The Organization of Warfare and the Doctrine of Hard Power Under Fluid Conditions: Mosaic Forces, Kill Webs, Algorithmic Opacity and the Algorithmic Doctrine of War
Oct 2026· Zenodo (CERN European Organization for Nuclear Research)· 3 citations· 3 references
Military Strategy and Technology
Abstract
The architecture of warfare is undergoing a structural shift driven by the integration of artificial intelligence, autonomous systems and advanced data analytics into the military apparatus. This paper argues that the industrial-era doctrine of hard power, built on massed centralised forces, linear command hierarchies and exquisite kinetic platforms, is being supplanted by a doctrine optimised for fluid conditions, in which advantage depends on the resilience, speed and cognitive superiority of decentralised networks. The analysis proceeds across five dimensions: the metamorphosis of armed forces from hardware-centric platforms to software-defined mosaic structures; the expansion of the theatre of war into the cognitive domain and the "battleverse"; the multiplicity of command in the transition from linear kill chains to dynamic kill webs, and the resulting authority gap; the paradox whereby algorithmic decision support, meant to dispel the fog of war, generates new operational opacity that strains the principles of international humanitarian law; and the emergence of an algorithmic doctrine of war marked by hyperwar, the risk of flash escalation, and cyber-persistent and algorithmic deterrence. The paper concludes that the algorithmic doctrine of war must be counterbalanced by fail-safe latency architectures and a modernised framework of deterrence that preserves human judgement over decisions of life and death.
This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.
F. Xia, Amir Zamir, Zhi-Yang He et al.· 2018 IEEE/CVF Conference on...· 1,069 citations· ⚡112
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