In this article, I’ll show you how to unlock the full power of GitHub Copilot agents inside VS Code. There are actually three main types of Copilot agents-most people only know about one, but I’m going to show you all of them. By the end, you’ll be able to create custom agents that act as your own specialized “sub-agents.” Getting Started: The Setup Before we dive into the agents, make sure your environment is ready. Update VS Code: Ensure […]

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arXiv:2601.03452v1 Announce Type: new Abstract: Resiliency has garnered attention in the management of critical infrastructure as a metric of system performance, but there are significant roadblocks to its implementation in a realistic decision-making framework. Contrasted to risk and reliability, which have robust quantification approaches and undergird many regulatory approaches to system safety (e.g., “risk-informed decision-making”), resiliency is a diffuse, qualitatively-understood characteristic, often treated differently or distinctly. However, in the emerging context of highly-complex, highly-interdependent critical systems, the idea […]

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A primary goal of online deliberation platforms is to identify ideas that are broadly agreeable to a community of users through their expressed preferences. Yet, consensus elicitation should ideally extend beyond the specific statements provided by users and should incorporate the relative salience of particular topics. We address this issue by modelling consensus as an interval in a one-dimensional opinion space derived from potentially high-dimensional data via embedding and dimensionality reduction. We define an objective that maximizes expected […]

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arXiv:2602.15977v1 Announce Type: new Abstract: An algorithm on weighted graphs is called universally optimal if it is optimal for every input graph, in the worst case taken over all weight assignments. Informally, this means the algorithm is competitive even with algorithms that are optimized for only one specific input graph. Universal optimality was recently introduced [Haeupler et al. 2024] as an alternative to the stronger, but often unachievable instance optimality. In this paper, we extend the concept of […]

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Feature learning strength (FLS), i.e., the inverse of the effective output scaling of a model, plays a critical role in shaping the optimization dynamics of neural nets. While its impact has been extensively studied under the asymptotic regimes — both in training time and FLS — existing theory offers limited insight into how FLS affects generalization in practical settings, such as when training is stopped upon reaching a target training risk. In this work, we investigate the impact […]

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arXiv:2603.23650v1 Announce Type: new Abstract: We present our system for the BLEMORE Challenge at FG 2026 on blended emotion recognition with relative salience prediction. Our approach combines six encoder families through late probability fusion: an S4D-ViTMoE face encoder adapted with soft-label KL training, frozen layer-selective Wav2Vec2 audio features, finetuned body-language encoders (TimeSformer, VideoMAE), and — for the first time in emotion recognition — Gemini Embedding 2.0, a large multimodal model whose video embeddings produce competitive presence accuracy (ACCP […]

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arXiv:2603.10087v1 Announce Type: new Abstract: Engram conditional memory has emerged as a promising component for LLMs by decoupling static knowledge lookup from dynamic computation. Since Engram exhibits sparse access patterns and supports prefetching, its massive embedding tables are well-suited for offloading to lower-tier memory. In this paper, we propose using Compute Express Link (CXL) memory pool for Engram storage. Compared to RDMA, CXL provides fine-grained and low-latency access required by minimal and discrete retrieval patterns of Engram. We […]

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Privacy-Preserving Federated Learning (PPFL) is a Decentralized machine learning paradigm that enables multiple participants to collaboratively train a global model without sharing their data with the integration of cryptographic and privacy-based techniques to enhance the security of the global system. This privacy-oriented approach makes PPFL a highly suitable solution for training shared models in sectors where data privacy is a critical concern. In traditional FL, local models are trained on edge devices, and only model updates are shared […]

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Modern distributed networks, notably transformers, acquire a remarkable ability (termed `in-context learning’) to adapt their computation to input statistics, such that a fixed network can be applied to data from a broad range of systems. Here, we provide a complete mechanistic characterization of this behavior in transformers trained on a finite set $S$ of discrete Markov chains. The transformer displays four algorithmic phases, characterized by whether the network memorizes and generalizes, and whether it uses 1-point or 2-point […]

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arXiv:2602.03851v1 Announce Type: new Abstract: The mastery of Hijaiyah letters is a crucial foundation for reading and comprehending the Quran, yet conventional pedagogical approaches based on repetitive memorization frequently struggle to maintain the engagement of young learners in contemporary educational contexts. This research presents the design and implementation of an innovative gamification-based methodology for Hijaiyah literacy acquisition, systematically developed through the ADDIE framework (Analysis, Design, Development, Implementation, Evaluation) to optimize student motivation, participation, and educational outcomes. The resulting […]

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