Autonomous AI systems reveal foundational limitations in deterministic, human-authored computing architectures. This paper presents Cognitive Silicon: a hypothetical full-stack architectural framework projected toward 2035, exploring a possible trajectory for cognitive computing system design. The proposed architecture would integrate symbolic scaffolding, governed memory, runtime moral coherence, and alignment-aware execution across silicon-to-semantics layers. Our design grammar has emerged from dialectical co-design with LLMs under asymmetric epistemic conditions--creating structured friction to expose blind spots and trade-offs. The envisioned framework would establish mortality as a natural consequence of physical constraints, non-copyable tacit knowledge, and non-cloneable identity keys as cognitive-embodiment primitives. Core tensions (trust/agency, scaffolding/emergence, execution/governance) would function as central architectural pressures rather than edge cases. The architecture theoretically converges with the Free Energy Principle, potentially offering a formal account of how cognitive systems could maintain identity through prediction error minimization across physical and computational boundaries. The resulting framework aims to deliver a morally tractable cognitive infrastructure that could maintain human-alignment through irreversible hardware constraints and identity-bound epistemic mechanisms resistant to replication or subversion.

Node Assigned physics-informed neural networks for thermal-hydraulic system simulation: CVH/FL module Jeesuk Shin a,1, Cheolwoong Kim b,1, Sunwoong Yang c, Minseo Lee a, Sung Joong Kim b,, Joongoo Jeon a,d,e, a Department of Applied Plasma and Quantum Beam Engineering, Jeonbuk National University, Jeonju-si, Republic of Korea b Department of Nuclear Engineering, Hanyang University, Seoul, Republic of Korea c Cho Chun Shik Graduate School of Mobility, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea d Department of Quantum System Engineering, Jeonbuk National University, Jeonju-si, Republic of Korea e Graduate School of Integrated Energy-AI, Jeonbuk National University, Jeonju-si, Republic of KoreaAbstract Severe accidents (SAs) in nuclear power plants have been analyzed using thermal-hydraulic (TH) system codes such as MELCOR and MAAP. These codes efficiently simulate the progression of SAs, while they still have inherent limitations due to their inconsistent finite difference schemes. The use of empirical schemes incorporating both implicit and explicit formulations inherently induces unidirectional coupling in multi-physics analyses. The objective of this study is to develop a novel numerical method for TH system codes using physics-informed neural network (PINN). They have shown strength in solving multi-physics due to the innate feature of neural networks--automatic differentiation. We propose a node-assigned PINN (NA-PINN) that is suitable for the control volume approach-based system codes. NA-PINN addresses the issue of spatial governing equation variation Corresponding author Corresponding author Email addresses: sungjkim@hanyang.ac.kr (Sung Joong Kim), jgjeon41@jbnu.ac.kr (Joongoo Jeon) 1 These authors contributed equally to this work. In this phase, we evaluated the accuracy of the PINN methods for the hydrodynamic module. In the 6 water tank simulation, PINN and NA-PINN showed maximum absolute errors of 1.678 and 0.007, respectively. It should be noted that only NA-PINN demonstrated acceptable accuracy. To the best of the authors' knowledge, this is the first study to successfully implement a system code using PINN. Our future work involves extending NA-PINN to a multi-physics solver and developing it in a surrogate manner Keywords: FDM, PINN, Thermal-hydraulics, Control-volume approach1. INTRODUCTION Due to the extremely low frequency of severe accident (SA) in nuclear power plants (NPPs) and the limited availability of real-world accident data, SA-related research inevitably relies on the use of system codes to simulate hypothetical accident scenarios and assess the potential safety concerns. Widely used system codes, such as RELAP5/SCDAP, MAAP, and MEL-COR, model the physical behavior of NPP components and simulate accident progression by accounting for complex thermal-hydraulic (TH) and physicochemical interactions arising under SA conditions.

