I wrote about the best suits from Marvel’s Spider-Man last week, and I had so much fun writing and ranking them that I, of course, also had to do the same for the sequel. Marvel’s Spider-Man: Miles Morales is much shorter than the first game; to 100% the whole game would take around 18 1/2 hours (compared to the first’s 45 hours). However, the development team still made sure to jam-pack the game with a ton of alternate […]

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We present a new nonlinear dimensionality reduction method, MAPLE, that enhances UMAP by improving manifold modeling. MAPLE employs a self-supervised learning approach to more efficiently encode low-dimensional manifold geometry. Central to this approach are maximum manifold capacity representations (MMCRs), which help untangle complex manifolds by compressing variances among locally similar data points while amplifying variance among dissimilar data points. This design is particularly effective for high-dimensional data with substantial intra-cluster variance and curved manifold structures, such as biological […]

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arXiv:2602.07051v1 Announce Type: new Abstract: Traditional Automatic License Plate Recognition (ALPR) systems employ multi-stage pipelines consisting of object detection networks followed by separate Optical Character Recognition (OCR) modules, introducing compounding errors, increased latency, and architectural complexity. This research presents Neural Sentinel, a novel unified approach that leverages Vision Language Models (VLMs) to perform license plate recognition, state classification, and vehicle attribute extraction through a single forward pass. Our primary contribution lies in demonstrating that a fine-tuned PaliGemma 3B […]

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Modeling causal relationships in graph representation learning remains a fundamental challenge. Existing approaches often draw on theories and methods from causal inference to identify causal subgraphs or mitigate confounders. However, due to the inherent complexity of graph-structured data, these approaches frequently aggregate diverse graph elements into single causal variables, an operation that risks violating the core assumptions of causal inference. In this work, we prove that such aggregation compromises causal validity. Building on this conclusion, we propose a […]

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arXiv:2602.21402v1 Announce Type: new Abstract: We present FlowFixer, a refinement framework for subject-driven generation (SDG) that restores fine details lost during generation caused by changes in scale and perspective of a subject. FlowFixer proposes direct image-to-image translation from visual references, avoiding ambiguities in language prompts. To enable image-to-image training, we introduce a one-step denoising scheme to generate self-supervised training data, which automatically removes high-frequency details while preserving global structure, effectively simulating real-world SDG errors. We further propose a […]

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Parallelization in Reinforcement Learning is typically employed to speed up the training of a single policy, where multiple workers collect experience from an identical sampling distribution. This common design limits the potential of parallelization by neglecting the advantages of diverse exploration strategies. We propose K-Myriad, a scalable and unsupervised method that maximizes the collective state entropy induced by a population of parallel policies. By cultivating a portfolio of specialized exploration strategies, K-Myriad provides a robust initialization for Reinforcement […]

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As the global technology ecosystem searches for its next frontier in growth, investment and collaboration, Riyadh will once again take centre stage as LEAP returns for its fifth edition from 13 to 16 April 2026. The award-winning global technology event will convene the world’s most influential technology leaders, policymakers, investors and founders to explore how innovation is reshaping economies, industries and society on a global scale. Co-organised by the Ministry of Communications and Information Technology (MCIT), the Saudi […]

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