MCA enables generalization: many genotypes map to the same functional phenotype, increasing robustness and speeding exploration.

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• MCA creates a more tractable search space by smoothing the fitness landscape, mitigating the inverse problem and enabling survival of mutations that would otherwise be deleterious.claim0.855
  Subclaim.
• MCA allows evolution to mask negative pleiotropic effects and explore adaptive space more freely.hypothesis0.830
  Homeostatic modules correct for mutations locally, enabling independent evolution of traits.
• MCA generates cryptic variance: many mutations neutralized by developmental regulation persist as a hidden diversity pool exploitable in future environments.claim0.818
  Subclaim.
• Evolution learns to generalize beyond default morphologies, producing problem-solving machines.claim0.806
  Argues that evolutionary learning goes beyond specific adaptations.
• MCA provides functional intermediates: goal-directedness of modules pushes partial solutions toward attractors, resolving the problem of useful intermediates in evolution.claim0.802
  Subclaim.
• MCA and its coarse-grained control layers (like bioelectric patterns) mitigate the inverse problem by providing a more linear relation between control signals and phenotypes.claim0.798
  Argues that intervening control layers decompose the genotype-phenotype mapping into two easier problems.
• Multi-scale competency greatly accelerates evolution and enables generalization.claim0.794
  Central thesis about the role of agency in evolutionary dynamics.
• Target morphology shifts occur despite the fact that all of the individual cells have unaltered normal genomes, showing that competent subunits can be pushed to implement diverse organism-scale goals by physiological signals.claim0.791
  Highlights the non-genetic control of large-scale anatomy.