Most existing music recommendation systems struggle to perceive users’ implicit emotional states and fail to adapt dynamically to evolving preferences in emotionally rich, context-sensitive scenarios. To address this limitation, we propose an emotion-aware conversational music recommender built on a multiagent system. The system incorporates specialized agents for emotion recognition, semantic intent analysis, and contextual understanding. It distinguishes between explicit emotions, which are directly expressed by the user (e.g., “I feel anxious”), and implicit emotions inferred from contextual cues such as time, environment, or behaviors the user may not be fully aware of. A dual-memory mechanism models long-term musical preferences using a linear decay function and captures short-term, emotion-driven preferences using exponential decay. To enrich music content understanding, multisource information fusion combines streaming platform suggestions with rich metadata from external repositories. The system employs a large language model (LLM) to conduct multiturn dialogues and generate personalized, explainable recommendations. Experimental results show that the proposed approach significantly outperforms existing platforms (e.g., Spotify and Last.fm) in recommendation accuracy, ranking performance, and Hit Ratio@K. These findings underscore the effectiveness of integrating multiagent collaboration, emotion modeling, memory-augmented user profiling, and multisource data fusion for adaptive, user-centric music recommendation.

Language models are trained to predict the next words for given content. The question arises: do large language models (LLMs), which have achieved technological breakthroughs in natural language generation, possess decision-making abilities as humans? The present article lets a series of LLMs do cognitive psychology experiments to explore this hypothesis. More specifically, we first identified a choice problem set and then conducted experiments following the certainty equivalent (CE) paradigm. We find that much of LLMs’ behaviors are consistent with humans: the fourfold pattern of risk attitudes can also be observed across the most LLMs subjects. Particularly, LLMs are risk-seeking and risk-averse for gains with low and high probabilities, LLMs are risk-averse and risk-seeking for losses with low and high probabilities. Furthermore, LLMs subjects exhibit greater rationality in losses than in gains, and less rationality during gains compared to losses. The exponential values of the value function and weighting function, fitted based on the data from LLMs subjects, satisfy the requirements of cumulative prospect theory (CPT). Taken together, these results will further enhance the theoretical foundations to understand the LLMs decision-making capabilities and provide novel tools from cognitive psychology for improving the interpretability of LLMs.

The high volatility, seasonality and complex trading environment of agricultural futures markets present significant challenges to the dynamic adaptability of algorithmic trading systems. Traditional methods rely on fixed-scale image analysis and lack adaptability, making them unsuitable for varying volatility conditions. Therefore, this study proposes an AI agent for agricultural futures trading decisions. First, an agricultural futures adaptive volatility rate serves as a computational tool to dynamically identify high-volatility regions and generate finer-scale candlestick charts. Second, the agent uses Vision-Language Large Model with robust image comprehension to analyze the multiscale candlestick charts and extract key features such as trend direction and technical patterns. Subsequently, a Large Language Model with advanced natural language understanding and logical reasoning serves as the “decision-making brain”, evaluating market trends and making buy/sell decisions. Finally, the agent refines its decision logic through a multimodal feedback mechanism that combines numerical and textual information from ongoing interactions with the environment, thereby enhancing system adaptability and robustness. Experimental results indicate that this framework significantly improves the accuracy, stability, and risk control of trading strategies, offering valuable insights for human decision-making. In addition, it demonstrates potential applicability in other financial markets such as stock indices, energy, and major commodities, providing an innovative solution for intelligent trading in complex market conditions.

Social fintech envelopes social networks within financial concepts and tokenization, and its complexity requires innovative technological solutions and theories to meet user demands and achieve sustainability. The first thing is the mapping principle between physical real society and virtual digital world. Therefore, this article uses online gameNova Empireas a case study, aiming at using multiplex networks to understand the physical–digital structural consistency for social fintech sustainable development. Specifically, we first proposed an eight-layer multiplex network to model the complex gaming behaviors for players. Furthermore, we analyze the structural properties and social balance of the social networks. Particularly, the layers in multiplex networks composed of positive behaviors has higher reciprocity than the layers composed of negative behaviors, the out-degree distributions of nodes in the layers composed of negative behaviors basically conform to the power-law distribution, and the small-world phenomenon is also common in virtual game. The experimental results prove the structural consistency between physical and digital. Finally, new solutions for social fintech and mobile Internet industry are proposed based on the mapping principle, which will provide technical supports for the realization of sustainable development and social responsibility of social fintech.

