In-context learning (ICL) can significantly enhance the complex reasoning capabilities of large language models (LLMs), with the key lying in the selection and ordering of demonstration examples. Previous methods typically relied on simple features to measure the relevance between examples. We argue that these features are not sufficient to reflect the intrinsic connections between examples. In this study, we propose a curriculum ICL strategy guided by problem-solving logic. We select demonstration examples by analyzing the problem-solving logic and order them based on curriculum learning. Specifically, we constructed a problem-solving logic instruction set based on the BREAK dataset and fine-tuned a language model to analyze the problem-solving logic of examples. Subsequently, we selected appropriate demonstration examples based on problem-solving logic and assessed their difficulty according to the number of problem-solving steps. In accordance with the principles of curriculum learning, we ordered the examples from easy to hard to serve as contextual prompts. Experimental results on multiple benchmarks indicate that our method outperforms previous ICL approaches in terms of performance and efficiency, effectively enhancing the complex reasoning capabilities of LLMs. Our project will be publicly available subsequently.

Current existing methods are either not very discriminative or too complex. In this work, an effective and very simple plant recognition method is proposed. The main innovations of our method are threefold. (1) The feature maps of multiple pretrained convolutional neural networks and multiple layers are extracted; the complementary information between different feature maps can be fully explored. (2) Performing spatial and channel feature recalibration on each feature map enables our method to highlight salient visual content and reduce non-salient content; as a result, more informative features can be discerned. (3) In contrast to conventional transfer learning with end-to-end network parameters fine-tuning, in our method one forward process is enough to extract discriminative features. All recalibrated features are concatenated to form the plant leaf representation, which is fed into a linear support vector machine classifier for recognition. Extensive experiments are carried out on eight representative plant databases, yielding outstanding recognition accuracies, which demonstrates the effectiveness and superiority of our method obviously. Moreover, the retrieval experiments show our method can offer higher or competitive mean average precisions compared with state-of-the-art method. The feature visualization shows our learned features have excellent intra-class similarity and inter-class diversity for leaf species from the same genus.