--Quantum computing has the potential to improve our ability to solve certain optimization problems that are computationally difficult for classical computers, by offering new algorithmic approaches that may provide speedups under specific conditions. In this work, we introduce QAOA-GPT, a generative framework that leverages Generative Pretrained Transformers (GPT) to directly synthesize quantum circuits for solving quadratic unconstrained binary optimization problems, and demonstrate it on the MaxCut problem on graphs. T o diversify the training circuits and ensure their quality, we have generated a synthetic dataset using the adaptive QAOA approach, a method that incrementally builds and optimizes problem-specific circuits. The experiments conducted on a curated set of graph instances demonstrate that QAOA-GPT, generates high quality quantum circuits for new problem instances unseen in the training as well as successfully parametrizes QAOA. Our results show that using QAOA-GPT to generate quantum circuits will significantly decrease both the computational overhead of classical QAOA and adaptive approaches that often use gradient evaluation to generate the circuit and the classical optimization of the circuit parameters. Our work shows that generative AI could be a promising avenue to generate compact quantum circuits in a scalable way. Quantum computing is rapidly emerging technology with significant potential across various domains, including finance [1], chemical simulations [2], material science [3], combinatorial optimization [4], and machine learning [5], among others. V ariational quantum-classical algorithms represent one of the most promising classes of quantum algorithms in different domains, showing potential for both fault-tolerant quantum computers and near-term noisy intermediate-scale quantum (NISQ) devices. The Quantum Approximate Optimization Algorithm (QAOA) [6] and many of its subsequent versions and customizations [7] belong to this class and demonstrate great potential due to their problem/application flexibility and compatibility with various quantum architectures. The original QAOA framework employs a fixed ansatz structure, which can limit expressibility and hinder performance, particularly on near-term quantum devices where circuit depth is limited. This rigid design may not capture the problem-specific features needed for efficient optimization. Such methods as ADAPT -QAOA [8] address this challenge by iteratively constructing the ansatz in a problem-informed manner. At each step, ADAPT -QAOA selects operators from a predefined pool based on their gradient with respect to the cost function, incorporating only those that contribute most significantly to improving the objective.

This paper presents the design and implementation of an AI vision-controlled orthotic hand exoskeleton to enhance rehabilitation and assistive functionality for individuals with hand mobility impairments. The system leverages a Google Coral Dev Board Micro with an Edge TPU to enable real-time object detection using a customized MobileNet\_V2 model trained on a six-class dataset. The exoskeleton autonomously detects objects, estimates proximity, and triggers pneumatic actuation for grasp-and-release tasks, eliminating the need for user-specific calibration needed in traditional EMG-based systems. The design prioritizes compactness, featuring an internal battery. It achieves an 8-hour runtime with a 1300 mAh battery. Experimental results demonstrate a 51ms inference speed, a significant improvement over prior iterations, though challenges persist in model robustness under varying lighting conditions and object orientations. While the most recent YOLO model (YOLOv11) showed potential with 15.4 FPS performance, quantization issues hindered deployment. The prototype underscores the viability of vision-controlled exoskeletons for real-world assistive applications, balancing portability, efficiency, and real-time responsiveness, while highlighting future directions for model optimization and hardware miniaturization.

Large language models (LLMs) have become increasingly embedded in organizational workflows. This has raised concerns over their energy consumption, financial costs, and data sovereignty. While performance benchmarks often celebrate cutting-edge models, real-world deployment decisions require a broader perspective: when is a smaller, locally deployable model "good enough"? This study offers an empirical answer by evaluating eleven proprietary and open-weight LLMs across ten everyday occupational tasks, including summarizing texts, generating schedules, and drafting emails and proposals. Using a dual-LLM-based evaluation framework, we automated task execution and standardized evaluation across ten criteria related to output quality, factual accuracy, and ethical responsibility. Results show that GPT-4o delivers consistently superior performance but at a significantly higher cost and environmental footprint. Notably, smaller models like Gemma-3 and Phi-4 achieved strong and reliable results on most tasks, suggesting their viability in contexts requiring cost-efficiency, local deployment, or privacy. A cluster analysis revealed three model groups -- premium all-rounders, competent generalists, and limited but safe performers -- highlighting trade-offs between quality, control, and sustainability. Significantly, task type influenced model effectiveness: conceptual tasks challenged most models, while aggregation and transformation tasks yielded better performances. We argue for a shift from performance-maximizing benchmarks to task- and context-aware sufficiency assessments that better reflect organizational priorities. Our approach contributes a scalable method to evaluate AI models through a sustainability lens and offers actionable guidance for responsible LLM deployment in practice.

