arXiv:2603.20303v1 Announce Type: new Abstract: Flow Matching (FM) has recently emerged as a leading approach for high-fidelity visual generation, offering a robust continuous-time alternative to ordinary differential equation (ODE) based models. However, despite their success, FM models are highly sensitive to dataset biases, which cause severe semantic degradation when generating out-of-distribution or minority-class samples. In this paper, we provide a rigorous mathematical formalization of the “Bias Manifold” within the FM framework. We identify that this performance drop is […]

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Disclaimer While working as a project manager, I often heard comments like: “Why do we even need PMs? What do they actually do? They only talk and nothing more.” It’s a fairly common opinion. So I prepared an article about what PM can actually do (together with their team) and how impactful that work can be for a project. As we PMs and producers love — with numbers, tables and charts. There are three part about Art Pipeline, […]

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arXiv:2602.02616v1 Announce Type: new Abstract: Simulating flow problems is at the core of many engineering applications but often requires high computational effort, especially when dealing with complex models. This work presents a novel approach for resolving flow problems using the LATIN-PGD solver. In this contribution, we place ourselves within the framework of Newtonian compressible and laminar flows. This specific and relatively simple case enables focusing on flows for which a state equation provides a direct relation between pressure […]

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Self-supervised pre-training with contrastive learning is a powerful method for learning from sparsely labeled data. However, performance can drop considerably when there is a shift in the distribution of data from training to test time. We study this phenomenon in a setting in which the training data come from multiple domains, and the test data come from a domain not seen at training that is subject to significant covariate shift. We present a new method for contrastive learning […]

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What 10 years of trust-based platforms and one zero-to-one product unlocked about intent qualification along with the mechanism most marketing and growth teams are missing. I’ve spent over a decade working inside trust-based technology platforms. Consumer products where a stranger getting into your car, opening their home to you, or meeting you alone in a foreign city were the entire value proposition. Platforms which operated across 20+ markets or even across several continents, with millions of users. In […]

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arXiv:2602.07197v1 Announce Type: new Abstract: Deep Neural Networks (DNNs) are vulnerable to backdoor attacks. Due to the nature of Machine Learning as a Service (MLaaS) applications, black-box defenses are more practical than white-box methods, yet existing purification techniques suffer from key limitations: a lack of justification for specific transformations, dataset dependency, high computational overhead, and a neglect of frequency-domain transformations. This paper conducts a preliminary study on various image transformations, identifying down-upscaling as the most effective backdoor trigger […]

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Modern social networks incorporate AI technologies throughout their platforms because they use it for content recommendations, ad targeting, content moderation, and analytics. Artificial intelligence functions as a secondary element in these platforms because it does not serve as the main component for operation. Content creation serves as the main method through which people establish social connections and make digital content interact with their audience. The Moltbook AI social network introduces an entirely different system to its users. The […]

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arXiv:2603.24916v1 Announce Type: cross Abstract: Deploying neural networks on microcontrollers is constrained by kilobytes of flash and SRAM, where 1×1 pointwise (PW) mixers often dominate memory even after INT8 quantization across vision, audio, and wearable sensing. We present HYPER-TINYPW, a compression-as-generation approach that replaces most stored PW weights with generated weights: a shared micro-MLP synthesizes PW kernels once at load time from tiny per-layer codes, caches them, and executes them with standard integer operators. This preserves commodity MCU […]

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arXiv:2602.00884v1 Announce Type: new Abstract: Neural operators have shown promise in learning solution maps of partial differential equations (PDEs), but they often struggle to generalize when test inputs lie outside the training distribution, such as novel initial conditions, unseen PDE coefficients or unseen physics. Prior works address this limitation with large-scale multiple physics pretraining followed by fine-tuning, but this still requires examples from the new dynamics, falling short of true zero-shot generalization. In this work, we propose a […]

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