The article provides a comprehensive theoretical and methodological substantiation of the systemic transformation processes affecting enterprise competitiveness factors under the severe security and macroeconomic shocks of martial law, while defining strategic priorities for post-war recovery and European integration. The empirical analysis demonstrates that traditional, static sources of competitive advantage, rooted in economies of scale, cost leadership, and concentration of tangible assets, have significantly lost their initial efficiency and transformed into critical vulnerabilities due to threats of physical destruction, resource scarcity, and logistical disruption. To address these challenges, the research develops a conceptual matrix mapping the correlation between martial environment disruptions and targeted managerial innovations. The study verifies a fundamental shift in the hierarchy of competitiveness toward dynamic capabilities and organizational resilience. Elements such as energy autonomy, advanced digital maturity, hyper-flexible logistics, multi-sourcing, and entrepreneurial courage of the management have evolved from auxiliary technical inputs into primary drivers of market reliability. The human capital and labor collective are identified as the main actors executing these survival strategies on a practical level. Furthermore, the paper substantiates that long-term international competitiveness during the post-war reconstruction period cannot rely solely on reactive crisis tactics. Successful integration into the global economic space and access to foreign funding directly depend on a structural modernization of business models. The study outlines that implementation of Industry 5.0 standards, generative artificial intelligence integration, supply chain digitization, and strict ESG compliance are mandatory prerequisites for securing international investment and building a highly adaptable, inclusive, competitive corporate framework.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 citations· ⚡17
This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9
This paper presents a comprehensive overview of the Ultralytics YOLO family, emphasizing architectural evolution, benchmarking, deployment, and emerging directions from YOLOv5 through YOLO27, and examines detection, segmentation, depth, classification, pose, oriented detection, tracking, export, quantization, and deplo...
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.