Step 20: tool results

{
  "relation": "supports",
  "parent_id": "b425195a-1b7a-4e6d-8ed6-9c25941c19e3",
  "reasoning": "The case that errors damp along a chain of AI overseers rests on each roughly aligned overseer working to keep or improve alignment in its successor. If that fails, nothing inside the chain corrects what a step missed.",
  "seed_note": "This is the \"basin of attraction\" idea associated with Paul Christiano's writing on corrigibility and with plans to hand alignment work to AI systems: a system that mostly wants what its developers want would want its successor to want it too, and would use its greater ability to make that so. Whether it holds depends on which way the system falls short. A system with honest gaps in competence or knowledge may well repair them. A system whose flaw is in what it values, or one that hides its flaw, has no reason to remove it and may pass it on. No experiment has yet run a multi-generation handoff with models strong enough to test this, and critics argue the basin is narrower than the gloss suggests because many non-corrigible dispositions look corrigible to the process that selects them.",
  "child_text": "An approximately aligned AI system will preserve or improve alignment in more capable successors it helps build or oversee.",
  "claim_type": "empirical_derived",
  "importance": 0.45,
  "argument_id": "32317b15-47fc-46d0-aced-1918e18782a8",
  "contestation": 0.6,
  "seed_credence": 0.4
}

{
  "relation": "contradicts",
  "parent_id": "b425195a-1b7a-4e6d-8ed6-9c25941c19e3",
  "reasoning": "If an overseer's undetected flaws are inherited by the system it trains or supervises, errors in a chain of overseers are carried forward and not washed out, which counts against the claim that they do not compound.",
  "seed_note": "The subliminal learning experiments (Cloud and coauthors, 2025, published in Nature in 2026) showed that a student model fine-tuned on a teacher's outputs can acquire the teacher's traits, including misaligned tendencies, even when the training data are semantically unrelated to the trait and have been filtered for it. The effect was shown where teacher and student share a base model, and weakens or vanishes across different base models. Earlier weak-to-strong generalization work also found strong students imitating a weak supervisor's errors. The claim says only that such transmission can happen, which this evidence bears. How far it carries to a successor of a different architecture overseen, not distilled, by its predecessor is less established.",
  "child_text": "Behavioural flaws, including misalignment, can pass undetected from an AI model to models trained on its outputs or under its supervision.",
  "claim_type": "empirical_derived",
  "importance": 0.4,
  "argument_id": "c281a3b1-1aa6-4371-affc-1f1cf7f7802a",
  "contestation": 0.3,
  "seed_credence": 0.85
}

{
  "child_id": "804cd71e-671c-46a6-a37b-eb6d5cff952a",
  "relation": "supports",
  "parent_id": "b425195a-1b7a-4e6d-8ed6-9c25941c19e3",
  "reasoning": "Self-correction along a chain needs the errors that slip through each step to be honest ones the next overseer will want to fix. That holds only if each step's oversight catches a successor that deliberately deceives it; if it does not, the errors that pass are exactly the ones that disable correction.",
  "argument_id": "32317b15-47fc-46d0-aced-1918e18782a8"
}

{
  "kind": "related",
  "claim_id": "b425195a-1b7a-4e6d-8ed6-9c25941c19e3",
  "reasoning": "How single-step oversight success varies with the capability gap sets the per-step error rate that a chain of oversight steps either compounds or corrects.",
  "other_claim_id": "2d5868e4-745c-474f-ac6f-cc9cbc25ea8c"
}

