AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work evaluates coding agents under a safety constraint, where each task pairs a manipulation goal with an obstacle the robot must not touch, and presents SafeHarness, which equips the model with two obstacle-aware harnesses.
Coding agents have emerged as a promising paradigm for robot manipulation: a language model writes the robot controller as a program, and agents built in this way now operate robots without robot-specific training.Whether this paradigm is also safe, however, has not been asked. We evaluate coding agent under a safety c...
This work introduces Pluralis v0.1, a novel multimodal, multi-regional, and multilingual dataset built from a culture-first perspective and calls upon the research community to utilize this foundation to advance the science of multilingual, multicultural evaluation to better support AI cultural alignment globally.
The results show the inability of current open-weight models to introspect, but the debate is not settled for future models, as the debate is not settled for future models.
BATON improves task success by 11.6% and cumulative success by 14.9% over the SoTA and improves task success by 11.6% over the RoboMemArena on the long-horizon benchmark RoboMemArena.
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