Large language models (LLMs) are increasingly deployed as intelligent agents capable of executing complex real-world tasks through external tool interactions, but effective tool selection remains challenging due to the inherent limitations of real-world training data. These datasets suffer from severe tool imbalance following long-tail distributions, data scarcity for specialized tools, logic conflicts between user queries and available tools, rapidly evolving toolsets, and the presence of subpar samples including partially correct and dirty examples. Existing supervised fine-tuning (SFT) approaches struggle with these multifaceted challenges as they require abundant high-quality data, treat all labeled examples as ground truth regardless of quality, and lack the flexibility to generalize beyond specific query-tool pairings seen during training. While reinforcement learning (RL) offers a promising alternative through outcome-based learning, vanilla approaches like Group Relative Policy Optimization (GRPO) suffer from training instability due to conflicting reward signals and inefficient learning from weak signals. To address these issues, we propose TOOL-CURE, a novel method with two key improvements to GRPO: Proficiency-Scaled Curriculum Learning (PSCL), which organizes training into a two-stage curriculum that builds foundational skills on easier samples before progressing to harder ones, and Online Policy Guarding via Sample Screening (OPGSS), which continuously assesses rollout quality and masks dirty samples to prevent noisy gradients from destabilizing policy updates. Our approach enables stable and efficient learning from heterogeneous real-world data, resulting in a robust tool-selection agent that demonstrates significant improvements in accuracy and generalization capability. Our code is available at: https://github.com/einnullnull/TOOL-CURE.git.

Hyperspectral image (HSI) classification based on deep learning is a hot research topic. The convolutional model employs a single rectangular window to interpret the sample neighborhood features, whereas effective characterization of the complex spatial structure of HSI is still an unsolved problem. In this article, we propose a structure-adaptive convolutional neural network (SACNN) for HSI classification, which efficiently exploits the intrinsic spatial geometry information. Four novel strategies are designed to construct the proposed SACNN network. First, superpixel homogeneous region (SHR) sample generation is introduced to achieve neighborhood features within the intercepted rectangular window of the superpixel. Second, online batch-wise standardization uses zero padding to unify the size of inputs in the same batch, thereby realizing parallel processing of irregular inputs. Third, structure-adaptive convolution (SConv) and structure-adaptive average pooling (SAP) are correspondingly constructed to extract deep spectral, spatial, and geometric features from the effective mapping area of superpixels, and further aggregate the information within irregular boundaries. Finally, a sample-adaptive loss weight (SLW) scheme is designed to adjust the influence of different labels on the same input. Experimental results show that the overall classification accuracy of SACNN reaches 93.11%, 90.96%, and 85.04% for 15 randomly selected training samples per class on three HSI datasets, respectively, obtaining an improvement of 0.97%–2.97% with respect to the best-compared method.

Based on the encoding process, arithmetic codes can be viewed as tree codes and current proposals for decoding arithmetic codes with forbidden symbols belong to sequential decoding algorithms and their variants. However, arithmetic coding can also be modeled as a finite state machine and can be treated as a variable-length trellis code. The number of states used for decoding can be reduced and techniques used for convolutional codes such as the list Viterbi decoding algorithm can be applied on the trellis. The proposed approach provides a rich environment for the design of joint source/channel codes. The particular implementation presented here shows significant performance improvement over previous approaches.