Autonomous navigation is reshaping various domains in people’s life by enabling safe and efficient movement in complex environments. Reliable navigation requires path planning algorithms that compute optimal or near-optimal trajectories while satisfying task-specific constraints and ensuring obstacle avoidance. However, existing algorithms struggle with slow convergence and suboptimal solutions, particularly in complex environments, limiting their real-world applicability. To address these limitations, this paper presents the Multi-Strategy Enhanced Crayfish Optimization Algorithm (MCOA), a novel approach integrating three strategies: 1) Refractive Learning to enhance diversity and global exploration, 2) Stochastic Centroid-Guided Exploration to balance global and local search, and 3) Adaptive Competition-Based Selection to accelerate convergence and improve solution quality. Experimental results show that MCOA significantly improves the performance of 3D UAV path planning, reducing computation time by 69.2% and trajectory cost by 67.0% compared to 11 baseline algorithms, which demonstrates its effectiveness in autonomous navigation within complex environments.

In modern machine learning circuits and systems, the need for centralized, standardized data poses significant challenges, especially in privacy-sensitive, resource-constrained, real-time, or asynchronous environments. We introduce Eigen-Component Analysis (ECA), a quantum-inspired linear model that circumvents these requirements by leveraging intrinsic data structures. ECA uses an orthogonal transformation to extract meaningful features from decentralized, non-standardized data, ensuring high classification accuracy without extensive preprocessing. Unlike traditional linear models, ECA adapts to complex data distributions and shows superior performance in feature extraction and classification across various datasets. Experiments indicate that ECA achieves robust accuracy with fewer parameters, highlighting its potential where data centralization and standardization are impractical. Our work extends the feasibility of linear models in diverse scenarios, enhancing interpretability and efficiency in challenging data environments.

Reliability is essential for ensuring efficiency in LLM training. However, many real-world reliability issues remain difficult to resolve, resulting in wasted resources and degraded model performance. Unfortunately, today's collective communication libraries operate as black boxes, hiding critical information needed for effective root cause analysis.

RoCE services are sensitive to network failures and performance bottlenecks, which become more common as the RoCE network scales. In addition, some non-network problems behave like network problems and can waste troubleshooting time. However, existing mechanisms cannot quickly detect and locate network problems or determine whether the service problem is network-related.