arXiv:2601.12147v1 Announce Type: new Abstract: Segment Anything (SAM) has recently pushed the boundaries of segmentation by demonstrating zero-shot generalization and flexible prompting after training on over one billion masks. Despite this, its mask prediction accuracy often falls short of the precision required in real-world applications. While several refinement modules have been proposed to boost SAM’s segmentation quality, achieving highly accurate object delineation within a single, unified framework remains an open challenge. Furthermore, interactive image matting, which aims to […]

Ver mais

Teilhard de Chardin charts how scientific inquiry evolved from occult secrets to aesthetic contemplation before the nineteenth century’s discovery of deep time ignited a revolutionary mysticism: humanity advancing toward a future it could shape. When materialism’s promise collapsed, leaving researchers spiritually adrift, Teilhard argues only Christianity can provide the animating soul science desperately needs—transforming the laboratory into a site of cosmic love.

Ver mais

arXiv:2602.15219v1 Announce Type: new Abstract: The growing complexity in home energy management demands advanced systems that guide occupants toward informed energy decisions. Large language model (LLM)-integrated home energy management systems (HEMS) have shown promise, but prior studies relied on prompt engineering or pre-built platforms with limited customization of agent behavior, or assessed performance through single-turn or -task evaluations. This study introduces a multi-agent home energy management assistant (HEMA), built on LangChain and LangGraph, designed to adaptively and intelligently […]

Ver mais

arXiv:2602.02593v1 Announce Type: new Abstract: Neural scaling laws govern the prediction power-law improvement of test loss with respect to model capacity ($N$), datasize ($D$), and compute ($C$). However, existing theoretical explanations often rely on specific architectures or complex kernel methods, lacking intuitive universality. In this paper, we propose a unified framework that abstracts general learning tasks as the progressive coverage of patterns from a long-tail (Zipfian) distribution. We introduce the Effective Frontier ($k_star$), a threshold in the pattern […]

Ver mais

arXiv:2601.05353v1 Announce Type: new Abstract: Accurate forecasting of blood glucose from CGM is essential for preventing dysglycemic events, thus enabling proactive diabetes management. However, current forecasting models treat blood glucose readings captured using CGMs as a numerical sequence, either ignoring context or relying on additional sensors/modalities that are difficult to collect and deploy at scale. Recently, LLMs have shown promise for time-series forecasting tasks, yet their role as agentic context extractors in diabetes care remains largely unexplored. To […]

Ver mais

arXiv:2603.25925v1 Announce Type: new Abstract: Game-based learning (GBL) is widely adopted in mathematics education. It enhances learners’ engagement and critical thinking throughout the mathematics learning process. However, enabling players to learn intrinsically through mathematical games still presents challenges. In particular, effective GBL systems require dozens of high-quality game levels and mechanisms to deliver them to appropriate players in a way that matches their learning abilities. To address this challenge, we propose a framework, guided by adaptive learning theory, […]

Ver mais

arXiv:2506.11743v2 Announce Type: replace-cross Abstract: Graph coarsening aims to diminish the size of a graph to lighten its memory footprint, and has numerous applications in graph signal processing and machine learning. It is usually defined using a reduction matrix and a lifting matrix, which, respectively, allows to project a graph signal from the original graph to the coarsened one and back. This results in a loss of information measured by the so-called Restricted Spectral Approximation (RSA). Most coarsening […]

Ver mais

Understanding whether fine-tuning elicits latent capabilities or teaches new ones is a fundamental question for language model evaluation and safety. We develop a formal information-theoretic framework for quantifying how much predictive structure fine-tuning extracts from the train dataset and writes into a model’s parameters. Our central quantity, Excess Description Length (EDL), is defined via prequential coding and measures the gap between the bits required to encode training labels sequentially using an evolving model (trained online) and the residual […]

Ver mais