Since there are various uncertain influences that affect the operation of the logistics supply chain, the existing supervision method cannot offer timely and efficient historical data, while the supply-demand relationship still needs enhancement. Therefore, this study puts forward a logistics supply chain risk dynamic...
Tingting Zheng· Journal of Advanced Manufact...· 0 citations
Abstract Contemporary large language models (LLMs) and reinforcement learning (RL) agents are inherently goal-seeking organisms: they optimize reward signals relentlessly, with no inherent ethical or epistemic constraints. While this intrinsic drive enables superhuman exploratory capability, it inevitably gives rise to...
Jiye Huang, Jizhang Huang· Zenodo (CERN European Organi...· 0 citations
The El-Rakhawi Framework for Cognitive Topological Metamaterials (CTM) introduces a revolutionary paradigm transforming passive matter into intelligent, adaptive systems. Addressing the fundamental limitation of static materials in extreme environments, CTM integrates higher-order topological insulators (HOTIs) with ph...
m el-rakhawi· Zenodo (CERN European Organi...· 0 citations
Today's healthcare systems represent complex adaptive systems that involve stochastic demand, tight coupling between subsystems, and nonlinearity of resource constraints. This paper proposes the Autonomous Healthcare Control System (AHCS), which brings together such approaches as stochastic state space models, Markov D...
Surendar Madhawa, Sahana Edwin, N. Srikumaran et al.· IGI Global eBooks· 0 citations
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Ultra-reliable low-latency communication (URLLC) is one of the hardest service classes that 6G has to carry, because a packet that misses its deadline is useless even if it eventually arrives. Routing based on fixed shortest paths ignores queue build-up, and link-state protocols that rebuild their tables every few mill...
Maruf Eshtursunov· Zenodo (CERN European Organi...· 0 citations
World models provide internal representations of environmental dynamics and support prediction, planning, simulation, and decision-making. However, errors in world models may persist when a system relies exclusively on its own internal representations, while external correction can introduce communication costs, confli...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Coordinating multiple unmanned aerial vehicles (UAVs) to assign targets and generate collision-free trajectories in dynamic, obstacle-dense environments is a challenging problem with applications in search-and-rescue, surveillance, and precision agriculture. Existing deep reinforcement learning approaches for joint tar...
Mohammed Abdelhak Bidi, Younes Guellouma, Chaker Abdelaziz Kerrache et al.· Drones· 0 citations
Evidence package for the manuscript "An Auditable Learning-Curve-Driven Automatic Policy Adjustment Framework for Engineering Reinforcement Learning Control Systems" (EAAI submission). The dataset contains the complete experiment ledger, the planner source code written by the diagnosis-driven agent, and the scripts tha...
The project studies whether training an agent across randomized environment physics can improve its performance on previously unseen conditions compared with training under fixed physics. It uses the LunarLander-v3 environment and Proximal Policy Optimization (PPO), alongside a custom PyTorch actor–critic implementatio...
Mohammed Jasim Salim· Zenodo (CERN European Organi...· 0 citations
The proliferation of wireless devices and intelligent environments has led to increasingly dense, interference-prone Wireless Local Area Network (WLAN) deployments. Recent amendments, such as IEEE 802.11ax/be, aim to enhance spatial reuse (SR) by optimizing spectrum efficiency. However, current SR initiatives face sign...
Oscar Arana, Javier Gómez, Carlos Tlapa et al.· Information· 0 citations
We study stochastic optimal control of rough stochastic differential equations (RSDEs). This is in the spirit of the pathwise control problem (Lions–Souganidis 1998, Buckdahn–Ma 2007; also Davis–Burstein 1992), with renewed interest and recent works drawing motivation from filtering, SPDEs, and reinforcement learning....
Peter K. Friz, Khoa Lê, Huilin Zhang· The Annals of Probability· 0 citations
Visual generation is becoming a general component of AI infrastructure, supporting content creation across design, entertainment, education, science, and industry. Generative and multimodal models have substantially improved single-pass synthesis. At the same time, multi-step visual creation requires spatial precision,...
Zihan Xing, Jin Wang, Rong Xia et al.· Preprints.org· 0 citations
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
MIT News · Artificial Intelligence· news.mit.eduOct 13, 2026
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.
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