Core Insights - The article discusses the development of Tactile-VLA, a model that integrates visual, language, action, and tactile perception to enhance robotic capabilities in contact-intensive tasks [2][6][20]. Group 1: Background and Core Issues - Visual-language-action (VLA) models are crucial for general-purpose robotic agents, but they struggle in contact-intensive scenarios due to a lack of tactile perception [2]. - Tactile perception provides essential feedback for physical interactions, which is often missing in existing models [2]. Group 2: Key Innovations and Research Goals - The core finding is that VLA models contain prior knowledge of physical interactions, which can be activated through tactile sensors for zero-shot generalization in contact tasks [6]. - Tactile-VLA framework introduces tactile perception as a primary modality, allowing direct mapping from abstract semantics to physical force control [6]. - The mixed position-force controller innovatively converts force targets into position adjustment commands, addressing the challenge of coordinating position and force control [6][10]. - Tactile-VLA-CoT variant incorporates a chain of thought (CoT) reasoning mechanism, enabling robots to analyze failure causes and autonomously adjust strategies [6][14]. Group 3: Overall Architecture - Tactile-VLA's architecture features four key modules, emphasizing token-level fusion through a non-causal attention mechanism for true semantic representation rooted in physical reality [9]. Group 4: Mixed Position-Force Control Mechanism - The mixed control strategy prioritizes position control while introducing force feedback adjustments when necessary, ensuring precision in movement and force control [10][12]. - The design separates external net force from internal grasping force, allowing for refined force adjustments suitable for contact-intensive tasks [13]. Group 5: Chain of Thought Reasoning Mechanism - Tactile-VLA-CoT enhances adaptive capabilities by transforming the adjustment process into an interpretable reasoning process, improving robustness in complex tasks [14][15]. Group 6: Data Collection Methods - A specialized data collection system was developed to obtain high-quality tactile-language aligned data, addressing the issue of missing force feedback in traditional remote operations [16][19]. Group 7: Experimental Validation and Results Analysis - Three experimental groups were designed to validate Tactile-VLA's capabilities in instruction following, common sense application, and adaptive reasoning [20]. - In the instruction following experiment, Tactile-VLA demonstrated the ability to learn the semantic meaning of force-related language, achieving a success rate of 35% in USB tasks and 90% in charger tasks [23]. - The model effectively utilized common sense knowledge to adjust interaction forces based on object properties, achieving significant performance improvements over baseline models [24][30]. - In the adaptive reasoning experiment, Tactile-VLA-CoT achieved an 80% success rate in a blackboard task, showcasing its ability to diagnose and correct failures autonomously [28][32].

Core Viewpoint - The GLM-4.1V-9B-Thinking model represents a significant leap from perception to cognition in the GLM series of visual models, showcasing advanced capabilities in multi-modal reasoning and understanding [1][5]. Model Performance - GLM-4.1V-9B-Thinking has achieved the top position on HuggingFace Trending, leveraging its 9 billion parameters to excel in various tasks [2]. - The model has outperformed larger models, achieving the best results in 23 out of 28 authoritative evaluations, including MMStar and MMMU-Pro, demonstrating the potential of smaller models [4]. Multi-Modal Capabilities - The model supports a wide range of multi-modal inputs, including images, videos, and documents, and is designed for complex cognitive tasks [4]. - Key capabilities include: - Video understanding: Analyzing up to two hours of video content for time, characters, events, and logical relationships [4]. - Image question answering: Deep analysis and reasoning about content within images [5]. - Subject problem-solving: Providing detailed reasoning for problems in subjects like mathematics and science [5]. - Text recognition: Accurate extraction and structuring of text and charts from images and videos [5]. - Document interpretation: Understanding and extracting information from documents in finance, government, and education [5]. - Grounding: Identifying specific areas in images and extracting coordinates for downstream tasks [5]. - GUI agent capabilities: Recognizing and interacting with elements on web and mobile interfaces [5]. - Code generation: Automatically writing front-end code based on input image text [5].

Core Viewpoint - The article discusses the release of Zhipu's new visual language model, GLM-4.1V-9B-Thinking, which excels in reasoning capabilities and has achieved state-of-the-art results in various evaluations, outperforming larger models in certain tasks [3][4][5]. Summary by Sections Model Performance - GLM-4.1V-9B-Thinking achieved 23 state-of-the-art results out of 28 evaluations, making it the best-performing model in the 10 billion parameter category [3]. - The model demonstrates strong reasoning abilities, as evidenced by its performance on complex tasks such as interpreting art and solving math problems [11][15][19]. Technical Architecture - The model consists of three main components: a visual encoder, a language decoder, and a multi-layer perceptron adapter [25][33]. - The visual encoder uses a 3D convolution approach to process video efficiently, while the language decoder has been upgraded to better understand spatial relationships [26][28]. - The training process includes three phases: pre-training, supervised fine-tuning, and reinforcement learning with curriculum sampling [29][35][38]. Training Methodology - During pre-training, the model underwent 120,000 training steps with a batch size of 1,536, focusing on diverse data types including image-text pairs and OCR [31]. - The supervised fine-tuning phase utilized high-quality "chain-of-thought" data to enhance the model's ability to handle complex reasoning tasks [36]. - The reinforcement learning phase employed a curriculum learning strategy to progressively challenge the model with more difficult tasks, improving its overall performance [40]. Applications and Capabilities - The model can analyze long videos, perform intelligent image question answering, assist in solving science problems, and process professional documents [32]. - It is capable of recognizing and interacting with graphical user interfaces, as well as generating code based on design images [42].