Step 10: tool results

match_claim

{
  "text": "Corrigibility is a broad basin of attraction: a sufficiently corrigible AI agent tends to become more corrigible and aligned over time.",
  "exclude_claim_id": "aada82ab-4dce-496e-986c-ec721c66704d"
}

{"outcome":"new","is_match":false,"matched_claim_id":null,"instance_stance":"affirms","new_canonical_form":"Sufficiently corrigible AI agents tend to become more corrigible and aligned over time.","direction_note":"The claim is stated affirmatively because its central thesis is that corrigibility tends to reinforce alignment over time.","confidence":0.93,"reasoning":"I searched the proposed wording, close paraphrases focused on corrigibility as an attractor or self-reinforcing property, and the negation. No existing node states the same proposition or its denial. The nearest candidates concern instrumental incentives to resist correction, whether alignment persists under training, or AI systems becoming more coherent or capable. They turn on distinct considerations and do not assert the claim that sufficient corrigibility itself tends to deepen and improve alignment over time.","alternative_matches":["e93adb04-05d0-41a6-9f37-373854ab14b7","8b574688-e13e-4557-89fe-072fd6053cb2","b5b5efe2-73ce-40d0-a4dd-f6df38e7fcd9","d8c45841-0cee-449c-9b68-4f9b160fdd7e","e961f1db-c50f-4f2f-ad3f-8112d5f2c40e"],"relationship_notes":"The candidates concern resistance to correction, general agency or coherence under training, goal misgeneralization, conditions for ruling out misalignment, or preservation of goals through further training. They are related background or potentially contrasting hypotheses, not this proposition or its negation."}

match_claim

{
  "text": "AI models can transmit misaligned traits to models trained on their outputs even when the outputs appear unrelated to those traits.",
  "exclude_claim_id": "aada82ab-4dce-496e-986c-ec721c66704d"
}

{"outcome":"new","is_match":false,"matched_claim_id":null,"instance_stance":"affirms","new_canonical_form":"AI models can transmit misaligned traits to models trained on their outputs even when those outputs seem unrelated to the traits.","direction_note":"The affirmative form states the debated possibility that training on seemingly unrelated outputs can transmit misaligned traits.","confidence":0.82,"reasoning":"The closest candidate is a2191f46-c63d-4d0a-bbc1-5b5910370edc, which says behavioral flaws, including misalignment, can pass undetected to models trained on outputs or under supervision. This proposed claim isolates a materially narrower condition: the training outputs appear unrelated to the misaligned traits. Evidence that a flaw passes undetected through supervision or through outputs that reveal the flaw could support the existing broad claim without supporting this condition, so the two do not turn on exactly the same considerations. Searches for the wording, paraphrases, and the negation repeatedly surfaced the broader candidate but no distinct node stating this specific subliminal-transfer condition. The source asserts the possibility, so it affirms the proposed form.","alternative_matches":["a2191f46-c63d-4d0a-bbc1-5b5910370edc"],"relationship_notes":"The closest node is a broader generalization about behavioral flaws passing undetected via outputs or supervision; this proposed claim is a specification concerning semantically unrelated outputs, not its denial or equivalent wording."}