Many-Task Learning refers to the setting where a large number of related tasks need to be learned, the exact relationships between tasks are not known. We introduce the Cascaded Transfer Learning, a novel many-task transfer learning paradigm where information (e.g. model parameters) cascades hierarchically through tasks that are learned by individual models of the same class, while respecting given budget constraints. The cascade is organized as a rooted tree that specifies the order in which tasks are learned […]

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:::info Astounding Stories of Super-Science April 2004, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. THE COUNTRY OF THE BLIND – XIX. — THE CRYSTAL EGG. Astounding Stories of Super-Science April 2004: THE COUNTRY OF THE BLIND – XIX. — THE CRYSTAL EGG. By H. G. Wells ::: There was, until a year ago, a little and very grimy-looking shop near Seven Dials, over which, in […]

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arXiv:2603.10090v1 Announce Type: new Abstract: Neural network weights are typically viewed as the end product of training, while most deep learning research focuses on data, features, and architectures. However, recent advances show that the set of all possible weight values (weight space) itself contains rich structure: pretrained models form organized distributions, exhibit symmetries, and can be embedded, compared, or even generated. Understanding such structures has tremendous impact on how neural networks are analyzed and compared, and on how […]

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If you hang around startup circles in the Bay Area long enough, you’ll start hearing something unexpected between funding rounds and AI debates: founders quietly Googling “car rental in UAE” and checking flight prices to Dubai and Abu Dhabi. What started as curiosity has turned into a real trend. From San Francisco to the sands of the Arabian Peninsula, American tech talent is seriously eyeing the United Arab Emirates—and not just for a quick conference or a flashy […]

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arXiv:2602.15210v1 Announce Type: new Abstract: Multilinguality is a core capability for modern foundation models, yet training high-quality multilingual models remains challenging due to uneven data availability across languages. A further challenge is the performance interference that can arise from joint multilingual training, commonly referred to as the “curse of multilinguality”. We study multilingual data curation across thirteen languages and find that many reported regressions are not inherent to multilingual scaling but instead stem from correctable deficiencies in data […]

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arXiv:2510.03437v2 Announce Type: replace-cross Abstract: Kernel change-point detection (KCPD) has become a widely used tool for identifying structural changes in complex data. While existing theory establishes consistency under independence assumptions, real-world sequential data such as text exhibits strong dependencies. We establish new guarantees for KCPD under $m$-dependent data: specifically, we prove consistency in the number of detected change points and weak consistency in their locations under mild additional assumptions. We perform an LLM-based simulation that generates synthetic $m$-dependent […]

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Backpropagation algorithm has driven the remarkable success of deep neural networks, but its lack of biological plausibility and high computational costs have motivated the ongoing search for alternative training methods. Hebbian learning has attracted considerable interest as a biologically plausible alternative to backpropagation. Nevertheless, its exclusive reliance on local information, without consideration of global task objectives, fundamentally limits its scalability. Inspired by the biological synergy between neuromodulators and local plasticity, we introduce a novel model-agnostic Global-guided Hebbian Learning […]

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arXiv:2602.10153v1 Announce Type: new Abstract: The AI Control research agenda aims to develop control protocols: safety techniques that prevent untrusted AI systems from taking harmful actions during deployment. Because human oversight is expensive, one approach is trusted monitoring, where weaker, trusted models oversee stronger, untrusted models$unicode{x2013}$but this often fails when the untrusted model’s actions exceed the monitor’s comprehension. We introduce legibility protocols, which encourage the untrusted model to take actions that are easier for a monitor to evaluate. […]

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