Remote Sensing Image Generation (RSIG) offers a viable solution to the high data collection costs by facilitating the generation of large datasets. However, it is often hindered by the complexity of tasks and the diverse requirements of generated images. This paper presents the RS-Agent system, a novel approach that harnesses the capabilities of Large Language Models (LLMs) within an innovative agent solution, effectively addressing these issues. The system comprises a Task Agent, a Prompt Agent, and a LoRA Agent, each performing crucial roles in task decomposition, prompt generation, and scene-specific fine-tuning, respectively. Anchored by Diffusion models and employing natural language dialogues for interaction, the system aligns closely with user intent and produces high-quality results. Experimental results demonstrate the efficacy of the RS-Agent system in managing diverse RSIG tasks, adapting to various input forms, and generating high-quality results.

As a common phenomenon that often appears on social platforms, news sites, and community forums, topics have played an irreplaceable role in public opinion and social governance. Meanwhile, people's daily lives are increasingly dependent on the breeding, transformation, and attenuation of hot topics. This article aims to discuss the problem about topic duration, that is, what are the principle factors that affect topic duration? Why do some topics survive longer and even generate subtopics, while other topics disappear rapidly? To answer these questions, we innovatively use 104 121 alliance chat content inNova Empire IIfrom July 2023 to December 2023 as a case study. Dynamic topics trajectories are first obtained from a novel multilevel association model. Then, a potential factors system based on the dimensions of topic properties, topic users, and social network is established to quantitatively evaluate the influence for different factors. Experimental results from a robust statistical analysis framework demonstrate that higher topic discussion intensity, more content from opinion leader, faster information diffusion, and closer intertopic correlations will significantly improve the topic duration. Finally, a series of strategies are proposed to promote the design of social system applications from the perspectives of online social group.

With the transformation of human life into virtual worlds based on the reality, current researches of smart cities and sustainability should integrate the factors of virtual social behaviors. Virtual games are an ideal domain and tools for exploring social science. Therefore, our work innovatively using Nova Empire as a case study to reveal the social group long-term survival via complexity analysis of behavior graph structural properties for building smart cities. Specifically, behavioral data of 101424 players and 5324 alliances from September 2021 to February 2022 are used. Our observations show that the phenomenon of “gap of wealth” in real society also exists in the virtual game. Meanwhile, the player online time is significantly positive related to alliance survival time, which is the foundation of our work. Then, correlation and regression analysis are performed to understand the significance of different structural properties on alliance survival time. Our original findings demonstrate that larger alliance, more subgroups, balanced player distribution, and frequent behavioral interactions will promote long-term survival of the alliance. Small subgroup and a relaxed social environment can improve player online time. Furthermore, we transfer the conclusions from virtual game to real society based on the mapping principle. Finally, new virtual tools and solutions for policymakers to improve smart cities and sustainable society ecosystem, and operation strategies for game designers to improve players retention rate are proposed.

With the constant fusion of virtual and reality, a new vision of human beings has emerged—Metaverse. At present, the development of metaverse is still in its infancy, and although the industry has put feverish investment into it, there are still many problems that need to be discussed in academia. The first thing is user engagement. Metaverse relies heavily on massive online users to realize its social value. In other words, user engagement is the foundation of the metaverse ecosystem. Therefore, our research uses Nova Empire as a case study, aiming at understanding metaverse engagement via complexity analysis of social patterns and reward mechanisms. Specifically, the behavioral data of 46 954 players in September 2021 are used for analysis. Our observations show that social behavior is not the main factor for user engagement. Then, we perform a correlation analysis of trigger times for different gaming behaviors to verify the above observations. The results prove that the main factors affecting user engagement are game tasks and reward mechanisms in the early stage and social gaming behaviors environment with alliances in the middle and late stages. Finally, we discuss the implications of our findings for the design of future games and propose operation strategies on user engagement to improve the metaverse ecosystem and other online social space.