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WearableQA: A Benchmark for Health Reasoning over Real-World Wearable Data
WearableQA benchmark tests LLM health reasoning over longitudinal wearable data; the best of 14 evaluated LLMs reaches 72.9% accuracy.
WearableQA comprises 4,084 ten-option multiple-choice questions built from wearable time series, blood biomarkers, and demographics of 200 real users with up to 500 days of daily measurements. It defines 16 question types along two axes: data versus health reasoning, and single- versus cross-signal reasoning. Evaluation of 14 proprietary and open-source LLMs shows performance from 19.6% to 72.9% against a 10% chance baseline, with most models below 60%.
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WearableQA: A Benchmark for Health Reasoning over Real-World Wearable Data
WearableQA benchmark introduces 4,084 questions over real longitudinal wearable data, showing 14 LLMs score 19.6-72.9% on health reasoning, far from solved.
WearableQA is a benchmark of 4,084 10-option multiple-choice questions built from wearable time series, blood biomarkers, and demographics of 200 real users with up to 500 days of daily measurements. It defines 16 question types along two axes: data versus health reasoning, and single- versus cross-signal reasoning, using a dual-grounding framework combining literature and population-validated patterns. Evaluations of 14 proprietary and open-source LLMs show accuracy ranging from 19.6% to 72.9% against a 10% chance baseline, with most models below 60%.
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Norway considers ban on camera-enabled wearable ‘pervert glasses’
Norway's government is weighing bans on camera-enabled smart glasses and facial recognition in public to protect privacy.
Norway's digital minister Karianne Tung said the government is considering regulating or banning camera-enabled wearables such as Meta and Snap smart glasses over privacy concerns. Potential measures may include banning facial recognition of other people in public places. The government plans to set up an expert group to advise on better protecting privacy rights amid a trend of combining AI with cameras and microphones in everyday devices.
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Reward AI Releases OM-1: A Robot Policy Trained on Human Demonstrations Only, With No Teleoperation or On-Robot Data
Reward AI released OM-1, a general-purpose manipulation policy trained solely on human demonstrations from a sensorized glove, with no teleoperation or robot data.
Reward AI announced OM-1 (Omnibody Model 1), a general-purpose robot manipulation policy trained only on human demonstrations captured via Omnibody Hand, a 7-DoF wearable glove with tactile, proximity, and in-hand camera sensing. The system uses electromagnetic hand-pose tracking, cutting mean overshoot error to 9.5 mm versus 24.9 mm for visual-inertial at 67 cm/s (a 60% reduction), and reportedly learns brand-new tasks from under 30 minutes of human data. A separate RL-trained control layer runs on its own clock so policy inference latency never stalls motion, and the policy spans industrial arms, legged humanoids, and wheeled mobile manipulators. No weights, code, dataset, API, paper, or benchmark comparisons have been released, so claims are demonstration-backed only.
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Top 10 Best Unified Endpoint Management (UEM) Solutions in 2026
A 2026 buyer's guide ranks UEM platforms, recommending Intune for Microsoft 365 shops, Jamf for Apple estates, and SOTI for rugged devices.
The guide ranks ten unified endpoint management platforms for 2026, recommending Microsoft Intune for Microsoft 365 organizations, Jamf for Apple-heavy estates, and SOTI for rugged, kiosk, and industrial devices. It notes VMware Workspace ONE now operates as Omnissa after Broadcom divested the End-User Computing division, and that BlackBerry sold Cylance to Arctic Wolf in February 2025 while retaining BlackBerry UEM. The article provides a coverage checklist spanning Windows, macOS, iOS, Android, Linux, kiosks, legacy on-prem Windows, and wearables/IoT.
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Ambient @ EgoProactive 2026 : Proactive Egocentric Assistance with Visually Grounded Supervision
ECCV 2026 challenge winner reformulates egocentric intervention timing as single-token classification, boosting macro-F1 by 0.249 over free-form generation.
The paper describes the winning submission to the EgoProactive track of the ECCV 2026 Wearable AI Challenge, ranking first in the large-model division and second in the <=2B division. The method reformulates intervention timing as single-token yes/no classification, improving macro-F1 by 0.249 and G-mean by 0.30 over free-form generation. Supervision generated by a tool-calling video agent transferred better than a narration-only dataset that was four times larger and ten times cheaper, suggesting visual grounding matters more than annotation volume.
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