The report gives a very clear, systematic exposition of the major shortcomings in current Large Language Model (LLM)-based automated reward design methods for reinforcement learning, and after a careful analysis of EUREKA and LEARN-Opt, it naturally and elegantly identifies two fundamental issues: dependence on environment source code and absence of mathematical guarantees for policy optimality. In view of the existing limitations, this paper proposes a well-designed framework that combines Vision-Language Models (VLMs) for visual observation, Graph-of-Thoughts for hierarchical task decomposition, and Potential - based Reward Shaping for theoretical safety guarantees, hence naturally solving the problem of code dependency by using pure visual feedback and reducing generation variance by structured decomposition. The paper presents a method that achieves policy invariance by means of mathematically constrained reward generation, and accordingly gives a clear, logically organized discussion of the problem analysis, theoretical background, proposed approach, implementation architecture, current results, and experimental validation plan. It makes a very natural and important contribution to building robust, generalizable, and theoretically sound automated reward design systems.

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Bearing fault diagnosis in modern industrial applications frequently encounters severe challenges due to data privacy fragmentation, domain shifts induced by varying working conditions, and the insufficiency of single-sensor information. To address these critical bottlenecks, this paper proposes a multi-modal domain generalization federated learning framework. Specifically, a dual-branch 1D-CNN feature extraction network is constructed to independently process horizontal and vertical vibration signals, which are then integrated using multi-scale convolutional blocks and a Squeeze-and-Excitation (SE) channel attention mechanism for robust feature fusion. Furthermore, to mitigate the client drift issue caused by non-IID data distributions across different rotational speeds and loads, a FedProx-based federated optimization strategy with a dynamic proximal term regularization is implemented. Extensive experiments conducted on the XJTU-SY bearing dataset demonstrate that the proposed framework achieves an overall classification accuracy of 67.20% and an F1-score of 67.32% under strict differential privacy constraints (DP-SGD noise multiplier = 0.05). Compared with the baseline federated model, the optimized version provides a significant performance gain of +12.37% in diagnostic accuracy. The proposed approach successfully strikes an optimal balance between data privacy protection and cross-condition domain generalization, offering a reliable paradigm for distributed industrial intelligent maintenance.

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Event-based visual-inertial odometry (VIO) is often considered a promising sensing solution for low-power micro-robots because event cameras naturally produce sparse, low-latency measurements. However, an efficient sensor does not automatically lead to an energy-efficient system. This paper adopts a system-level view, treating the energy consumption of event-based VIO as an outcome of the interaction between event representation and state-estimation strategy. it examines how information density and computational demand jointly shape system power. From this analysis, it introduces the principle of representation–estimation coupling consistency. Evidence from a broad range of systems supports this principle. The framework also suggests where future low-power VIO systems are likely to converge: event-driven, semi-continuous, and jointly adaptive architectures under strict power budgets. Finally, it provides design guidance for three power ranges and discuss open problems. This study aims to offer a unified perspective for building energy-efficient perception systems for resource-constrained micro-robots.

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To address background noise that reduces speech intelligibility in voice communications, this paper compares the noise-reduction performance of FIR (window function), Butterworth IIR, and adaptive LMS filters. Experiments using speech signals with white noise, pink noise, and real-world environmental noise under input SNRs of 0–10 dB show that at 0 dB input SNR, the LMS filter achieves an output SNR of 8 dB and MSE of 0.01, significantly outperforming FIR (6 dB, 0.07) and IIR (5 dB, 0.08). At an input SNR of 10 dB, the performance gap narrows to 1 dB. The IIR filter has the lowest order (5th) and minimal computational cost; the FIR filter (65th) offers the best linear phase; the LMS filter (32nd) exhibits the strongest adaptability at low SNRs. Overall, LMS prioritizes adaptability, IIR prioritizes efficiency, and FIR suits phase-sensitive applications. This study provides quantitative references for noise suppression in voice communication systems.

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