LLM-based approaches have recently achieved impressive results in zero-shot stance detection. However, they still struggle in complex real-world scenarios, where stance understanding requires dynamic background knowledge, target definitions involve compound entities or events that must be explicitly linked to stance labels, and rhetorical devices such as irony often obscure the author’s actual intent. To address these challenges, we propose MSME, a Multi-Stage, Multi-Expert framework for zero-shot stance detection. MSME consists of three stages: (1) Knowledge Preparation, where relevant background knowledge is retrieved and stance labels are clarified; (2) Expert Reasoning, involving three specialized modules—Knowledge Expert distills salient facts and reasons from a knowledge perspective, Label Expert refines stance labels and reasons accordingly, and Pragmatic Expert detects rhetorical cues such as irony to infer intent from a pragmatic angle; (3) Decision Aggregation, where a Meta-Judge integrates all expert analyses to produce the final stance prediction. Experiments on three public datasets show that MSME achieves state-of-the-art performance across the board.

In recent years, infrared small target detection (ISTD) networks based on deep learning have achieved notable advances. However, these methods still face significant challenges when applied to the real world. Most of them lack the fundamental principles of small target detection in infrared imagery, which results in difficulties in distinguishing targets from complex backgrounds and poor interpretability. To address these challenges, an interpretable network architecture for ISTD, termed low-rank sparse decomposition state space models (LSDSSMs), is proposed. LSDSSMs use the principles of low-rank and sparse decomposition, incorporating dedicated modules for the low-rank space separation module and the sparse target extraction module. These modules facilitate the extraction of sparse representations for both low-rank backgrounds and small targets. In addition, a joint reconstruction module is employed to integrate these components, generating reconstructed images. Considering the unique imaging characteristics of infrared images and the sparse nature of small targets, a channel selection module (CSM) is proposed to enhance the extraction of sparse targets. To enhance the adaptability, stability, and resistance resistance of LSDSSMs in complex environments, robust state space models are integrated that combine local and global information representations. Furthermore, a multilevel loss function is introduced to enforce comprehensive constraints on low-rank backgrounds, sparse targets, and reconstructed images. This design improves not only the robustness of the LSDSSMs but also its performance across different scenarios. Extensive experimental results demonstrate that LSDSSMs surpass existing baseline methods in both qualitative and quantitative assessments, validating their effectiveness and reliability.