arXiv:2607.00005v1 Announce Type: new Abstract: Identifying where to innovate in a dense technical domain – such as operating systems or hardware/software co-design – is fundamentally a search problem in a high-dimensional knowledge space. Existing approaches rely on keyword search, citation proximity, or human intuition, none of which formalise the notion of an unexplored region that is simultaneously relevant to a target goal and absent from prior art. We present Topological Void Analysis (TVA), a mathematical framework that defines […]

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I want to learn how to build an external AI player that plays a game from the outside (by capturing the desktop screen and simulating WASD keyboard inputs, not by modifying the game files). The game is a fast-paced 3D game where the player must dodge oncoming obstacles. The control inputs are simple (just WASD), but the game visuals are very chaotic with flashing, shifting background colors. My Background: * I have some mid experience with Supervised and […]

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arXiv:2605.22829v1 Announce Type: new Abstract: Multimodal Retrieval-Augmented Generation (RAG) has emerged as an effective paradigm for enhancing Large Language Models (LLMs) with external knowledge. However, existing multimodal RAG systems predominantly rely on coarse-grained page-level retrieval, which fails to capture fine-grained semantic and layout structures in visually rich documents, thereby compromising retrieval accuracy and leading to redundant context in downstream tasks. To address these issues, we propose Layout-oriented Fine-grained Retrieval-Augmented Generation (LFRAG), a novel framework that advances multimodal RAG […]

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arXiv:2602.04925v1 Announce Type: new Abstract: The internalization of chain-of-thought processes into hidden states has emerged as a highly efficient paradigm for scaling test-time compute. However, existing activation steering methods rely on static control vectors that fail to adapt to the non-stationary evolution of complex reasoning tasks. To address this limitation, we propose STIR (Self-Distilled Tools for Internal Reasoning), a framework that reformulates reasoning enhancement as a dynamic latent trajectory control problem. STIR introduces a synergistic three-stage pipeline: (1) […]

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arXiv:2604.05120v1 Announce Type: new Abstract: Digital humans are lifelike virtual agents capable of natural conversation and are increasingly deployed in domains like retail and finance. However, most current digital humans operate in isolation from their surroundings and lack contextual awareness beyond the dialogue itself. We address this limitation by integrating ambient intelligence (AmI) – i.e., environmental sensors, IoT data, and contextual modeling – with digital human systems. This integration enables situational awareness of the user’s environment, anticipatory and […]

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arXiv:2604.08643v1 Announce Type: new Abstract: User interactions in online recommendation platforms create interdependencies among content creators: feedback on one creator’s content influences the system’s learning and, in turn, the exposure of other creators’ contents. To analyze incentives in such settings, we model collaboration as a multi-agent stochastic linear bandit problem with a transferable utility (TU) cooperative game formulation, where a coalition’s value equals the negative sum of its members’ cumulative regrets. We show that, for identical (homogenous) agents […]

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arXiv:2602.08151v1 Announce Type: cross Abstract: We consider the problem of prediction with expert advice for “easy” sequences. We show that a variant of NormalHedge enjoys a second-order $epsilon$-quantile regret bound of $Obig(sqrt{V_T log(V_T/epsilon)}big) $ when $V_T > log N$, where $V_T$ is the cumulative second moment of instantaneous per-expert regret averaged with respect to a natural distribution determined by the algorithm. The algorithm is motivated by a continuous time limit using Stochastic Differential Equations. The discrete time analysis […]

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In the age of digital epidemiology, epidemiologists are faced by an increasing amount of data of growing complexity and dimensionality. Machine learning is a set of powerful tools that can help to analyze such enormous amounts of data. This chapter lays the methodological foundations for successfully applying machine learning in epidemiology. It covers the principles of supervised and unsupervised learning and discusses the most important machine learning methods. Strategies for model evaluation and hyperparameter optimization are developed and […]

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arXiv:2504.18522v2 Announce Type: replace Abstract: We consider the problem of modeling the effects of perturbations, such as gene knockdowns or drugs, on measurements, such as single-cell RNA or protein counts. Given data for some perturbations, we aim to predict the distribution of measurements for new combinations of perturbations. To address this challenging extrapolation task, we posit that perturbations act additively in a suitable, unknown embedding space. We formulate the data-generating process as a latent variable model, in which […]

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