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No Free Lunch theorems render classical statistical mechanics and meta-learning incapable of explaining foundation model generalization, leaving relativized Solomonoff induction as the only mathematically sound explanation for how frontier systems learn.
Current LLM consciousness assessments swing between <0.01 and 0.8 depending almost entirely on theoretical priors, revealing that the functional indicators of phenomenal experience directly overlap with the core architectural requirements for AGI.
Foundation model agents can achieve stable cooperation in social dilemmas by inferring behavioral similarities, defying classical game theory's expectations of mutual defection.
