What if machines could discover human behavioral patterns better than experts? Traditional behavioral modeling in economics depends on costly manual refinement by domain experts, severely limiting scalability and discovery potential. We introduce AutoBM, an automated behavioral modeling framework leveraging large language models (LLMs) to systematically generate, evaluate, and refine interpretable behavioral models directly from human behavior data. AutoBM represents candidate models as structured natural language specifications, explicitly defining symbolic terms along with their tunable parameters, interpretations, and design rationales. AutoBM leverages LLMs to automatically translate each language specification into executable code, optimize tunable parameters, and evaluate model performance. Utilizing LLM-guided search strategies, AutoBM iteratively recombines and improves models at the term level, closely mirroring human expert practices. Experiments conducted across three distinct strategic environments (the ultimatum game, repeated rock-paper-scissors, and continuous double auctions) demonstrate that AutoBM-generated models consistently outperform leading manually crafted models, achieving significant improvements in prediction accuracy while maintaining clear interpretability. Our results demonstrate that automated frameworks can not only match but systematically exceed human expertise in behavioral modeling, fundamentally changing how we understand strategic human behavior.

Understanding the roles of genes, phenotypes, and diseases is crucial for advancing biomedical research. However, efficient and accessible retrieval of biomedical knowledge remains a challenge due to the complexity of the relevant data. We introduce BioRAGent, an intelligent biomedical assistant that combines Tool-augmented retrieval-augmented generation (RAG) with a multiagent system. Leveraging the ability of large language models, BioRAGent facilitates natural language queries about genes, phenotypes, diseases, and their interrelationships. BioRAGent employs three specialized agents: Guide (query optimization), Retriever (data retrieval), and Reviewer (answer validation) to access authoritative biomedical databases and to generate accurate responses. We evaluate the performance of BioRAGent on a benchmark of eleven single-hop and three multi-hop tasks, demonstrating superior results compared with state-of-the-art models. User evaluations highlight the practicality and robust user experience of BioRAGent, particularly in handling complex multi-hop queries. Moreover, ablation experiments validate the contribution of each agent in improving retrieval accuracy.

Speech Emotion Recognition (SER) is a significant task for human communication. In the recent years, Mel-frequency Cepstrum Coefficient (MFCC) feature can be usually utilized in the related tasks of speech emotion recognition. In this study, we developed a multi-head-attention CNN model with auxiliary task of gender task. Base on proposed model, we explore the effect of different time-scale MFCCs and different combination of them as input on the performance of proposed model. Experimental results show that MFCC having higher resolution in time-scale as input can help model achieving better performance of speech emotion recognition with a moderate range. Also, it can help model achieving better performance to combine different time-scale MFCCs appropriately.

Image segmentation is an important step in image processing. Segmentation based on threshold is a common method. Searching the suitable threshold vector is essentially an optimization problem, especially for color image which have higher dimensions and complexity. This paper proposes a merged Biogeography-Based Optimization algorithm (MBBO) for color image segmentation based on threshold. We merge a mutation operation into the migration operator of BBO to enhance the global search ability. Then we merge a chemotaxis operation into the mutation operator of BBO to enhance the local search ability. A greedy selection method is also used to further improve the performance and reduce computation complexity. Experimental results show that MBBO obtains better optimization performance, stronger stability and faster running speed compared with other existing algorithms.