February 2026

We present BiTimeCrossNet (BTCNet), a multimodal self-supervised learning framework for long physiological recordings such as overnight sleep studies. While many existing approaches train on short segments treated as independent samples, BTCNet incorporates information about when each segment occurs within its parent recording, for example within a sleep session. BTCNet further learns pairwise interactions between physiological signals via cross-attention, without requiring task labels or sequence-level supervision. We evaluate BTCNet on pediatric sleep data across six downstream tasks, including sleep […]

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Key Highlights: There has been an active debate on the internet about whether tech giants should push AI features to users or let them choose. Microsoft and Google have been two of the notable tech companies that have been increasingly experimenting with AI features. While Google’s implementation is often praised, there’s a section that has been heavily criticizing Microsoft for pushing AI nonsensically all over Windows. Users have complained that rather than fixing Windows 11 at its core, […]

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Continual learning (CL) aims to train models on a sequence of tasks while retaining performance on previously learned ones. A core challenge in this setting is catastrophic forgetting, where new learning interferes with past knowledge. Among various mitigation strategies, data-replay methods, where past samples are periodically revisited, are considered simple yet effective, especially when memory constraints are relaxed. However, the theoretical effectiveness of full data replay, where all past data is accessible during training, remains largely unexplored. In […]

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Semi-supervised imitation learning (SSIL) consists in learning a policy from a small dataset of action-labeled trajectories and a much larger dataset of action-free trajectories. Some SSIL methods learn an inverse dynamics model (IDM) to predict the action from the current state and the next state. An IDM can act as a policy when paired with a video model (VM-IDM) or as a label generator to perform behavior cloning on action-free data (IDM labeling). In this work, we first […]

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The Amazon Nova AI Challenge is back for its second year. As ten selected university teams from around the world gather in Seattle this week for bootcamp (February 2-4), they’re preparing to tackle a real-world challenge in software development: building AI agents that can handle complex coding tasks while maintaining security and reliability. For the first time in an academic competition, participating teams will use Amazon Nova Forge to customize Nova models with access to tools, models and […]

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Advancing AI requires more than breakthrough models. It depends on communities of builders and researchers who experiment, test assumptions, and share what they learn. That belief is guiding how Amazon engages developers and academics around Amazon Nova, Amazon’s portfolio of AI offerings including the Nova models, Nova Forge and Nova Act. Today, two Nova initiatives launch in parallel, each designed for a distinct audience but connected by a common purpose: to help people innovate, build skills, and tackle […]

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We propose a hierarchical entity-centric framework for offline Goal-Conditioned Reinforcement Learning (GCRL) that combines subgoal decomposition with factored structure to solve long-horizon tasks in domains with multiple entities. Achieving long-horizon goals in complex environments remains a core challenge in Reinforcement Learning (RL). Domains with multiple entities are particularly difficult due to their combinatorial complexity. GCRL facilitates generalization across goals and the use of subgoal structure, but struggles with high-dimensional observations and combinatorial state-spaces, especially under sparse reward. We […]

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