Large Language Model (LLM) has achieved great success in many fields over recent years, demonstrating a promising future. Nevertheless, hallucination remains a major impediment that limits the usability and reliability of LLM. In order to mitigate hallucination, researchers have proposed various approaches, among which self-reflection stands out. While the initial optimism about pure LLM self-reflection slowly fades away, its methodologies are becoming more complicated and interweaved with other approaches. Due to fact that the boundaries between self-reflection and many of its synonyms are ambiguous, and that researchers keep coining new concepts and terminologies that may overlap and intertwine intricately with each other, this paper aims to provide a clear definition of self-reflection and some frequently used terminologies. In addition, given that currently there is no systematic work sorting out papers in this field, this paper fills this gap by classifying different self-reflection methodologies and presenting paradigmatic researches in each category. By the end, this paper discusses the possible limitations and directions of future development in this field, predicting that self-reflection might evolve into a component in hybrid LLM training approaches that is critical to hallucination alleviation.

The accuracy of fault diagnosis for rolling bearings degrades sharply when operating conditions shift. Existing high-precision classifiers often experience a drop of over 50% in predictive accuracy when speed or load fluctuates, which seriously jeopardizes the reliability of industrial equipment health monitoring. Titan, a recently proposed architecture for long-context language modelling, tackles a similar challenge of maintaining performance across varying contexts. TitanDiag adapts this mechanism to fault diagnosis. The underlying rationale is that a persistent memory accumulates evidence across operating conditions and stabilizes predictions when the current segment alone is ambiguous. The architecture places Titan's dual-path memory (a long-term store gated by surprise plus a short-term FIFO buffer) inside a Transformer encoder. The multi-view front-end provides three complementary representations for every vibration segment, namely the raw waveform, the Fourier magnitude spectrum, and the continuous wavelet transform scalogram. At inference, Monte Carlo dropout produces per-prediction uncertainty scores that align naturally with Titan's surprise metric. On the CWRU and PU bearing benchmarks, TitanDiag attains 99.25% accuracy on the challenging PU-C2 low-speed condition, where TimeMachine and TSCMamba drop to 41.68% and 60.20%, respectively. The mean error-detection AUROC reaches 0.9668, well above the best baseline of 0.9391, demonstrating that the memory-driven variance inflation produces uncertainty estimates that are closely aligned with actual misclassification patterns.

Online monitoring of welding quality is a key technology in intelligent manufacturing and process closed-loop control. Its core essence is the paradigm shift from passive post-weld inspection to in-process active perception, real-time diagnosis, and closed-loop feedback. Currently, technological development in this field advances along three main paths: first, vision-based detection technology, which provides process information by analyzing weld pool morphology, weld geometry, and surface defects; second, multi-modal sensor-based detection technology, which utilizes acoustic emission, arc/spectrum, thermal imaging, and ultrasonic methods to reflect the physical state and internal changes of the welding process; third, multi-source information fusion technology, which integrates the complementary advantages of multi-source heterogeneous sensing data to enhance diagnostic accuracy and robustness. This paper systematically reviews online monitoring technologies for welding quality: first, it analyzes single-modal detection methods based on vision and multi-sensing; then, taking fusion hierarchy as the dimension, it organizes the technical framework of data-level, feature-level, and decision-level fusion, with emphasis on the frontier progress of deep learning-driven feature-level fusion; finally, it envisions future development trends and key challenges from three dimensions—advanced sensing innovation, large-model enablement, and digital twin.

As the consumer market continues to expand, relatively homogeneous approaches to industrial product appearance design can no longer meet consumers' changing needs. This study aims to examine the sustainability assessment and improvement of generative AI in industrial product appearance design, with the goal of adapting to evolving consumer demands while promoting continuous innovation. An analysis of the requirements for industrial product appearance design shows that the application of generative AI offers significant advantages. It can not only reduce production costs but also enhance the market appeal of products. Under these conditions, the application of generative AI in industrial product appearance design is becoming increasingly widespread and demonstrates promising prospects for future development.

Predation is a key biological factor influencing population dynamics, yet traditional teaching methods often struggle to visually illustrate its synchronous periodicity and causal cycles. By leveraging AI to build a predation model, we have developed a predator-prey simulation system. The web-based platform employs a three-pane layout to integrate ecological simulations, data charts, and functional controls. It supports customizable parameters, allows for comparisons between J-shaped and S-shaped growth curves, and includes a CSV data export feature, all of which help foster students' interdisciplinary literacy.

With the development of intelligent transportation and intelligent connected vehicles, the accuracy of traffic sign recognition directly affects intelligent driving decision-making and driving safety. Nevertheless, traffic sign recognition suffers from issues such as blurred images, reduced contrast and difficult feature extraction under severe weather including rainy, foggy and nighttime conditions. To tackle the above problems, this paper proposes a joint model integrating image enhancement and recognition. A coding-attention-decoding structured image enhancement module is adopted to strengthen the edges of traffic signs against image quality degradation caused by severe weather. Meanwhile, a loss optimization model based on YOLO is established to reduce the missed detection rate and false detection rate of target recognition. Finally, severe weather data are simulated based on public datasets, and experiments verify the effectiveness of the proposed joint model, which provides effective support for the subsequent technological development of intelligent vehicles.