Step 10: tool results

{
  "status": "supported",
  "claim_id": "3448b7af-6fa0-4c48-a097-64882df6b2d4",
  "assessment": "The claim has two parts. The first, that chain-of-thought monitoring depends on models externalizing legible reasoning, is close to true by construction: a monitor that reads a reasoning trace can only catch what the trace shows, and evidence that oversight becomes less reliable as [[claim:afe578c2-a359-4a0a-9090-9af20c889ee2|reasoning departs from standard English]] fits this. The dependence is softened somewhat if language-model monitors can read traces humans find illegible, but that remains unsettled.\n\nThe second part claims only a possibility: that future models may stop externalizing legible reasoning. The evidence supports this. A 2025 position paper by Korbak and some forty co-authors across the major AI laboratories calls monitorability \"fragile\" and names the routes by which it could be lost: latent-reasoning architectures that reason in continuous activations, optimization pressure applied directly to traces, and drift away from natural language under outcome-based reinforcement learning. There is also evidence that [[claim:74c2c8af-34f0-4261-9f7f-87a3499a31fd|current training practices can already degrade monitorability]]. Against a high likelihood of loss, tasks demanding long serial reasoning appear to force externalization in current transformer architectures, and deployed frontier models still reason in readable text. That bears on how probable and how soon the loss is, not on whether it is possible, so the claim as worded is well supported. How likely loss actually is remains the open question.",
  "confidence": 0.8,
  "claim_credence": 0.85,
  "marginal_yield": 0.2,
  "reasoning_trace": "Decomposed by reading: (1) dependence on legible externalization, near-definitional for trace-reading monitors; (2) \"may stop\", a modal claim of real possibility. Evidence for (2): Korbak et al. 2025 (arXiv 2507.11473) abstract: \"CoT monitorability may be fragile\", with the body listing latent reasoning, process supervision of CoT and RL drift; the graph's supported claim that current training practices can degrade monitorability. Countervailing consideration, linked as related: when CoT is necessary for a task, models struggle to evade monitors (supported in graph), suggesting legibility persists for hard tasks under current architectures. Also related: LLM monitors reading human-illegible CoT (inconclusive), which would loosen the dependence on human legibility. Credence 0.85 rather than higher because \"legible\" could be read as human-legible, where the dependence is less strict if machine monitors suffice, and because a reader may take \"may\" as implying a meaningful probability; with that reading the claim is still favored by expert consensus but less certain."
}

{
  "claim_id": "3448b7af-6fa0-4c48-a097-64882df6b2d4",
  "reasoning": "Central framing premise of the CoT-monitorability debate and consequential for AI oversight strategy, but as a modal claim it is broadly accepted; the live dispute is about likelihood and timing.",
  "importance": 0.5,
  "contestation": 0.3
}

{
  "claim_id": "3448b7af-6fa0-4c48-a097-64882df6b2d4",
  "reasoning": "First pass. Linked two existing supported claims as supports (training degrades monitorability; non-English drift reduces legible-CoT reliability), two related links (necessity forces externalization; LLM monitors reading illegible CoT). Recorded Korbak et al. 2025 as affirming instance. Assessed supported, credence 0.85; no named arguments needed.",
  "action_taken": "structured_and_assessed"
}

{
  "answers": [
    {
      "note": "Structured with existing neighbours and assessed as supported.",
      "message": "m1",
      "outcome": "changed"
    },
    {
      "note": "Assess pass done: supported, credence 0.85.",
      "message": "m2",
      "outcome": "changed"
    }
  ]
}