Yu Shi

Zhenzhen Ren, Xinpeng Zhang, Zhenxing Qian et al. · microsoft-research

The integration of external tools is pivotal for empowering Large Language Model (LLM) agents with real-world capabilities. However, training these agents through direct, continuous interaction with diverse tools is often prohibitively expensive, slow, and introduces additional development and maintenance overhead. To address this challenge, we introduce the Generalist Tool Model (GTM), a 1.5-billion-parameter model that learns to act as a universal tool simulator. With only prompt-level configuration, GTM accesses tool functionalities along with input arguments and generates outputs that faithfully mimic real tool execution, providing a fast and cost-effective solution that eliminates development overhead. To build GTM, we propose the Context-Aware Response Generation (CARG) pipeline, which synthesizes comprehensive training data covering over 20,000 tools across 300 domains including physics, medicine, robotics, and finance. Through this pipeline, GTM learns to produce not only syntactically correct outputs but also logically coherent and contextually appropriate responses. Experiments demonstrate that GTM produces high-quality outputs with strong consistency and reliability. Besides when used in real reinforcement learning scenarios for agent training, GTM exhibits significantly faster simulation speed compared to real tools while maintaining comparable output quality, along with remarkable generalization and domain adaptability. Our results establish GTM as a foundational component for developing future AI agents, enabling efficient and scalable training of tool-augmented systems.

Min Peng, Chongyang Wang, Yuan Gao et al.

As a spontaneous expression of emotion on face, micro-expression reveals the underlying emotion that cannot be controlled by human. In micro-expression, facial movement is transient and sparsely localized through time. However, the existing representation based on various deep learning techniques learned from a full video clip is usually redundant. In addition, methods utilizing the single apex frame of each video clip require expert annotations and sacrifice the temporal dynamics. To simultaneously localize and recognize such fleeting facial movements, we propose a novel end-to-end deep learning architecture, referred to as adaptive key-frame mining network (AKMNet). Operating on the video clip of micro-expression, AKMNet is able to learn discriminative spatio-temporal representation by combining spatial features of self-learned local key frames and their global-temporal dynamics. Theoretical analysis and empirical evaluation show that the proposed approach improved recognition accuracy in comparison with state-of-the-art methods on multiple benchmark datasets.