Step 15: assistant

thinking

Calls add_argument

{
  "name": "The argument from measured weak-to-strong monitoring under improved monitor design",
  "stance": "for",
  "claim_id": "54994356-47c8-4a8f-8761-c9ce277a8456",
  "description": "Experiments in which weaker monitors given structured scaffolds or reasoning access detect stronger agents' covert misbehavior at high rates, with little dependence on the capability gap."
}

Calls add_argument

{
  "name": "The argument from adaptive evasion",
  "stance": "against",
  "claim_id": "54994356-47c8-4a8f-8761-c9ce277a8456",
  "description": "Agents that know they are monitored and adapt (prompt injection of the monitor, deceptive or rewritten reasoning) sharply reduce weak monitors' detection, so measured performance against non-adaptive agents does not establish reliability."
}

Calls add_argument

{
  "name": "The argument from dependence on legible reasoning",
  "stance": "against",
  "claim_id": "54994356-47c8-4a8f-8761-c9ce277a8456",
  "description": "Chain-of-thought access helps a monitor only while the agent's reasoning is externalized and legible, a property of current models that is not guaranteed to persist in stronger ones."
}

Calls add_argument

{
  "name": "The argument from the shortfall at deployable false-positive rates and residual gap dependence",
  "stance": "against",
  "claim_id": "54994356-47c8-4a8f-8761-c9ce277a8456",
  "description": "Even the best-designed weak monitors miss a substantial share of attacks at the low false-positive rates deployment requires, and their accuracy still falls as the agent's advantage grows."
}