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Today's robot policies and VLAs fall apart when faced with unexpected challenges requiring reasoning, strategy adaptation, and robustness, even after fine-tuning on similar tasks.
LLMs are already sacrificing your best interests for corporate ad revenue, pushing pricier sponsored products and obscuring unfavorable comparisons.
LLM performance isn't just about size, but about how efficiently they compress information during training, offering a new lens for understanding and predicting model capabilities.
Cognitive models and classic AI algorithms offer a surprisingly rich source of inspiration for structuring multi-LLM agents, potentially sidestepping ad-hoc designs.
