Abstract
The design and development of consumer products require an interdisciplinary approach, often constrained by time-consuming prototyping and manual decision-making processes. As product complexity increases and market demands evolve, the need for automation and intelligent collaboration becomes evident. This paper presents a case study on the design and virtual validation of a premium pen using a multi-agent system, leveraging the integration of large language models (LLMs) and software agents. This combination enables a rational representation of human expertise and interactions, streamlining the design process while enhancing adaptability. Using CrewAI, agents were configured with specialized tasks, collaborating to optimize design, select sustainable materials, and establish quality standards. The agents generated a markdown report and a 3D simulation using Blender and Python, ensuring efficient coordination for an ergonomic, sustainable, high-quality pen. By modeling the rational behavior of human experts, the system demonstrated how LLMs and multi-agent coordination can reduce decision overhead and improve collaboration. The results show that multi-agent systems streamline product development by reducing decision overhead, improving task delegation, and enhancing collaboration. The final design met strict virtual quality standards and aligned with market preferences. This study demonstrates the role of multi-agent systems and LLM integration in Industry 4.0, supporting digital prototyping and virtual simulations to replace traditional physical prototyping. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
City council minutes are typically lengthy and formal documents with a bureaucratic writing style. Although publicly available, their structure often makes it difficult for citizens or journalists to efficiently find information. In this demo, we present CitiLink, a platform designed to transform unstructured municipal meeting minutes into structured and searchable data, demonstrating how NLP and IR can enhance the accessibility and transparency of local government. The system employs LLMs to extract metadata, discussed subjects, and voting outcomes, which are then indexed in a database to support full-text search with BM25 ranking and faceted filtering through a user-friendly interface. The developed system was built over a collection of 120 min made available by six Portuguese municipalities. To assess its usability, CitiLink was tested through guided sessions with municipal personnel, providing insights into how real users interact with the system. In addition, we evaluated Gemini’s performance in extracting relevant information from the minutes, highlighting its performance in data extraction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

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Abstract
With the surge of multimodal models and the demand for effective image Information Retrieval (IR) systems, high-quality text-to-image datasets have become paramount. However, most existing datasets are primarily in English, limiting their applicability to multilingual settings. To address this, we introduce the pt-image-ir-dataset, a manually annotated resource for text-based Image IR in European Portuguese. The dataset comprises 80 diverse queries and a curated pool of 5,201 images, each annotated for relevance by multiple human judges. The proposed dataset is a step forward in supporting the development and evaluation of image IR systems for European Portuguese, addressing a clear gap in multilingual multimodal research. To this end, we have made our dataset publicly available, alongside baseline experimental results, demonstrating its suitability on the Image IR task across different retrieval paradigms, including traditional text-based lexical IR methods, semantic dense retrieval models based on language embeddings, cutting-edge vision-language models and proprietary black-box image retrieval systems. Results demonstrate that vision-language models, particularly OpenCLIP/xlm-roberta-base-ViT-B-32, significantly outperform other approaches (MRR = 0.610).

Abstract
Online retail is transforming the way distribution networks are managed. One prominent change is that retailers can now use their full network to fulfil orders. This process involves allocating orders to fulfilment nodes and, depending on the setting, can include other operational decisions, such as order consolidation, shipping mode selection and product substitution. This order allocation problem (OAOR) has garnered considerable attention in recent years. However, there is no comprehensive view of what has been done in the literature, nor a consistent terminology across papers, which makes it hard to position existing work and identify research gaps. To address these concerns, we conduct a systematic literature review, where we find over 60 articles contributing to the OAOR literature. From this review, we formulate the baseline problem, consider multiple extensions, and identify key problem characteristics. Additionally, we analyse and categorize the solution methods found based on the optimization mechanism, policy class, and incorporation of future information and learning. Our review points to several avenues for future research, both in problems and in solution methods.

Abstract