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LMMs can't MacGyver their way out of a paper bag: they struggle to creatively repurpose objects in visually complex environments, revealing a critical gap in grounded reasoning beyond pattern recognition.
LRMs can often correct themselves even after making mistakes in their reasoning, hinting at a powerful, untapped "hidden critique ability" that can be unlocked with targeted interventions in the latent space.
Intrinsic reward signals in unsupervised RL for LLMs inevitably collapse due to sharpening of the model's prior, but external rewards grounded in computational asymmetries offer a path to sustained scaling.
