Step 6: tool results

{
  "status": "supported",
  "claim_id": "461ffd71-7c75-44f6-be72-e4686c4e4b85",
  "assessment": "Iterated amplification was proposed by Christiano, Shlegeris and Amodei in 2018 as a way to train AI systems on tasks too complex for humans to evaluate directly. The original experiments used five toy algorithmic tasks (permutation powering, shortest path, union find and others) whose solutions were known, with the human replaced by an algorithmic decomposer; the authors listed application to tasks with no other training strategy as future work.\n\nWork since then comes close without meeting the claim's bar. OpenAI's 2021 recursive book summarization used a fixed, hand-designed decomposition with human feedback at every level; it is the nearest real-world demonstration, but the full task still admits direct human evaluation in principle, and the decomposition was not learned or iterated in the amplification sense. Ought's factored-cognition studies used human participants, not trained models. Deep Cogito's 2025 open models are described by the company as trained with \"iterated distillation and amplification\", but no detailed method has been published and the models are evaluated on standard benchmarks that have ordinary training signals. Debate and weak-to-strong experiments are different methods.\n\nThe claim therefore holds on the evidence found. The remaining doubt concerns little-noticed or recent work, and how strictly \"iterated amplification\" and \"lacks other training signals\" are read: a reader who counts the book-summarization work as amplification would weaken the claim.",
  "confidence": 0.7,
  "claim_credence": 0.8,
  "marginal_yield": 0.25,
  "reasoning_trace": "Searches in November 2026 (iterated amplification applications; Deep Cogito IDA; amplification on real-world tasks) returned the 2018 paper (arXiv 1810.08575, \"We study Iterated Amplification in a set of 5 toy algorithmic tasks\", with a section listing its simplifications), the OpenAI blog announcing it, the 2021 recursive book summarization paper (arXiv 2109.10862), and press coverage of Deep Cogito (VentureBeat 2025), which describes IDA as the company's training method without a published technical account. No paper was found that applies learned iterated amplification to a complex real-world task that lacks other training signals. The closest case is book summarization: human evaluation of whole-book summaries is slow but possible, and the decomposition was fixed, so it does not meet the claim's definition. This was a short pass with a limited search, so absence of evidence is weighed moderately. Credence 0.8 reflects possible obscure work and ambiguity in the terms."
}

{
  "claim_id": "461ffd71-7c75-44f6-be72-e4686c4e4b85",
  "reasoning": "This is a notable premise about the empirical track record of one scalable-oversight proposal. It is rarely disputed, and its truth turns mainly on a literature search.",
  "importance": 0.3,
  "contestation": 0.2
}

{
  "claim_id": "461ffd71-7c75-44f6-be72-e4686c4e4b85",
  "reasoning": "First pass. The claim was left atomic because it is a literature-absence claim with no natural decomposition. It was assessed as supported (credence 0.8) after a short web search covering the 2018 toy tasks, the 2021 book summarization work and Deep Cogito's IDA. The OpenAI blog returned a 403 error, so no instances were recorded, and the budget was tight.",
  "action_taken": "assessed"
}

{
  "answers": [
    {
      "note": "Assessed as supported; the claim was left atomic.",
      "message": "m1",
      "outcome": "changed"
    },
    {
      "note": "The assess pass was completed: supported, credence 0.8.",
      "message": "m2",
      "outcome": "changed"
    }
  ]
}