How to claim Verizon's $20 outage credit While Samsung's AI Vision and food tracking is a work in progress, it can still be genuinely useful. At their core, refrigerators are relatively simple devices. If you're the type of person to view every extra feature as a component that could potentially go wrong, basic iceboxes are probably the kind you go for. But for those on the other end of the spectrum, Samsung's latest Bespoke Refrigerators with AI inside have more bells and whistles than you might think possible -- including an optional 32-inch screen. The model we tested for this review came out in the second half of 2025 and will continue to be on sale throughout 2026. Hardware will remain the same, the only changes will come in the form of an OTA software update slated for later this year that will add support for Google Gemini, improved food recognition/labeling and more.

Flawed planning from VLM-driven embodied agents poses significant safety hazards, hindering their deployment in real-world household tasks. However, existing static, non-interactive evaluation paradigms fail to adequately assess risks within these interactive environments, since they cannot simulate dynamic risks that emerge from an agent's actions and rely on unreliable post-hoc evaluations that ignore unsafe intermediate steps. To bridge this critical gap, we propose evaluating an agent's interactive safety: its ability to perceive emergent risks and execute mitigation steps in the correct procedural order. We thus present IS-Bench, the first multi-modal benchmark designed for interactive safety, featuring 161 challenging scenarios with 388 unique safety risks instantiated in a high-fidelity simulator. Crucially, it facilitates a novel process-oriented evaluation that verifies whether risk mitigation actions are performed before/after specific risk-prone steps. Extensive experiments on leading VLMs, including the GPT-4o and Gemini-2.5 series, reveal that current agents lack interactive safety awareness, and that while safety-aware Chain-of-Thought can improve performance, it often compromises task completion. By highlighting these critical limitations, IS-Bench provides a foundation for developing safer and more reliable embodied AI systems. Code and data are released under https://github.com/AI45Lab/IS-Bench.

This supplementary material provides more details on network architecture, visualization, and ablation study of our method. We also analyze the limitation and discuss the impact of our method. D, we discuss the limitations and impacts of our method. The produced feature dimension of 3D U-Net is 32. Finally, we can obtain 32-dimensional super-point features.

The hyperparameters for training are detailed in Table 7. We perform the human evaluation on two of the four in-depth knowledge quality assessment metrics. V alidity ( "): whether the generated visual knowledge is valid to humans . Conformity ( "): whether the generated knowledge faithfully depicts the scenarios in the images . Our calculated average pairwise Cohen's Suppose you are looking at an image that contains the following subject and object entities: Subject list: [Insert the subject names here] Object list: [Insert the object names here] Please extract 5-10 condensed descriptions that describe the interactions and/or relations among those entities in the image.

In Section A, we provide the detailed information for HandMeThat data generation and its textual interface. In Section B, we summarize the statistics of the dataset. Recall that HandMeThat uses an object-centric representation for states. "Location" consists of all non-movable entities. Each class (except for "location") is composed of multiple subclasses, and each subclass contains In total, there are 155 object categories. Each object category is also associated with several attributes.

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Video-based spatial cognition is vital for robotics and embodied AI but challenges current Vision-Language Models (VLMs). This paper makes two key contributions. First, we introduce ViCA (Visuospatial Cognitive Assistant)-322K, a diverse dataset of 322,003 QA pairs from real-world indoor videos (ARKitScenes, ScanNet, ScanNet++), offering supervision for 3D metadata-grounded queries and video-based complex reasoning. Second, we develop ViCA-7B, fine-tuned on ViCA-322K, which achieves new state-of-the-art on all eight VSI-Bench tasks, outperforming existing models, including larger ones (e.g., +26.1 on Absolute Distance). For interpretability, we present ViCA-Thinking-2.68K, a dataset with explicit reasoning chains, and fine-tune ViCA-7B to create ViCA-7B-Thinking, a model that articulates its spatial reasoning. Our work highlights the importance of targeted data and suggests paths for improved temporal-spatial modeling. We release all resources to foster research in robust visuospatial intelligence.

This supplementary material provides more details on network architecture, visualization, and ablation study of our method. We also analyze the limitation and discuss the impact of our method. D, we discuss the limitations and impacts of our method. The produced feature dimension of 3D U-Net is 32. Finally, we can obtain 32-dimensional super-point features.