arXiv:2602.18507v1 Announce Type: new Abstract: Neural networks have long strived to emulate the learning capabilities of the human brain. While deep neural networks (DNNs) draw inspiration from the brain in neuron design, their training methods diverge from biological foundations. Backpropagation, the primary training method for DNNs, requires substantial computational resources and fully labeled datasets, presenting major bottlenecks in development and application. This work demonstrates that by returning to biomimicry, specifically mimicking how the brain learns through pruning, we […]

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arXiv:2410.18844v2 Announce Type: replace-cross Abstract: Pure exploration in bandits formalises multiple real-world problems, such as tuning hyper-parameters or conducting user studies to test a set of items, where different safety, resource, and fairness constraints on the decision space naturally appear. We study these problems as pure exploration in multi-armed bandits with unknown linear constraints, where the aim is to identify an $r$-optimal and feasible policy as fast as possible with a given level of confidence. First, we propose […]

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We introduce $textsf{gradOL}$, the first gradient-based optimization framework for solving Chebyshev center problems, a fundamental challenge in optimal function learning and geometric optimization. $textsf{gradOL}$ hinges on reformulating the semi-infinite problem as a finitary max-min optimization, making it amenable to gradient-based techniques. By leveraging automatic differentiation for precise numerical gradient computation, $textsf{gradOL}$ ensures numerical stability and scalability, making it suitable for large-scale settings. Under strong convexity of the ambient norm, $textsf{gradOL}$ provably recovers optimal Chebyshev centers while directly computing […]

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arXiv:2603.22488v1 Announce Type: new Abstract: Current architecture proposals within standards development organizations such as ETSI and 3GPP enable sensing capabilities in mobile networks; however, they do not include a repository for storing sensing data. Such a repository can be used for AI model training and to complement ongoing sensing service provisioning by improving efficiency and accuracy. One way of realizing this is through the fusion of historical sensing data with live sensing data. In this paper, we study […]

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arXiv:2603.28809v1 Announce Type: new Abstract: Selecting appropriate values for the configurable parameters of Database Management Systems (DBMS) to improve performance is a significant challenge. Recent machine learning (ML)-based tuning systems have shown strong potential, but their practical adoption is often limited by the high tuning cost. This cost arises from two main factors: (1) the system needs to evaluate a large number of configurations to identify a satisfactory one, and (2) for each configuration, the system must execute […]

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Most AI systems don’t fail when things are normal; they fail in rare, unpredictable situations. One idea stuck with me from my recent podcast conversation: building AI for the real world is less about making models smarter and more about making systems reliable when things go wrong. What’s interesting is that a lot of the engineering effort goes into handling edge cases, the scenarios that rarely happen, but matter the most when they do. It changes how you […]

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Table of Links Abstract and 1. Introduction Related Work Preliminaries and Notations Differentiable Structural Information 4.1. A New Formulation 4.2. Properties 4.3. Differentiability & Deep Graph Clustering LSEnet 5.1. Embedding Leaf Nodes 5.2. Learning Parent Nodes 5.3. Hyperbolic Partitioning Tree Experiments 6.1. Graph Clustering 6.2. Discussion on Structural Entropy Conclusion, Broader Impact, and References Appendix A. Proofs B. Hyperbolic Space C. Technical Details D. Additional Results 4.3. Differentiability & Deep Graph Clustering :::info Authors: (1) Li Sun, North […]

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arXiv:2603.28775v1 Announce Type: new Abstract: As generative AI increasingly mediates learning and decision-making, users often act effectively while struggling to interpret how system outcomes are produced. While Explainable Artificial Intelligence (XAI) research has primarily addressed this problem through transparency and visualization, less attention has been paid to how explanation is constructed through interaction. This paper examines digital games as explainable interfaces by analyzing how explanation can be configured as a playable process. Using Arknights as a case study, […]

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arXiv:2601.03498v1 Announce Type: cross Abstract: As intelligent systems are developed across diverse substrates – from machine learning models and neuromorphic hardware to in vitro neural cultures – understanding what gives a system agency has become increasingly important. Existing definitions, however, tend to rely on top-down descriptions that are difficult to quantify. We propose a bottom-up framework grounded in a system’s information-processing order: the extent to which its transformation of input evolves over time. We identify three orders of […]

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