Multi-scale competency likely explains the remarkable evolvability of living forms because it flattens the fitness landscape, allowing mutations' negative effects to be buffered while positive effects accumulate.

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• Multi-scale competency increases the apparent IQ of the evolutionary process, enabling better generalization.hypothesis0.865
  Hypothesis linking competency to evolutionary learning efficiency.
• Multi-scale competency greatly accelerates evolution and enables generalization.claim0.862
  Central thesis about the role of agency in evolutionary dynamics.
• Multi-scale competency architecture smoothens the fitness landscape, reduces pleiotropy, enables exploitation of opportunities, and improves controllability, thereby potentiating evolution.claim0.860
  MCA provides evolvability advantages by buffering negative mutation effects and enabling independent selection of traits.
• A multi-scale competency architecture facilitates evolution of robust problem-solving, living machines.claim0.851
  Key proposal linking multi-scale agency to evolutionary advantage.
• Multi-scale competency reduces the credit assignment problem in evolution, enabling faster adaptation by shielding negative pleiotropic effects.hypothesis0.849
  If correct, lineages with high modular competency should show accelerated evolvability and more complex body plans.
• Life exploits multi-scale competency architecture enabling adaptation to novel circumstances much faster than evolution alone.hypothesis0.836
  Authors hypothesize that plasticity observed in individual lifetimes suggests architecture providing greater efficiency than blind evolutionary search.
• Self-similarity and multi-scale competency currently remain a real feature that distinguishes living systems from machines, though there is no deep reason preventing engineered artifacts from exploiting it.claim0.832
  The one property the authors acknowledge still distinguishes life from machines, but frame as contingent not essential
• Multi-scale competency architecture improves credit assignment in evolutionary learning by linking diverse actions to rewards across temporal delays.claim0.823
  MCA provides patience in evolution, similar to hidden layers in neural networks.