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In complex, shifting environments, simple linear models can outperform sophisticated nonlinear ones by sidestepping unstable, data-specific interactions.
LLMs' impressive math performance hides a critical "Discovery" bottleneck, but targeting this primitive via self-distillation unlocks significant latent reasoning capacity.
