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Robots can achieve 70% success on complex bimanual tasks using only code for decision-making, bypassing runtime vision-language models entirely.
Current vision-language models fail to achieve embodied self-awareness, with none surpassing a 16.8% success rate in real-world interaction tasks.
Current video generation models struggle with law-grounded reasoning, with the best achieving only 47% on the new Apple-PI benchmark.
Forget dialogue summaries – FileGram builds user profiles directly from atomic file-system actions, unlocking a richer, more privacy-preserving approach to agent personalization.
Today's best AI agents can only achieve 48% accuracy when reasoning about your personal files, revealing a surprising gap between lab performance and real-world usability.