Graph Continual Learning (GCL) aims to solve the challenges of streaming graph data. However, current methods often depend on replay-based strategies, which raise concerns like memory limits and privacy issues, while also struggling to resolve the stability-plasticity dilemma. In this paper, we suggest that lightweight, task-specific modules can effectively guide the reasoning process of a fixed GNN backbone. Based on this idea, we propose Task-Aware Adaptive Modulation (TAAM). The key component of TAAM is its lightweight Neural Synapse […]

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(A senior engineer perspective on bucket metadata, negative caches, and surviving random-key floods) Introduction At internet scale, object storage is less about moving bytes and more about answering questions quickly: does this bucket exist, is this caller allowed, where is this object, and what exactly should we return? Those are metadata questions. This is why metadata becomes the real bottleneck. Every request touches it, including failures. A 404 still requires the system to prove absence. A HEAD is […]

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arXiv:2602.16807v1 Announce Type: new Abstract: A collection of hyperplanes $mathcal{H}$ slices all edges of the $n$-dimensional hypercube $Q_n$ with vertex set ${-1,1}^n$ if, for every edge $e$ in the hypercube, there exists a hyperplane in $mathcal{H}$ intersecting $e$ in its interior. Let $S(n)$ be the minimum number of hyperplanes needed to slice $Q_n$. We prove that $S(n) leq lceil frac{4n}{5} rceil$, except when $n$ is an odd multiple of $5$, in which case $S(n) leq frac{4n}{5} +1$. This […]

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arXiv:2601.07886v1 Announce Type: new Abstract: In this paper, we develop a multivariate framework for approximation by max-min neural network operators. Building on the recent advances in approximation theory by neural network operators, particularly, the univariate max-min operators, we propose and analyze new multivariate operators activated by sigmoidal functions. We establish pointwise and uniform convergence theorems and derive quantitative estimates for the order of approximation via modulus of continuity and multivariate generalized absolute moment. Our results demonstrate that multivariate […]

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Accurate and efficient 3D medical image segmentation is essential for clinical AI, where models must remain reliable under stringent memory, latency, and data availability constraints. Transformer-based methods achieve strong accuracy but suffer from excessive parameters, high FLOPs, and limited generalization. We propose LightMedSeg, a modular UNet-style segmentation architecture that integrates anatomical priors with adaptive context modeling. Anchor-conditioned FiLM modulation enables anatomy-aware feature calibration, while a local structural prior module and texture-aware routing dynamically allocate representational capacity to boundary-rich […]

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Communication overhead is a critical challenge in federated learning, particularly in bandwidth-constrained networks. Although many methods have been proposed to reduce communication overhead, most focus solely on compressing individual gradients, overlooking the temporal correlations among them. Prior studies have shown that gradients exhibit spatial correlations, typically reflected in low-rank structures. Through empirical analysis, we further observe a strong temporal correlation between client gradients across adjacent rounds. Based on these observations, we propose GradESTC, a compression technique that exploits […]

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Self-correction is essential for solving complex reasoning problems in vision-language models (VLMs). However, existing reinforcement learning (RL) methods struggle to learn it, as effective self-correction behaviors emerge only rarely, making learning signals extremely sparse. To address this challenge, we propose correction-specific rollouts (Octopus), an RL rollout augmentation framework that synthesizes dense self-correction examples by recombining existing rollouts. This augmentation simultaneously improves sample efficiency due to rollout reuse and stabilizes RL optimization through balanced supervision. Furthermore, we introduce a […]

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Unlike many of his tech industry peers who have cut thousands of jobs citing the need to restructure to make the most of AI, Robinhood’s CEO Vlad Tenev conspicuously made no mention of AI in his note about layoffs.

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