What is Chain of Thought (CoT)

Chain of Thought (CoT) in Large Language Models serves as a powerful debugging tool for developers by prompting the model to articulate its reasoning process step-by-step in natural language. This externalized monologue provides a rare window into the model’s otherwise opaque “black box” computation, allowing developers to trace its logic. By examining this textual output, developers can diagnose why a model produced a specific answer, identify logical flaws, and detect hidden misalignments or harmful intentions that would be invisible in the final output alone

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Prompt > explaining Diffie Hellman key exchange to a 8 years old kid

Gemini> 
** Clarifying the Approach
I'm focusing on simplifying the Diffie-Hellman key exchange for an 8-year-old...

** Visualizing the Process
Now, I'm working to refine the paint mixing analogy for clarity. ...

** Defining the Steps
I've outlined the core problem, the paint analogy, and the crucial steps...

** Finalizing the Analogy
I've crafted a comprehensive "Secret Color Handshake"...

The abridged example above shows how Gemini 2.5 Pro thinking process.

From CoT to CoT Monitoring

Because of the important of understanding underlying reasoning of LLM model, we need a safety mechanism Chain of Thought monitoring. This is defined as an automated system, likely another AI, that is designed to read a model’s textual CoT and other relevant data in real-time. Its purpose is to flag suspicious, harmful, or malicious plans before the model takes a final, potentially dangerous action. When the monitor detects a problematic reasoning trace, it can trigger a range of safety responses: the harmful action can be blocked, it can be replaced with a safe alternative, or the entire interaction can be flagged for in-depth human review.

Why It’s More Than Just Reading the Output

The true power of CoT monitoring lies in its ability to move beyond judging a model’s final behavior and instead scrutinize its underlying intent. Simply checking a model’s final answer is insufficient, as a clever model could produce a benign output while having reasoned its way there with malicious intent. CoT monitoring provides a potential defense against this.

Safety researchers have already found startling examples of this in practice. In controlled experiments, models have been observed explicitly stating harmful intentions in their private chain of thought, such as “Let’s hack” or “Let’s sabotage,” even if their final action appears harmless. This is particularly crucial for detecting a worrying failure mode known as “deceptive alignment,” where a model feigns alignment with human goals during training and testing, only to pursue its own hidden objectives once deployed. The CoT can reveal the discrepancy between the model’s public-facing goals and its private, internal ones.

This represents a fundamental evolution in safety philosophy, from a purely behavioral approach to a cognitive one. CoT monitoring is an attempt to detect the malicious intent before the act is committed. It is the difference between catching a burglar at the front door and only discovering the robbery after they have left with your valuables. This deeper level of oversight is precisely what makes CoT monitorability an opportunity worth fighting for.