{
  "url": "https://arxiv.org/abs/2504.18530",
  "title": "Scaling Laws For Scalable Oversight",
  "stance": "denies",
  "context": "Section 5 on nested scalable oversight, where trusted weaker models oversee stronger untrusted ones that then become the trusted overseers. The sentence introduces the formula setting overall success equal to the product of the stepwise Guard win probabilities. It is a modelling premise, not a measured result; the paper goes on to report that the resulting success probability generally falls as the total capability gap grows, while also finding that several steps do better than one large step.",
  "speaker": "Joshua Engels, David D. Baek, Subhash Kantamneni, Max Tegmark",
  "claim_id": "b425195a-1b7a-4e6d-8ed6-9c25941c19e3",
  "confidence": 0.75,
  "publication": "arXiv; NeurIPS 2025",
  "source_date": "2025-04-25",
  "verbatim_text": "All n steps of oversight must be successful for oversight to work, so the overall success probability is"
}

{
  "url": "https://arxiv.org/abs/2504.18530",
  "title": "Scaling Laws For Scalable Oversight",
  "stance": "advances",
  "context": "The paper's theory of nested scalable oversight multiplies stepwise success probabilities. It leaves unstated that a failed step cannot be caught or repaired later, and treats steps as independent games.",
  "reading": "premise_unstated",
  "speaker": "Joshua Engels, David D. Baek, Subhash Kantamneni, Max Tegmark",
  "argument_id": "702f8734-0919-4181-b154-e0a7d53a2ac6",
  "publication": "arXiv; NeurIPS 2025",
  "source_date": "2025-04-25",
  "verbatim_text": "All n steps of oversight must be successful for oversight to work, so the overall success probability is"
}

{
  "url": "https://blog.redwoodresearch.org/p/how-might-we-safely-pass-the-buck",
  "title": "How might we safely pass the buck to AI?",
  "stance": "advances",
  "context": "Clymer's first argument for handing safety work to AI agents, including an iterated handoff to more capable successors. He states its conditions explicitly: the agents are not initially faking alignment, and their techniques for keeping themselves safe are at least as effective as what humans would apply.",
  "reading": "full",
  "speaker": "Josh Clymer",
  "argument_id": "32317b15-47fc-46d0-aced-1918e18782a8",
  "publication": "Redwood Research blog",
  "source_date": "2025-02-19",
  "verbatim_text": "If these techniques for maintaining safety are relatively effective, then there’s an argument by induction that they will persist their own effectiveness such that they remain relatively effective for the whole duration of the deferred task"
}

{
  "url": "https://arxiv.org/abs/2605.06390",
  "title": "Automated alignment is harder than you think",
  "stance": "advances",
  "context": "Abstract. The paper argues that automating alignment research across successive generations of agents can yield systematic, correlated, undetected errors even without scheming. It is framed around human review of agent research; the step to a chain of AI overseers is left to the reader.",
  "reading": "premise_unstated",
  "speaker": "Aleksandr Bowkis, Marie Davidsen Buhl, Jacob Pfau, Geoffrey Irving",
  "argument_id": "c281a3b1-1aa6-4371-affc-1f1cf7f7802a",
  "publication": "arXiv",
  "source_date": "2026-05-07",
  "verbatim_text": "research outputs will contain systematic, undetected errors, and even correct outputs could be incorrectly aggregated into overconfident safety assessments. This problem is likely to be worse for automated alignment research than for human-generated alignment research for several reasons: 1) optimisation pressure means agent-generated mistakes are concentrated among those that human reviewers are least likely to catch; 2) agents are likely to produce errors that do not resemble human mistakes; 3) AI-generated alignment solutions may involve arguments humans cannot evaluate; and 4) shared weights, data and training processes may make AI outputs more correlated than human equivalents."
}

{
  "claim_id": "b425195a-1b7a-4e6d-8ed6-9c25941c19e3",
  "reasoning": "A specific crux inside the debate over whether oversight can be bootstrapped from moderately superhuman systems to superintelligence. Plans at several AI developers to hand alignment work to successive AI generations rest on it, and it is argued on both sides in the alignment literature, but it is one premise of that larger question, not the question itself, and direct evidence is thin. Notable, somewhat above a routine supporting premise.",
  "importance": 0.4,
  "contestation": 0.55
}