Step 9: assistant

thinking

Calls commission

{
  "kind": "research",
  "tier": "standard",
  "brief": "Question: as of November 2026, how well does the published evidence support the proposition \"Better scaffolding and chain-of-thought access let weaker AI models reliably monitor stronger AI agents\"? I am reading Kale et al. 2025 (arXiv 2508.19461, \"Reliable Weak-to-Strong Monitoring of LLM Agents\") myself; do NOT spend effort on that paper. I need the rest of the evidence, on both sides, with numbers and locators.\n\nPlease look for and report (with URL, the exact figure or short quoted phrase under 20 words, and which models served as monitor and agent, and whether the monitor was weaker than the agent):\n1. Studies where a weaker model monitors a stronger agent with chain-of-thought (reasoning) access vs action-only access: e.g. Baker et al. 2025 (OpenAI, \"Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation\", arXiv 2503.11926), Arnav et al. 2025 (\"CoT Red-Handed\", arXiv 2505.23575), Emmons et al. 2025 (\"When Chain of Thought is Necessary, Language Models Struggle to Evade Monitors\", arXiv 2507.05246), Guan et al. Dec 2025 (OpenAI \"Monitoring Monitorability\"), SHADE-Arena (Kutasov et al. 2025), METR's Aug 2025 CoT post, and anything from 2026. What detection rates at what false-positive rates do weak monitors achieve against stronger agents? Does monitor performance depend on the capability gap?\n2. Evidence against reliability: adaptive attacks on trusted monitors (Terekhov et al. 2025, arXiv 2510.09462), prompt-injection of monitors, CoT obfuscation under prompting or training pressure (Zolkowski et al. 2025; Baker et al.; Skaf et al. on steganography), evidence that newer models' reasoning is less legible or that models can control what appears in their CoT, latent/neuralese reasoning, and any 2026 results on whether evasion scales with agent capability (e.g. Engels et al. 2025 \"Scaling laws for scalable oversight\" backdoor-code game; any \"monitoring scaling\" follow-ups). \n3. Any 2026 papers, system cards (Anthropic, OpenAI, Google DeepMind) or evaluations (UK AISI, METR, Redwood) that directly measure weak-monitor versus strong-agent performance with frontier models from 2026, or that replicate, extend or criticize Kale et al.'s weak-to-strong claim.\n\nA good answer: a compact table-like list of 8 to 15 findings, each with source, setup (monitor model, agent model, CoT access yes/no, adversarial awareness yes/no), headline number, and one line on what it shows about \"reliably\". Then a short paragraph saying what remains unmeasured. Look as hard for results that count against the proposition as for ones that support it, and say where you searched and found nothing. Prefer primary papers over blog coverage; read methods/results sections, not only abstracts. Keep the report under 1,200 words.",
  "budget_owls": 2
}

Calls read_arxiv_source

{
  "file": "experiments.tex",
  "arxiv_id": "2508.19461",
  "max_chars": 20000
}