arXiv:2604.18741v1 Announce Type: new Abstract: Older adults living with multiple chronic conditions (MCC) can considerably benefit from collecting and reflecting on their health data. Many older adults collect their health data using various approaches, such as digital tools or handwritten notebooks. However, in these approaches, the act of collecting data does not itself yield insights; sensemaking and reflection happen only if individuals later review their accumulated records. The daily process of data collection thus offers limited opportunity for […]

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arXiv:2603.03405v1 Announce Type: new Abstract: The forward and reverse Kullback-Leibler (KL) divergences arise as limiting objectives in learning and inference yet induce markedly different inductive biases that cannot be explained at the level of expectations alone. In this work, we introduce the Surprisal-R’enyi Free Energy (SRFE), a log-moment-based functional of the likelihood ratio that lies outside the class of $f$-divergences. We show that SRFE recovers forward and reverse KL divergences as singular endpoint limits and derive local expansions […]

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Preply uses OpenAI to launch AI-generated lesson summaries, providing personalised feedback and language learning exercises.

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arXiv:2602.11203v1 Announce Type: new Abstract: In the late 1970s, C.A. Petri introduced partially ordered event occurrences (runs), then called emph{processes}, as the appropriate model to describe the individual evolutions of distributed systems. Here, we present a unified framework for handling Petri nets and their runs, specifically to compose and decompose them. It is shown that, for nets $M$ and $N$, the set of runs of the composed net $M bullet N$ equals the composition of the runs of […]

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arXiv:2502.06545v5 Announce Type: replace-cross Abstract: We study the problem of preconditioning in sequential prediction. From the theoretical lens of linear dynamical systems, we show that convolving the target sequence corresponds to applying a polynomial to the hidden transition matrix. Building on this insight, we propose a universal preconditioning method that convolves the target with coefficients from orthogonal polynomials such as Chebyshev or Legendre. We prove that this approach reduces regret for two distinct prediction algorithms and yields the […]

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arXiv:2603.26786v1 Announce Type: new Abstract: The rapid evolution of Multimodal Large Language Models (MLLMs) is bottlenecked by the saturation of high-quality public data, while vast amounts of diverse multimodal data remain inaccessible in privacy-sensitive silos. Federated Learning (FL) offers a promising solution to unlock these distributed resources, but existing research focuses predominantly on fine-tuning, leaving the foundational pre-training phase largely unexplored. In this paper, we formally introduce the Federated MLLM Alignment (Fed-MA) task, a lightweight pre-training paradigm that […]

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