Buffaloe Digital supports brands in building these immersive ecosystems by designing virtual try-ons, 3D visualization, and gamified launch experiences, designed to make online shopping feel exploratory.

تتناول أميليا كاستيلانوس إمكانيات الذكاء الاصطناعي التوليدي والتجارة الغامرة كأدوات استراتيجية لمستقبل التجارة الإلكترونية. تدعم شركة بوفالو ديجيتال العلامات التجارية في بناء أنظمة بيئية غامرة من خلال تصميم تجارب تجربة افتراضية، وتصوير ثلاثي الأبعاد، وتجارب إطلاق موجهة للألعاب، تهدف إلى جعل التسوق عبر الإنترنت أكثر استكشافًا.

Amelia Castellanos analiza el potencial de la IA generativa y el comercio inmersivo como herramientas estratégicas para el futuro del comercio electrónico. Buffaloe Digital, la empresa que representa, se enfoca en crear experiencias de compra inmersivas a través de pruebas virtuales, visualizaciones en 3D y lanzamientos gamificados, con el objetivo de hacer que las compras en línea sean más exploratorias y atractivas.

Amelia Castellanos aborde le potentiel de l'IA générative et du commerce immersif comme outils stratégiques pour l'avenir du commerce électronique. Buffaloe Digital, l'entreprise qu'elle représente, se concentre sur la création d'expériences d'achat immersives grâce à des essayages virtuels, des visualisations 3D et des lancements gamifiés, visant à rendre le shopping en ligne plus exploratoire et engageant.

Amelia Castellanos discusses the potential of generative AI and immersive commerce as strategic tools for the future of e-commerce. Buffaloe Digital, the company she represents, focuses on creating immersive shopping experiences through virtual try-ons, 3D visualizations, and gamified launches, aiming to make online shopping feel more exploratory and engaging.

Amelia Castellanos on How Generative AI and Immersive Commerce Can Be Strategic Levers for Next-Gen E-Commerce

arXiv:2601.08621v1 Announce Type: new 
Abstract: Recent advances in search-augmented large reasoning models (LRMs) enable the retrieval of external knowledge to reduce hallucinations in multistep reasoning. However, their ability to operate on graph-structured data, prevalent in domains such as e-commerce, social networks, and scientific citations, remains underexplored. Unlike plain text corpora, graphs encode rich topological signals that connect related entities and can serve as valuable priors for retrieval, enabling more targeted search and improved reasoning efficiency. Yet, effectively leveraging such structure poses unique challenges, including the difficulty of generating graph-expressive queries and ensuring reliable retrieval that balances structural and semantic relevance. To address this gap, we introduce GraphSearch, the first framework that extends search-augmented reasoning to graph learning, enabling zero-shot graph learning without task-specific fine-tuning. GraphSearch combines a Graph-aware Query Planner, which disentangles search space (e.g., 1-hop, multi-hop, or global neighbors) from semantic queries, with a Graph-aware Retriever, which constructs candidate sets based on topology and ranks them using a hybrid scoring function. We further instantiate two traversal modes: GraphSearch-R, which recursively expands neighborhoods hop by hop, and GraphSearch-F, which flexibly retrieves across local and global neighborhoods without hop constraints. Extensive experiments across diverse benchmarks show that GraphSearch achieves competitive or even superior performance compared to supervised graph learning methods, setting state-of-the-art results in zero-shot node classification and link prediction. These findings position GraphSearch as a flexible and generalizable paradigm for agentic reasoning over graphs.

تم تقديم إطار عمل جديد يسمى GraphSearch، الذي يوسع التفكير المعزز بالبحث إلى تعلم الرسوم البيانية، مما يمكّن التعلم من الرسوم البيانية بدون ضبط محدد للمهمة. هذا التقدم يعالج التحديات المتعلقة بالتعامل مع البيانات الهيكلية في الرسوم البيانية، والتي تزداد شيوعًا في مجالات متنوعة مثل التجارة الإلكترونية والشبكات الاجتماعية.

Se ha introducido un nuevo marco llamado GraphSearch, que extiende el razonamiento aumentado por búsqueda al aprendizaje de grafos, permitiendo el aprendizaje de grafos sin ajuste específico a la tarea. Este avance aborda los desafíos de operar con datos estructurados en grafos, que son cada vez más prevalentes en diversos dominios como el comercio electrónico y las redes sociales.

Un nouveau cadre nommé GraphSearch a été introduit, étendant le raisonnement augmenté par la recherche à l'apprentissage des graphes, permettant un apprentissage de graphes sans coupure spécifique à la tâche. Cette avancée répond aux défis liés à l'exploitation des données structurées en graphes, de plus en plus présentes dans divers domaines tels que le commerce électronique et les réseaux sociaux.

A new framework named GraphSearch has been introduced, extending search-augmented reasoning to graph learning, enabling zero-shot graph learning without the need for task-specific fine-tuning. This advancement addresses the challenges of operating on graph-structured data, which is increasingly prevalent in various domains such as e-commerce and social networks.

GraphSearch: Agentic Search-Augmented Reasoning for Zero-Shot Graph Learning

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Amelia Castellanos on How Generative AI and Immersive Commerce Can Be Strategic Levers for Next-Gen E-Commerce

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