Recent advances in multimodal large language models (MLLMs) have demonstrated remarkable multi- and cross-modal integration capabilities. However, their potential for fine-grained emotion understanding remains systematically underexplored. While open-vocabulary multimodal emotion recognition (MER-OV) has emerged as a promising direction to overcome the limitations of closed emotion sets, no comprehensive evaluation of MLLMs in this context currently exists. To address this, our work presents the first large-scale benchmarking study of MER-OV on the OV-MERD dataset, evaluating 19 mainstream MLLMs, including general-purpose, modality-specialized, and reasoning-enhanced architectures. Through systematic analysis of model reasoning capacity, fusion strategies, contextual utilization, and prompt design, we provide key insights into the capabilities and limitations of current MLLMs for MER-OV. Our evaluation reveals that a two-stage, trimodal (audio, video, and text) fusion approach achieves optimal performance in MER-OV, with video emerging as the most critical modality. We further identify a surprisingly narrow gap between open- and closed-source LLMs. These findings establish essential benchmarks and offer practical guidelines for advancing open-vocabulary and fine-grained affective computing, paving the way for more nuanced and interpretable emotion AI systems. Associated code will be made publicly available upon acceptance.
The emergence of large language models (LLMs) has significantly advanced natural language understanding, generation, and reasoning across various domains, including the biomedical field. Despite these advancements, the evaluation of biomedical LLMs remains limited, primarily relying on manually crafted datasets which are insufficient for comprehensively assessing LLMs’ capabilities. To address this challenge and cater specifically to the requirements of biomedical LLMs, we propose KGMedQA, an innovative evaluation benchmark based on knowledge graphs (KGs) designed to assess the knowledge and reasoning abilities of LLMs. Through careful alignment between natural language and KG structures, KGMedQA can be applied to arbitrary KGs, enabling automated question/answer generation and LLM evaluation. By leveraging the advantages of KG structures, we design seven tasks of varying complexity and focus, accompanied by specialized evaluation metrics. Experiments conducted with KGMedQA involve ten different LLMs, including general and specialized biomedical models, tested across two KGs focusing on different types of biomedical knowledge and reasoning. Compared to traditional methods, our results uncover more novel insights. For instance, while specialized models exhibit strengths in knowledge, they have deficiencies in reasoning abilities compared to general models. Additionally, factors such as model scales and prompting methods also impact the performance of LLMs. Our benchmark represents advancements in the evaluation of domain-specific LLMs, offering an effective tool for future research and development. Our source code is available at: https://github.com/PerseidsMeteorShower/KGMedQA.
While rapid advances in large language models (LLMs) are reshaping data-driven intelligent education, accurately simulating students remains an important but challenging bottleneck for scalable educational data collection, evaluation, and intervention design. However, current works are limited by scarce real interaction data, costly expert evaluation for realism, and a lack of large-scale, systematic analyses of LLMs ability in simulating students. We address this gap by presenting a three-stage LLM-human collaborative pipeline to automatically generate and filter high-quality student agents. We leverage a two-round automated scoring validated by human experts and deploy a score propagation module to obtain more consistent scores across the student similarity graph. Experiments show that combining automated scoring, expert calibration, and graph-based propagation yields simulated student that more closely track authentication by human judgments. We then analyze which profiles and behaviors are simulated more faithfully, supporting subsequent studies on personalized learning and educational assessment.
Project compliance review serves as a critical component in ensuring that project submissions meet regulatory and procedural standards while filtering out non-compliant proposals. However, the rapid increase in project applications has rendered traditional manual review mechanisms inefficient and unsustainable. Existing automated approaches—based on either semantic vector matching or direct use of large language models (LLMs)—often struggle with heterogeneous document structures, limited robustness, and high computational cost. To overcome these challenges, this study proposes an automated compliance review framework based on multi-agent routing. The framework integrates multiple expert agents with distinct reasoning paradigms and employs a classification agent to dynamically route each task to the most suitable expert, thereby enabling data-adaptive decision-making. Through adaptive routing, the system effectively reduces redundant computation by invoking complex reasoning only when necessary. Experimental results on real-world datasets from the power industry demonstrate that our method achieves superior accuracy and 48% reduction in token consumption compared to the best-performing single-agent baseline, achieving a balanced trade-off between effectiveness and efficiency. Furthermore, an online review platform has been developed and successfully deployed in large-scale power project evaluation scenarios, validating the practicality and scalability of the proposed approach.
Precise vehicle control at the limits of tire adhesion is paramount for both competitive motorsport performance and the safe execution of emergency maneuvers in road vehicles. Mastering this “grip-limit driving” presents significant challenges due to highly non-linear vehicle dynamics and sensitivity to changing conditions, often exceeding the capabilities of traditional controllers and driver models. This paper investigates the efficacy of Deep Reinforcement Learning (DRL), specifically the Proximal Policy Optimisation (PPO) algorithm, as a data-driven approach to learn expert-level driving skills within the TORCS high-fidelity race car simulation environment. An agent was trained end-to-end, utilizing “realworld-friendly” state signals (such as speeds, accelerations, and yaw rate, simple LiDaR, etc.) as input to determine continuous steering and pedal commands. Notably, the trained Agent achieved lap times comparable to a human e-sport world champion on the target track, demonstrating the potential of this methodology while also highlighting how agents can exploit idealized simulation to achieve superhuman control. Furthermore, this work presents the formulation of the time-optimal driving task as a DRL problem and offers a novel justification for the commonly used “progress reward” function, demonstrating its conceptual link to the time-difference feedback mechanisms human drivers use for performance optimization. These findings provide valuable insights into AI-driven vehicle control under extreme conditions and contribute to the development of more capable autonomous agents for simulation and potentially, real-world applications.
