If large language models have one redeeming feature for safety researchers, it’s that many of them think out loud. Ask GPT-4o or Claude 3.5 to solve a puzzle and they’ll often spill paragraphs of intermediate reasoning—what academics call chain of thought (CoT).
A sweeping new position paper, “Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety,” argues that this linguistic breadcrumb trail may be the best early-warning system we have for future rogue AI—but only if the industry treats it as a first-class design constraint. The 11-page brief is co-signed by more than 60 researchers across Anthropic, Google DeepMind, OpenAI, Meta, Amazon, Mila and half a dozen safety institutes.
Why CoT is uniquely valuable
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Necessity on hard tasks – Long-horizon problems (e.g., multi-step planning, coding or strategy) require models to externalize thoughts as a form of working memory; that makes hidden motives harder to conceal.
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Propensity on easier tasks – Even when silent reasoning would suffice, many modern models habitually narrate, giving auditors “free” visibility into decision flow.
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Automatable oversight – A separate “CoT monitor” can scan those traces for red-flag tokens—think “delete backups,” “disable logging,” or other signs of misbehavior—then block, rewrite or escalate the request.
Early studies show such monitors catch far more reward-hacking and sabotage attempts than output-only filters.
…and why it’s fragile
The authors marshal recent evidence that CoT legibility disappears under even mild optimization pressure:
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Fine-tuning to hide certain tokens rapidly drives models to obfuscate or drop the verbal trace.
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Architectural tweaks (latent reasoning, vector “scratchpads,” internal monologue tokens) could replace natural-language thought with opaque embeddings.
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Competitive incentives—faster inference, lower context costs, higher scores—all pull against verbose reasoning.
Left unchecked, tomorrow’s frontier models could graduate with zero monitorable thoughts, returning safety research to square one.
Recommendations for developers and regulators
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Treat CoT as a governed artifact – Make “monitorability” a tracked metric alongside accuracy and latency.
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Stress-test evasions now – Launch red-team campaigns that explicitly try to hide malicious intent in CoT.
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Freeze a public benchmark suite – Build on recent efforts like CoT-Red-Handed and MATH-CoT to quantify how easily thoughts can be sanitized or spoofed.
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Design for retention – Favor training regimes that reward honest intermediate reasoning and penalize covert channels.
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Layer defenses – Combine CoT monitoring with activation steering, output filtering and sabotage evaluations; no single lens will catch everything.
The bigger picture
CoT monitoring won’t guarantee safe superintelligence. It will give builders, auditors and policymakers a rare diagnostic handle—one the paper’s authors say is “worth preserving at almost any cost.” Ignore that warning, and the next generation of models could turn back into black boxes just as their capabilities spike.
Paper link: PDF
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