Vision Language Models (VLMs) have shown strong performance in multimodal understanding, offering promise for the circuit-to-netlist translation task. However, the diverse component symbols and complex connections in circuit images challenge VLMs in understanding physical layouts and reasoning for electrical connection logic. To address these, we propose Circuit-Think, the first multimodal reasoning framework for the automated circuit-to-netlist translation task, which employs a Trajectory-Guided Reinforcement Learning (TGRL) paradigm for structured logical reasoning on circuit images. Circuit-Think initializes reasoning capabilities through supervised fine-tuning (SFT) on image-netlist pairs, then optimizes reasoning trajectories and netlist generation decisions using TGRL. Firstly, TGRL introduces a step-by-step reasoning paradigm, which guides the model with stepwise reward functions to simulate the human cognitive trajectory of ``identifying ports, recognizing devices, and inferring connections''. Secondly, we customize a multi-level reward that maps reasoning and answers into graph structures and node sets, jointly optimizing logical consistency and netlist accuracy via graph similarity and set matching. Thirdly, TGRL contains a reflective learning mechanism for low-scoring samples, which corrects the reasoning trajectory through reference answers as hints, avoiding local optima caused by sparse reward signals or erroneous reasoning paths. Moreover, we construct a circuit image-netlist reasoning dataset with 3,100 samples, offering step-by-step annotations for converting circuit images to netlists. Extensive experiments demonstrate that Circuit-Think achieves SOTA netlist accuracy and significantly improves the accuracy of downstream tasks.

Time series anomaly detection (TSAD) is critical for ensuring the reliability of equipment in complex industrial environments. However, existing methods face significant challenges, including imbalanced data, lack of labeled samples, reliance on prior knowledge, poor generalization across diverse industrial scenarios, and low sensitivity to subtle anomalies. To address these limitations, we propose KARMAD, a novel framework that integrates Kolmogorov-Arnold Networks (KANs) for bidirectional function learning, adversarial training to enhance sensitivity to minor anomalies, and an adaptive thresholding strategy for improved precision and transferability. Evaluated on five public datasets against 14 state-of-the-art methods, KARMAD achieves state-of-the-art performance on all datasets, with an average F1 score improvement of 13.14%. Further experiments showcase its robustness to noise and adaptability in real-world industrial applications. KARMAD represents a significant advancement in developing scalable and accurate TSAD models suitable for diverse and high-stakes environments.

Aeromagnetic compensation is important in aeromagnetic detection. Traditionally, a calibration flight would be implemented to estimate the interference model. However, the magnetic interference is not constant during the mission flight, which makes the interference model constructed in the calibration flight inapplicable in the mission. To solve that problem, this paper proposes an evolutionary aeromagnetic compensation method based on Woodbury equation. With this method, the magnetic interference model parameters can be evolved during the mission flight on basis of the previous interference model and the updated acquired data. To evaluate the performance of the evolutionary method, both simulation and the experiment were conducted. The results indicate that the proposed method can effectively reduce the influence of changing magnetic interference. In the experiment, the standard deviation (STD) of measured data before compensation in the last mission flight is 1.2070nT, the traditional compensation can reduce the STD to 0.0863nT, while our evolutionary aeromagnetic compensation method can reduce STD to 0.0450nT. The results show that increasing changes of magnetic interference will make evolutionary aeromagnetic compensation vital for isolating signals created by geologic features from signals created by the aircraft.