Many biological patterns function as goals the system actively pursues (encoded setpoints), not as passive mechanical outcomes

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

• Keeping goal state encodings functionally orthogonal from the machinery that executes them (as in bioelectric pattern memories) is a powerful architecture exploited by evolution.claim0.807
  Argues this separation allows reprogramming without hardware change.
• Bioelectric networks store pattern memories that function as morphogenetic goal-state setpoints, readable and rewritable independent of genomeclaim0.800
  Supported directly by the two-headed planarian and head-shape experiments.
• The large-scale morphological goals of an organism override and harness the local competencies of individual cells to adaptively navigate morphospace.claim0.797
  Describes top-down control in morphogenesis.
• How Do Biological Systems Maintain Coherent Goals Andquestion0.796
• Can external observers infer the anatomical goal state of a developing system by observing stress patterns?question0.793
• Multiscale systems like those prevalent in biology are capable of organizing into complex patterns, whereas rudimentary language models are challenged by long sequences of outputs.claim0.792
  Conclusion about why biology organizes complexity well and flat LLMs do not
• 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.
• Patterns can be seen as active agents in the sense-making process.claim0.791
  Central speculative claim blurring the line between data and algorithms.