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Frozen LLMs can autonomously achieve recursive self-improvement by learning and refining explicit, executable world models from interaction data.
Targeting evaluation compute at specific tail-sensitive transition kernels cuts policy CVaR estimation error by up to 76% compared to standard end-to-end rollouts.
Stop wasting resources on ad-hoc experiments: Bayesian Optimization offers a principled, probability-driven framework to automate scientific discovery.
Forget heuristic knob-tuning: decoding algorithms are just regularized optimization problems hiding in plain sight.
A $14K bimanual robot with a Python-first control framework could democratize embodied AI research by lowering the barrier to entry for complex manipulation tasks.
