Recent advances in Multimodal Large Language Models (MLLMs) have enabled joint reasoning over financial textual and visual inputs. However, they still struggle with financial terminology, logical consistency, and numerical computations. Moreover, while commercial large models perform well on reasoning tasks, their high inference costs limit their scalable usage in real world financial applications. We thus propose a cost-effective framework, CLER, that combines contrastive retrieval with step-wise reflection to improve reasoning performance. Also, the reasoning cost is only generated in the test stage when using commercial large models. CLER leverages FinErrorSet, a dataset of 8,000+ mistake correction pairs from diverse open-source MLLMs. A fine grained retriever is trained to identify structurally relevant errors for self-correction through individual reflection. Experiments on three benchmarks show that CLER consistently outperforms other baselines. To our knowledge, CLER is the first framework to use cross-model errors for financial reasoning.

Abstract In recent years, deep learning-based emotion recognition from electroencephalography (EEG) signals has garnered significant attention in brain-computer interfaces. However, effectively capturing local and global dependencies remains a challenge due to the complexities of EEG data. Furthermore, traditional convolutional neural networks and RNNs often struggle to fully explore the spatio-temporal relationships between different features. To address these issues, we propose an end-to-end model with the augmented capsule-gated Transformer to improve the performance of EEG emotion recognition, in which we learn cross-channel spatial features effectively, and the raw EEG signals are automatically weighted to emphasize key attributes. Subsequently, the capsule network extracts low-level and high-level spatial information, fully leveraging the potential insights within the signals. Building on this, an efficient Transformer is employed to model the relationships among different electrodes, allowing for a more in-depth analysis of the temporal dependencies across multiple features. Extensive experiments are conducted on the Dataset for Emotion Analysis using Physiological Signals (DEAP) dataset, and comparison results with existing state-of-the-art methods demonstrate the superior performance of the proposed method. Specifically, for the arousal and valence dimensions, the average recognition accuracies in subject-dependent experiments reach 93.51% and 94.24%, while the subject-independent experiments achieve average accuracies of 86.78% and 87.59%.

Diabetes self-management continues to exhibit wide interpersonal variability, a phenomenon insufficiently addressed by standardized Diabetes Self-Management Education (DSME) programs. The limitations of existing approaches stem largely from their implicit assumption of homogeneous cognitive structures, an assumption inconsistent with theoretical insights from conceptual metaphor theory and cognitive science indicating that individuals rely on divergent conceptual mappings to make sense of chronic illness. This study addresses this gap by conducting a large-scale computational analysis of 39,880 spontaneous patient self-disclosures from diabetes-related subreddits using MetaPro to uncover the underlying cognitive frameworks of diabetic patients. Comparative analysis demonstrated significant differences in cognitive conceptualizations between type 1 (T1D) and type 2 (T2D) diabetes patients. T1D discourse is dominated by an “action-monitoring” schema, whereas T2D discourse emphasizes a “process-mishap-component” schema. Gender comparisons further distinguish a male “metric-status-trajectory” profile from a female “burden-care-intuition” profile. These findings illuminate the deep cognitive heterogeneity underlying diabetes self-management and underscore the need for personalized, cognitively informed intervention strategies that attend to demographic and clinical distinctions.