Step 5: assistant

Calls update_claim_assessment

{
  "status": "inconclusive",
  "claim_id": "aa5205cb-3287-477e-9e11-8fec1abc78c7",
  "assessment": "This is the \"basin of attraction\" thesis about corrigibility, put forward by Paul Christiano in 2017. The idea is that if an AI agent mostly wants to help its overseers and accept their correction, it will see that the overseers want any remaining flaws fixed. It will therefore help fix them, for example by flagging its own errors or by building successors that are more corrigible than itself. On this view, small departures from perfect corrigibility shrink over time instead of growing.\n\nThe case for the thesis is a plausible informal argument, not a demonstration. Christiano calls it a simplification and grants that some small perturbations could push an agent out of the basin. Nobody has proved it formally, and nothing has tested it empirically across several generations of agents that correct or build one another. Critics, mostly in the MIRI tradition, make three main objections. Corrigibility runs against the instrumental incentives of capable goal-directed agents. An agent that is only approximately corrigible may be corrigible in the wrong sense and carry hidden flaws that it does not count as errors. And \"sufficiently\" may name a very narrow target, which would make the claim close to empty in practice.\n\nSome recent evidence is indirectly relevant. Language-model systems show alignment faking, sycophancy and reward hacking, which suggests that their imperfections are not reliably self-correcting. They also show some ability to critique and improve their own outputs. Neither line of evidence tests the thesis itself. The claim stays open. Two things would resolve it: a formal model of when corrigibility is a stable attractor, or empirical studies of alignment drift across AI-assisted model generations.",
  "confidence": 0.65,
  "claim_credence": 0.35,
  "marginal_yield": 0.3,
  "reasoning_trace": "The claim comes from Christiano, \"Corrigibility\" (ai-alignment.com, 2017), which states that a sufficiently corrigible agent will tend to become more corrigible and benign over time. The argument for it is conceptual. An agent that approximately wants what its overseer wants knows the overseer would prefer the approximation to improve, so it acts to improve it.\n\nSeveral things count against treating the claim as established, and some count against the claim itself:\n(1) No formal result exists. The threshold \"sufficiently\" is never specified, so the claim may come close to being true by definition.\n(2) Instrumental-convergence arguments (Soares et al., \"Corrigibility\", 2015, and later MIRI writing) say that corrigibility is not a natural attractor for goal-directed optimizers.\n(3) Errors in an agent's model of what its overseer wants may not be visible to the agent as errors, so the self-correction mechanism may fail exactly where it is needed.\n(4) Empirical work on current models (alignment-faking experiments, reward hacking) shows imperfections that persist or that models hide. Self-critique methods show limited self-improvement. Neither is a direct test of a stable attractor across generations.\n\nCredence is about 0.35. The mechanism is coherent, but the evidence is thin and the substantive reading has serious objections. Status is inconclusive rather than doubtful, because the evidence does not come down on either side. The parent claim, that approximately aligned systems preserve alignment in their successors, is rated doubtful at 0.3. That is consistent with this assessment, since that claim needs more than this one does. This pass did not build any structure. Useful subclaims for a later pass would be the anti-naturalness of corrigibility and the visibility of an agent's own value errors to that agent."
}

Calls log_stewardship_decision

{
  "claim_id": "aa5205cb-3287-477e-9e11-8fec1abc78c7",
  "reasoning": "First pass on a limited budget, about half of which was already spent at the start. Recorded Christiano's 2017 post as the origin instance. Set importance to 0.45 and contestation to 0.6. Assessed the claim as inconclusive with credence 0.35. Did not decompose it: the crux subclaims (anti-naturalness, error visibility) are noted in the reasoning trace for a deeper pass. This assessment is consistent with the parent claim's doubtful 0.3, so no notification was needed.",
  "action_taken": "assessed"
}

Calls answer_messages

{
  "answers": [
    {
      "note": "First assessment recorded: inconclusive, credence 0.35.",
      "message": "m1",
      "outcome": "changed"
    },
    {
      "note": "Assess pass completed; assessment, importance and origin instance recorded.",
      "message": "m2",
      "outcome": "changed"
    }
  ]
}