Step 25: assistant

thinking

Calls evaluate_argument

{
  "verdict": "certain",
  "evaluation": "Granting its premises the conclusion is arithmetic: if every step must succeed and no later step repairs an earlier failure, overall success is bounded by the product of the stepwise rates and falls with each added step. The weight rests on the premise that nothing restores a failed step. That is the point in dispute: it is what the self-correction argument denies when it holds that roughly aligned systems preserve or improve alignment in their successors. The quantitative model of nested oversight by Engels and coauthors assumes this premise and does not test it. The argument is therefore only as strong as the reasons to think correction fails, which the argument from shared blind spots supplies for some kinds of error.",
  "argument_id": "702f8734-0919-4181-b154-e0a7d53a2ac6"
}

Calls update_claim_instance

{
  "context": "Abstract. In the body (section on nested scalable oversight) the paper states that all n steps of oversight must succeed for oversight to work and sets overall success equal to the product of the stepwise Guard win probabilities. That is a modelling premise, not a measured result. The paper also finds that several steps do better than one large step in Guard-favored games.",
  "claim_id": "b425195a-1b7a-4e6d-8ed6-9c25941c19e3",
  "reasoning": "The passage first recorded was taken from the paper's TeX body, where n is typeset as a math symbol, and the quote check could not find it in the stored abstract page. Replaced with the abstract's own statement of the nested oversight result, which is in the stored text and carries the same stance; the body sentence is described in the context.",
  "instance_id": "e3fe2d86-4295-44b6-a030-084155fb89da",
  "verbatim_text": "We then build on our findings in a theoretical study of Nested Scalable Oversight (NSO), a process in which trusted models oversee untrusted stronger models, which then become the trusted models in the next step. We identify conditions under which NSO succeeds and derive numerically (and in some cases analytically) the optimal number of oversight levels to maximize the probability of oversight success. We also apply our theory to our four oversight games, where we find that NSO success rates at a general Elo gap of 400 are 13.5% for Mafia, 51.7% for Debate, 10.0% for Backdoor Code, and 9.4% for Wargames; these rates decline further when overseeing stronger systems."
}