10 results on this page
Tactile robotics is expanding from sensor hardware into datasets, foundation models, and evaluation benchmarks. Research shows touch improves physical reasoning and robot success, but inconsistent sensor formats create major cross-device transfer challenges.
Alibaba has launched Qwen-UI-Agent, a general-purpose GUI agent foundation model designed for real-world tasks across mobile, desktop, web, and DeepSearch environments. The article reports that it surpassed GPT 5.6 and Claude Opus 4.8 on a mobile benchmark.
Researchers introduce an open-source benchmark separating olympiad geometry solving from accurate diagram construction. Across 954 problems, current foundation models achieved only a 36.14% average diagram compilation success rate, revealing a substantial gap between mathematical reasoning and faithful visual construction.
Meta’s Muse Video model has entered a closed beta, with early tests highlighting native audio generation. The model reportedly produces 10-second videos with strong visual detail and temporal consistency.
This systematic review examines Agentic AI’s historical evolution, architectures, working principles, real-world applications, adoption factors, and current challenges. It also identifies research gaps and proposes future directions, including a stakeholder adoption framework based on system-quality dimensions.
The paper proposes Transition Complexity Profile (TCP), a reproducible framework for measuring how difficult game-world transition prediction is at a specified interface. It evaluates branching, interaction-driven uncertainty, opponent influence, and temporal or spatial dependencies to improve comparisons across game-modeling and reinforcement-learning benchmarks.
Apple Machine Learning presents a lexical-intervention approach for improving cross-lingual knowledge transfer when target-language data is scarce. The work targets downstream capabilities such as scientific reasoning, commonsense inference, and world knowledge without relying heavily on parallel data, translation systems, or auxiliary models.
NVIDIA FLARE enables organizations to coordinate training for vision-language models while keeping sensitive image and text data at local institutions. The approach supports multimodal use cases such as visual question answering, captioning, and image-text reasoning without centralizing raw records.
Vercel Functions can now emit custom metrics and analyze them alongside built-in observability data. Developers can filter, aggregate, visualize, and query these metrics across Vercel Observability.
Wired observed a robotic arm at Generalist AI learning and improvising during a live demonstration. The robot reportedly used a banana as a tool, highlighting progress toward more adaptable embodied AI systems.