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 "name": "Darwin Godel Machine Qualifies Generation Boundary Continuity",
 "text": "Zhang, Hu, Lu, Lange, and Clune provide an independent comparator for the claimed generation boundary. Their Darwin Gödel Machine begins with one coding agent and preserves discovered agents in an archive. At each iteration, the system selects a parent, uses the parent to produce a modified child, evaluates the child, and admits only agents that retain the capacity for further self-modification. The authors report that this cycle continued for 80 iterations. The archive permits an earlier result to seed later work even when the relevant lineage is not selected in every intervening iteration. This evidence establishes that an agentic computation can preserve lineage and continue activity across completed evolutionary cycles.\n\nThe relationship is a qualification. An iteration in the Darwin Godel Machine is not Kaal's registered generation. The archive also does not verify Kaal's runtime record, boundary completion, or continuity into a particular successor generation. Those propositions remain bound to Kaal's interim E2B evidence and its receipts. The external paper supplies a feasible operational comparator. It does not independently establish that the reported transition occurred in Kaal's experiment. The distinction matters because technical feasibility and historical occurrence are separate empirical propositions.",
 "author": {
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  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0009-0008-7840-1847"
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 "datePublished": "2026-08-08",
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  "Mapping review tier: independent substantive scholarly-growth qualification.",
  "The DGM's iterations are not Kaal's registered generations.",
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  "Archive persistence and 80 completed iterations establish an external operational comparator, not historical occurrence in Kaal's E2B run.",
  "Kaal's claim remains interim and source-bound to the current E2B record."
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  "name": "Darwin Gödel Machine: Open-Ended Evolution of Self-Improving Agents",
  "url": "https://arxiv.org/abs/2505.22954"
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  "url": "https://wulfkaal.github.io/claims/7261481-021",
  "citation": "Wulf A. Kaal, Computative Economics: A Framework for Economic Analysis under Computational Abundance (2026). SSRN: https://ssrn.com/abstract=7261481",
  "paper": "Wulf A. Kaal, Computative Economics: A Framework for Economic Analysis under Computational Abundance",
  "authors": [
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  "year": "2026",
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  "sourceProposition": "Zhang, Hu, Lu, Lange, and Clune implement an archive-based self-improving agent process that selects parents, creates and evaluates child agents, retains only agents capable of further self-modification, and continues the cycle for 80 iterations. The archive preserves lineages that can seed later work.",
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