What separates a cryptocurrency that endures for over a decade from the thousands that vanish within their first year? January 18, 2025 marks twelve years since Dash launched as a fork of Bitcoin, making it one of the oldest active blockchain networks still operating with its original vision intact. While data from CoinGecko shows that over 90% of cryptocurrencies launched since 2017 no longer maintain active development or trading volume, Dash continues processing transactions daily across 158 countries. […]

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Multi-task reinforcement learning trains generalist policies that can execute multiple tasks. While recent years have seen significant progress, existing approaches rarely provide formal performance guarantees, which are indispensable when deploying policies in safety-critical settings. We present an approach for computing high-confidence guarantees on the performance of a multi-task policy on tasks not seen during training. Concretely, we introduce a new generalisation bound that composes (i) per-task lower confidence bounds from finitely many rollouts with (ii) task-level generalisation from […]

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Two friends in a canoe discover that sharing a sandwich isn’t really about food—it’s about . When people trust a shared “we,” generosity feels natural; when entitlement, guilt, or ego intrude, the sense of togetherness collapses. Healthy cooperation arises when individuals reveal their needs openly and surrender not to each other, but to the possibility of a caring whole.

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Introduction When writers think about LaTeX, they think about academic papers. Dense research documents filled with equations, citations, and footnotes. They don’t think about fantasy novels, thriller manuscripts, or literary fiction. That’s a missed opportunity. LaTeX—the typesetting system beloved by mathematicians and scientists—offers creative writers something word processors can’t match: programmable consistency. The same features that let physicists define custom notation for quantum equations let novelists define custom styling for character dialogue, narrative voices, and recurring textual elements. […]

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Federated Learning (FL) enables a distributed client-server architecture where multiple clients collaboratively train a global Machine Learning (ML) model without sharing sensitive local data. However, FL often results in lower accuracy than traditional ML algorithms due to statistical heterogeneity across clients. Prior works attempt to address this by using model updates, such as loss and bias, from client models to select participants that can improve the global model’s accuracy. However, these updates neither accurately represent a client’s heterogeneity […]

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arXiv:2601.21324v1 Announce Type: new Abstract: Distributionally robust optimisation (DRO) minimises the worst-case expected loss over an ambiguity set that can capture distributional shifts in out-of-sample environments. While Huber (linear-vacuous) contamination is a classical minimal-assumption model for an $varepsilon$-fraction of arbitrary perturbations, including it in an ambiguity set can make the worst-case risk infinite and the DRO objective vacuous unless one imposes strong boundedness or support assumptions. We address these challenges by introducing bulk-calibrated credal ambiguity sets: we learn […]

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arXiv:2603.12273v1 Announce Type: new Abstract: Multi-turn user interactions are among the most abundant data produced by language models, yet we lack effective methods to learn from them. While typically discarded, these interactions often contain useful information: follow-up user messages may indicate that a response was incorrect, failed to follow an instruction, or did not align with the user’s preferences. Importantly, language models are already able to make use of this information in context. After observing a user’s follow-up, […]

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