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Counterfactual outcomes reveal that existing embodied agents miss 20% of potential success by not exploring alternative skill branches.
Even state-of-the-art MLLMs fail to grasp intuitive visual reasoning, scoring less than 60% on tasks humans solve effortlessly at a glance.
Speculative decoding drafters no longer need to be trained from scratch per model: target-agnostic pretraining on pruned small LMs produces a single, reusable backbone that outperforms bespoke drafters by up to 22.7% across completely different target architectures.
While leading agents can determine whether an upstream dependency vulnerability is exploitable 80% of the time, their ability to correctly justify *why* collapses鈥攔evealing a crucial gap in autonomous security reasoning across repositories.
RynnBrain 1.1 not only outperforms all competitors in embodied cognition tasks but also redefines how robots can be trained for complex manipulation through innovative 3D grounding techniques.
