<p>Swedish agentic operations startup Kovant, officially known as Kovan AB, today is launching its platform globally after closing on $1.5 million in a pre-seed funding round led by J12 Ventures. The round also saw participation from Ampli, Green Ventures and a number of prominent angel investors. Stockholm-based Kovant says it wants to help industrial enterprises [&#8230;]</p>
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أطلقت شركة كوفانت السويدية، المعروفة رسميًا باسم كوفان AB، منصتها عالميًا بعد تأمين 1.5 مليون دولار في جولة تمويل أولية بقيادة J12 Ventures، بمشاركة من Ampli وGreen Ventures وعدد من المستثمرين الملائكة.

La startup sueca de operaciones agenticas Kovant, oficialmente conocida como Kovan AB, ha lanzado su plataforma a nivel global tras asegurar 1,5 millones de dólares en una ronda de financiación pre-semilla liderada por J12 Ventures, con la participación de Ampli, Green Ventures y varios inversores ángeles.

La startup suédoise Kovant, officiellement connue sous le nom de Kovan AB, a lancé sa plateforme à l'échelle mondiale après avoir sécurisé 1,5 million de dollars lors d'un tour de financement pré-seed dirigé par J12 Ventures, avec la participation d'Ampli, Green Ventures et plusieurs investisseurs providentiels.

Swedish agentic operations startup Kovant, officially known as Kovan AB, has launched its platform globally after securing $1.5 million in a pre-seed funding round led by J12 Ventures, with participation from Ampli, Green Ventures, and several angel investors.

Kovant wants to become the nerve center of enterprise agentic AI operations after pre-seed funding

arXiv:2511.20257v1 Announce Type: new 
Abstract: Accurate and interpretable air pollution forecasting is crucial for public health, but most models face a trade-off between performance and interpretability. This study proposes a physics-guided, interpretable-by-design spatiotemporal learning framework. The model decomposes the spatiotemporal behavior of air pollutant concentrations into two transparent, additive modules. The first is a physics-guided transport kernel with directed weights conditioned on wind and geography (advection). The second is an explainable attention mechanism that learns local responses and attributes future concentrations to specific historical lags and exogenous drivers. Evaluated on a comprehensive dataset from the Stockholm region, our model consistently outperforms state-of-the-art baselines across multiple forecasting horizons. Our model's integration of high predictive performance and spatiotemporal interpretability provides a more reliable foundation for operational air-quality management in real-world applications.

قدمت دراسة جديدة إطارًا للتعلم الزماني المكاني الموجه بالفيزياء لتوقع تلوث الهواء، والذي يقوم بتفكيك سلوكيات تركيز الملوثات إلى وحدتين شفافيتين. تم تقييم هذا النموذج باستخدام بيانات من منطقة ستوكهولم، ويظهر أداءً متفوقًا مقارنةً بأساليب التوقع الحالية عبر آفاق زمنية متعددة.

Un nuevo estudio ha presentado un marco de aprendizaje espaciotemporal guiado por la física para la previsión de la contaminación del aire, que descompone los comportamientos de concentración de contaminantes en dos módulos transparentes. Este modelo, evaluado con datos de la región de Estocolmo, demuestra un rendimiento superior en comparación con los métodos de previsión existentes en varios horizontes temporales.

Une nouvelle étude a présenté un cadre d'apprentissage spatiotemporel guidé par la physique pour la prévision de la pollution de l'air, qui décompose les comportements de concentration des polluants en deux modules transparents. Ce modèle, évalué sur des données de la région de Stockholm, montre une performance supérieure par rapport aux méthodes de prévision existantes sur divers horizons temporels.

A new study has introduced a physics-guided spatiotemporal learning framework for air pollution forecasting, which decomposes pollutant concentration behaviors into two transparent modules. This model, evaluated on data from the Stockholm region, demonstrates superior performance compared to existing forecasting methods across various time horizons.

Kovant wants to become the nerve center of enterprise agentic AI operations after pre-seed funding

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