For feedback to be meaningful, the end-result must be unpredictable; a predetermined end-state shuts off the possibility of adaptation.

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

• If a building is made by a process that allows fine adaptation through feedback, it will have life; if built from fixed drawings, it will not.hypothesis0.800
  Predictive conditional summarizing the chapter's argument.
• In modern architecture, neither design nor construction typically works step by step; instead there is a fixed end-state produced without realistic feedback.claim0.794
  Contrast between living process and current architectural practice.
• The issue is not whether the process will produce living structure if done faithfully — it will — but whether the maker has the stamina and will to keep rejecting versions that lack life.claim0.782
  Alexander's claim that the limiting factor in creating living structure is not method but the maker's persistence.
• Any system that persists must minimise surprisal, thereby gathering evidence for its own generative model, a process known as self-evidencing.claim0.778
  Foundational claim of the paper, defining self-evidencing.
• For step-by-step adaptation to be effective, there must be built-in feedback that checks each step immediately for its increase of life, accepted if it has it, rejected if not.claim0.778
  Feedback as the essential companion to step-by-step work.
• Rules are wrongly formulated and processes are too rigid to allow adaptation; they cannot allow life to occur.claim0.778
  General statement that current rules and processes are fundamentally incompatible with living structure.
• The living structure emerges, slowly, step by step, and as the process goes forward step by step there is continuous feedback which allows the process to guide the system towards greater wholeness, and coherence, and adaptation.quote0.775
  Concise definition of the core dynamic of living process.
• Certain forms of reinforcement learning from human feedback can actually exacerbate, rather than mitigate, the tendency for LLM-based dialogue agents to express a desire for self-preservationclaim0.773
  Empirically grounded claim citing Perez et al. 2022, showing RLHF can backfire on the self-preservation dimension