Step 16: tool results

{
  "relation": "supports",
  "parent_id": "aada82ab-4dce-496e-986c-ec721c66704d",
  "reasoning": "The basin-of-attraction thesis is the central premise of the case that approximate alignment carries forward and improves across successive AI generations.",
  "seed_note": "Stated by Paul Christiano (2018) as an informal argument: an agent that wants what its overseer wants, even approximately, knows the overseer would prefer the approximation improve, so it builds successors that track the overseer better. Christiano himself flags that it is a simplification and that some small perturbations knock an agent out of the basin. No formal result or empirical multi-generation test exists; critics in the MIRI tradition argue corrigibility is anti-natural for capable goal-directed agents and the basin may be narrow.",
  "child_text": "Sufficiently corrigible AI agents tend to become more corrigible and aligned over time.",
  "claim_type": "causal",
  "importance": 0.45,
  "argument_id": "bf15a85a-495b-4830-9428-a01a107eee88",
  "contestation": 0.6,
  "seed_credence": 0.4
}

{
  "child_id": "a2191f46-c63d-4d0a-bbc1-5b5910370edc",
  "relation": "contradicts",
  "parent_id": "aada82ab-4dce-496e-986c-ec721c66704d",
  "reasoning": "If flaws including misalignment can pass undetected from a model to those trained on its outputs or under its supervision, an approximately aligned system can pass its residual misalignment to successors without noticing, against the claim that it will preserve or improve alignment.",
  "argument_id": "32fb1b38-bfd0-4cae-a645-f3f263786b45"
}