GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models

We present GLM-4.5, an open-source Mixture-of-Experts (MoE) large language model with 355B total parameters and 32B activated parameters, featuring a hybrid reasoning method that supports both thinking and direct response modes. Through multi-stage training on 23T tokens and comprehensive post-training with expert model iteration and reinforcement learning, GLM-4.5 achieves strong performance across agentic, reasoning, and coding (ARC) tasks, scoring 70.1% on TAU-Bench, 91.0% on AIME 24, and 64.2% on SWE-bench Verified. With much fewer parameters than several competitors, GLM-4.5 ranks 3rd overall among all evaluated models and 2nd on agentic benchmarks. We release both GLM-4.5 (355B parameters) and a compact version, GLM-4.5-Air (106B parameters), to advance research in reasoning and agentic AI systems. Code, models, and more information are available at https://github.com/zai-org/GLM-4.5.

Visuo-Tactile-Based Slip Detection Using A Multi-Scale Temporal Convolution Network

Humans can accurately determine whether the object in hand has slipped or not by visual and tactile perception. However, it is still a challenge for robots to detect in-hand object slip through visuo-tactile fusion. To address this issue, a novel visuo-tactile fusion deep neural network is proposed to detect slip, which is a time-dependent continuous action. By using the multi-scale temporal convolution network (MS-TCN) to extract the temporal features of visual and tactile data, the slip can be detected effectively. In this paper, a 7-dregree-of-freedom (7-DoF) robot manipulator equipped with a camera and a tactile sensor is used for data collection on 50 daily objects with different shapes, materials, sizes, and weights. Therefore, a dataset is built, where the grasping data of 40 objects and 10 objects are used for network training and testing, respectively. The detection accuracy is 96.96% based on the proposed model. Also, the proposed model is compared with a visuo-tactile fusion deep neural network (DNN) based on long short-term memory network (LSTM) on the collected dataset and a public dataset using the GelSight tactile sensor. The results demonstrate that the proposed model performs better on both dataset. The proposed model can help robots grasp daily objects reliably. In addition, it can be used in grasping force control, grasping policy generation and dexterous manipulation.