arXiv:2602.14952v1 Announce Type: cross Abstract: We consider the general problem of learning a predictor that satisfies multiple objectives of interest simultaneously, a broad framework that captures a range of specific learning goals including calibration, regret, and multiaccuracy. We work in an online setting where the data distribution can change arbitrarily over time. Existing approaches to this problem aim to minimize the set of objectives over the entire time horizon in a worst-case sense, and in practice they do […]

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Obtaining labelled ECG data for developing supervised models is challenging. Contrastive learning (CL) has emerged as a promising pretraining approach that enables effective transfer learning with limited labelled data. However, existing CL frameworks either focus solely on global context or fail to exploit ECG-specific characteristics. Furthermore, these methods rely on hard contrastive targets, which may not adequately capture the continuous nature of feature similarity in ECG signals. In this paper, we propose Beat-SSL, a contrastive learning framework that […]

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arXiv:2603.02253v1 Announce Type: new Abstract: This paper analyzes execution instability in traditional cost-based database management systems (DBMS) and identifies a structural timing misalignment between optimization and execution stages that contributes to tail-latency amplification. Beyond estimation accuracy and raw execution throughput, we argue that decision timing and the availability of runtime signals materially affect robustness under uncertainty. In conventional DBMS architectures, the optimizer relies on historical statistics, the executor observes runtime data distributions and resource states, and accelerators impose […]

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arXiv:2602.03970v2 Announce Type: replace Abstract: We study the statistical behaviour of reasoning probes in a stylized model of looped reasoning, given by Boolean circuits whose computational graph is a perfect $nu$-ary tree ($nuge 2$) and whose output is appended to the input and fed back iteratively for subsequent computation rounds. A reasoning probe has access to a sampled subset of internal computation nodes, possibly without covering the entire graph, and seeks to infer which $nu$-ary Boolean gate is […]

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arXiv:2602.04911v1 Announce Type: new Abstract: We introduce a simple initial working system in which relations (such as part-whole) are directly represented via an architecture with operating and learning rules fundamentally distinct from standard artificial neural network methods. Arbitrary data are straightforwardly encoded as graphs whose edges correspond to codes from a small fixed primitive set of elemental pairwise relations, such that simple relational encoding is not an add-on, but occurs intrinsically within the most basic components of the […]

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“Social reputation” keeps turning up on every timeline with the confidence of a solved problem. It’s pitched as the missing layer. The thing that will finally let DeFi graduate from over-collateralized lending into something resembling real credit. The thing that will make identity usable without surrendering to surveillance. The thing that will push the ecosystem toward maturity—clean it up, make it legible, make it safer. I understand the itch. DeFi has spent years pretending that credit is an […]

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Representation learning from complex data typically involves models with a large number of parameters, which in turn require large amounts of data samples. In neural network models, model complexity grows with the number of inputs to each neuron, with a trade-off between model expressivity and learning time. A precise characterization of this trade-off would help explain the connectivity and learning times observed in artificial and biological networks. We present a theoretical analysis of how learning time depends on […]

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arXiv:2602.07207v1 Announce Type: new Abstract: We propose a novel recommender framework, MuSTRec (Multimodal and Sequential Transformer-based Recommendation), that unifies multimodal and sequential recommendation paradigms. MuSTRec captures cross-item similarities and collaborative filtering signals, by building item-item graphs from extracted text and visual features. A frequency-based self-attention module additionally captures the short- and long-term user preferences. Across multiple Amazon datasets, MuSTRec demonstrates superior performance (up to 33.5% improvement) over multimodal and sequential state-of-the-art baselines. Finally, we detail some interesting facets […]

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