Aug 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
Abstract The historical legacy of friendship between India and Russia is firmly rooted in defense, energy and diplomatic cooperation. However, taking into consideration the current demands of the rapidly growing world of Artificial Intelligence and digital technologies, both these powerful nations should look beyond the traditional areas of partnership for building a truly sustainable partnership. Both nations are currently facing socio-economic problems like rising inflation, regional imbalances, income inequalities, poverty and unemployment. Though the individual governments of India and Russia are implementing welfare measures to solve these problems and are cooperating in the fields of science and technology, still there are limitations. This paper tries to understand the gaps in India-Russia collaboration in the field of science and technology and analyzes how the cooperation in this field could enhance the promotion of human development vis-à-vis societal development. The paper uses qualitative research methodology based on secondary data like bilateral agreements, policy documents, government reports amongst others. The aim of the paper is to find out how science and technological cooperation could bring positive changes in the improvement of education, health, employment and overall living standards of the people in both nations. The major finding of the paper is that though both the nations have developed treaties of cooperation in the field of Science and Technology in past years, there is a need to deepen these ties and extend them to societal growth and development. The paper concludes that Science and Technology can bring revolutionary changes in societal development through proper mapping of scientific collaboration, technological innovation and sharing of knowledge to each parameter of societal development including social equity and inclusion. In other words, both the nations should emphasize the direct impact of their research and industrial partnership on overall well-being of their populations, the economic growth and improvement of quality of life and happiness of their citizens
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.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.