aforementioned risk measures, and establish $mathcal{O}left(1/mright)$ mean-squared error bounds for our estimators, where $m$ is the number of trajectories. Further, under […]

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arXiv:2505.12129v2 Announce Type: replace-cross Abstract: We introduce the first graph kernels for metric graphs via tropical algebraic geometry. In contrast to conventional graph kernels based on graph combinatorics such as nodes, edges, and subgraphs, our metric graph kernels are purely based on the geometry and topology of the underlying metric space. A key characterizing property of our construction is its invariance under edge subdivision, making the kernels intrinsically well-suited for comparing graphs representing different underlying metric spaces. We […]

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arXiv:2603.28981v1 Announce Type: new Abstract: We develop a hybrid conservative finite-volume / bounded-interval multiwavelet formulation for the deterministic one-dimensional Buckley–Leverett equation. Because Buckley–Leverett transport is a nonlinear hyperbolic conservation law with entropy-admissible shocks, the saturation update is performed by a conservative finite-volume scheme with monotone numerical fluxes, while the evolving state is represented and reconstructed in a bounded-interval multiwavelet basis. This strategy preserves the correct shock-compatible transport mechanism and simultaneously provides a hierarchical multiresolution description of the solution. […]

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arXiv:2601.20738v1 Announce Type: cross Abstract: Biased gradient compression with error feedback (EF) reduces communication in federated learning (FL), but under non-IID data, the residual error can decay slowly, causing gradient mismatch and stalled progress in the early rounds. We propose step-ahead partial error feedback (SA-PEF), which integrates step-ahead (SA) correction with partial error feedback (PEF). SA-PEF recovers EF when the step-ahead coefficient $alpha=0$ and step-ahead EF (SAEF) when $alpha=1$. For non-convex objectives and $delta$-contractive compressors, we establish a […]

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arXiv:2602.04925v1 Announce Type: new Abstract: The internalization of chain-of-thought processes into hidden states has emerged as a highly efficient paradigm for scaling test-time compute. However, existing activation steering methods rely on static control vectors that fail to adapt to the non-stationary evolution of complex reasoning tasks. To address this limitation, we propose STIR (Self-Distilled Tools for Internal Reasoning), a framework that reformulates reasoning enhancement as a dynamic latent trajectory control problem. STIR introduces a synergistic three-stage pipeline: (1) […]

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In Part 1, a smart account was deployed and the first UserOperation successfully executed through the EntryPoint. At that point, everything worked — but a critical part of the system stayed mostly invisible: the bundler. Bundlers are the bridge between account abstraction and the Ethereum execution layer. They take UserOperations from a separate mempool, pay gas costs upfront, and get reimbursed through the protocol. Understanding how they work—the validation rules, reputation system, and economic incentives—is essential for debugging […]

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Large language models (LLMs) are increasingly deployed in decision-making tasks, where not only accuracy but also reliable confidence estimates are essential. Well-calibrated confidence enables downstream systems to decide when to trust a model and when to defer to fallback mechanisms. In this work, we conduct a systematic study of calibration in two widely used fine-tuning paradigms: supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR). We show that while RLVR improves task performance, it produces extremely overconfident […]

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