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A fully automated warehouse system is changing the way we shop for groceries. GROCERIES IN 5 MIN: Imagine a grocery store where your entire order is picked, packed and ready for delivery in just five minutes without a single human hand touching your food. BRAVE NEW WORLD: Anthropic – the company behind the artificial intelligence platform Claude – anticipates that digital AI employees will appear on corporate networks in the next year, the organization's top security leader informed Axios. THESE FUELS ARE OUT: Imagine powering your boat not with gasoline but with clean hydrogen fuel. That's exactly what Yamaha, together with Roush Industries and Regulator Marine, is working on right now.

We consider the problem of non-stationary reinforcement learning (RL) in the infinite-horizon average-reward setting. We model it by a Markov Decision Process with time-varying rewards and transition probabilities, with a variation budget of $\Delta_T$. Existing non-stationary RL algorithms focus on model-based and model-free value-based methods. Policy-based methods despite their flexibility in practice are not theoretically well understood in non-stationary RL. We propose and analyze the first model-free policy-based algorithm, Non-Stationary Natural Actor-Critic (NS-NAC), a policy gradient method with a restart based exploration for change and a novel interpretation of learning rates as adapting factors. Further, we present a bandit-over-RL based parameter-free algorithm BORL-NS-NAC that does not require prior knowledge of the variation budget $\Delta_T$. We present a dynamic regret of $\tilde{\mathscr O}(|S|^{1/2}|A|^{1/2}\Delta_T^{1/6}T^{5/6})$ for both algorithms, where $T$ is the time horizon, and $|S|$, $|A|$ are the sizes of the state and action spaces. The regret analysis leverages a novel adaptation of the Lyapunov function analysis of NAC to dynamic environments and characterizes the effects of simultaneous updates in policy, value function estimate and changes in the environment.

Machine learning interatomic potentials, particularly those based on deep equivariant neural networks, have demonstrated state-of-the-art accuracy and computational efficiency in atomistic modeling tasks like molecular dynamics and high-throughput screening. The size of datasets and demands of downstream workflows are growing rapidly, making robust and scalable software essential. This work presents a major overhaul of the NequIP framework focusing on multi-node parallelism, computational performance, and extensibility. The redesigned framework supports distributed training on large datasets and removes barriers preventing full utilization of the PyTorch 2.0 compiler at train time. We demonstrate this acceleration in a case study by training Allegro models on the SPICE 2 dataset of organic molecular systems. For inference, we introduce the first end-to-end infrastructure that uses the PyTorch Ahead-of-Time Inductor compiler for machine learning interatomic potentials. Additionally, we implement a custom kernel for the Allegro model's most expensive operation, the tensor product. Together, these advancements speed up molecular dynamics calculations on system sizes of practical relevance by up to a factor of 18.

Abstract-- In this paper, we address the problem of optimizing flows on generalized graphs that feature multiple entry points and multiple populations, each with varying co st structures. We tackle this problem by considering the multi - population Wardrop equilibrium, defined through variation al inequalities. We rigorously analyze the existence and uniq ueness of the Wardrop equilibrium. Furthermore, we introduce an efficient numerical method to find the solution. In particula r, we reformulate the equilibrium problem as a distributed optimization problem over subgraphs and introduce a novel Hessian Riemannian flow method--a Riemannian-manifold-projected Hessian flow--to efficiently compute a solution. Fi - nally, we demonstrate the effectiveness of our approach thr ough examples in urban traffic management, including routing for diverse vehicle types and strategies for minimizing emissi ons in congested environments. In traffic management, each driver--whether operating a car, SUV, or truck--selects the route they perceive to be the shortest.