Abstract
As recommender systems play an increasingly central role in shaping information exposure on platforms like YouTube, understanding the nature of the content they promote, especially in sensitive contexts, requires scalable and reliable labelling methods. This paper investigates the use of Large Language Models (LLM) to label YouTube videos based solely on their metadata. We propose a committee-based approach that aggregates predictions from an ensemble of seven state-of-the-art LLMs through majority voting. Using a novel dataset collected via simulated user interactions on YouTube, we analyse model agreement, labelling behavior, and the influence of model size. To assess label reliability, we also investigate the semantic coherence of label assignments. Our results show that LLM committees produce highly consistent labels in low-disagreement settings. These findings highlight both the promise and limitations of LLM-based annotation for auditing social networks.

Abstract
We study a major cyberattack at a large omnichannel grocery retailer that triggered a one-week shutdown of its online store and a corporate data breach. Using high-frequency panel data on more than 20,000 loyalty customers over 2019 and 2022, we implement a within-customer difference-indifferences design. Over the 13-week post-attack window, online transactions and online revenue both declined by approximately 10% relative to the counterfactual, with effects persisting across short-, medium-, and long-term horizons and no evidence of recovery. This contrasts with firm-level event studies, which show only short-term market reactions. The offline channel provided limited mitigation of the effects. Customers primarily substituted for alternative retailers rather than reallocating purchases within the firm, resulting in a net revenue decline for our industry partner. Effects were heterogeneous and customers with a higher concentration of online purchases, higher patronage (greater online purchase frequency and spending), and subscription ties exhibited greater shopping resilience, whereas customers with predictable shopping patterns who likely would have shopped during the affected period (and thus directly experienced the outage) were more prone to churn. This concentration of the effect among customers most likely to have been directly affected by the outage is more consistent with a substitution mechanism triggered by the disruption itself than with a generalised trust-loss interpretation, which would predict a similar response across informed customers regardless of their individual exposure to the disruption window.

Abstract
Background: Depressive symptoms are common among older adults and can significantly impact their quality of life. However, many older adults face barriers to accessing psychological treatment. Internet-based cognitive behavioral therapy (iCBT) is a promising alternative to face-to-face treatments, but its feasibility among older adults has been less extensively studied than in adult populations. Objective: This study evaluated the feasibility of guided iCBT for adults aged 55 years and older with mild to moderate depressive symptoms recruited from the general population. Methods: This study is a feasibility study with a single-group, pretest-posttest design (n=21), in which all participants received guided iCBT for 8 weeks. Assessments were conducted at baseline (T0) and after the intervention (T1). The primary outcome was feasibility, conceptualized as satisfaction, usability, engagement, and uptake of iCBT. Secondary outcome measures included depression severity, working alliance, and technical alliance. Results: Participants were mostly highly educated (13/21, 61.9%), female (18/21, 85.7%), had an average age of 59.85 (SD 4.19; range 55-68) years, and reported moderate digital literacy. Feasibility outcomes indicated high satisfaction and engagement and moderate usability. Working alliance was rated as good by both participants and coaches, and technical alliance was rated as moderate by the participants. There was a nonsignificant modest decrease in depressive symptoms (Cohen d=0.47). Of the 20 participants who started the intervention, all completed the first 2 modules, but completion declined across the remaining 6 modules, with only 1 (5%) participant completing all modules. Conclusions: This study found that guided iCBT has the potential to be a feasible option for older adults experiencing depressive symptoms, with participants reporting generally positive satisfaction, moderate engagement, and a moderate therapeutic bond with their coaches. However, below-average usability ratings and a moderate technical alliance suggest that some aspects of the platform require improvement. Future research should focus on improving usability and adherence, as well as testing the intervention in a larger and more diverse population. ©Khadicha Amarti, Mieke H J Schulte, Annet Kleiboer, Claire Rosalie van Genugten, Mardien Oudega, Artur Rocha, Heleen Riper.

Abstract
The rapid advancement of warehouse automation has increased the need for intelligent intralogistics solutions that enhance material handling efficiency and optimize space utilization. This research presents a simulation-based methodology that integrates Autonomous Mobile Robots (AMRs) with container loading optimization in a unified decision-support framework that dynamically synchronizes AMR routing with optimized truckload configurations, a feature not commonly addressed jointly in existing literature to improve warehouse operations. By leveraging a hybrid approach combining discrete event and agent-based simulation in FlexSim, the study evaluates the impact of AMR fleet size, routing strategies, and truckload configurations on overall logistics performance. A proof-of-concept industrial case study illustrates how different scenarios influence key performance metrics, such as total operation time and resource utilization. The findings demonstrate that synchronized AMR deployment and optimized container loading strategies contribute to increased throughput, reduced handling time, and enhanced logistics unit utilization. This work provides a framework for dynamic logistics planning, offering valuable insights for companies seeking to enhance warehouse efficiency and sustainability through simulation-driven decision support. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
For eight years, the Text2Story Workshop series has fostered a vibrant research community dedicated to narrative understanding, advancing shared insights into the challenges of modelling narrative structure in text. While earlier approaches laid important foundations, recent progress in Transformers and Large Language Models (LLMs) has fundamentally reshaped the field. Building on the increasing prominence of LLM-based contributions in recent editions, the ninth edition of Text2Story expands the focus toward agentic AI, where systems plan, reason, and interact over time using narratives as internal representations. Recent advances, including long-context architectures, instruction and preference-tuned models, retrieval-augmented generation, and discourse-aware prompting, have broadened the applicability of LLMs to complex narrative tasks. Nevertheless, reliably capturing fine-grained narrative structures remains challenging, particularly for event chains, temporal and causal relations, character development, and perspective consistency. These challenges are amplified in interactive and agentic settings, where narrative coherence, controllability, and reliability are critical. This edition of Text2Story explores both the opportunities and limitations of LLMs and agentic systems for narrative understanding, including the analysis of narratives generated by LLMs themselves with respect to consistency, hallucination, bias, and control. Through a diverse program of research papers, works in progress, demos, resources, and keynote talks, the workshop continues to advance narrative understanding in the era of foundation and agentic models.

Abstract
This study addresses the optimization of automated order-picking systems in retail warehouses through the integration of Autonomous Mobile Robots (AMRs) in a parts-to-picker system. A mixed-integer programming model with a lexicographic objective function is developed to optimize AMR planning while maintaining predefined pallet loading sequences. The model aims to minimize the makespan and the number of stock pallets used, and to maximize the continuity of AMR-pallet pairing. Computational experiments across 81 instances demonstrate that the model consistently achieves optimal makespan values in scenarios of moderate complexity. The results indicate that, while increasing the number of AMRs provides limited benefits in simple configurations, it significantly improves performance in complex scenarios. The research contributes to the literature on warehouse automation by providing a solid foundation for the optimization of AMR-assisted order-picking.