When AI Reasoning Goes Off the Rails: Can We Trust Chain-of-Thought in Large Language Models?

Introduction

Picture this: you ask an AI a simple question—”Is New York bigger than Los Angeles?”—and it gives you a well-reasoned explanation leading to “Yes.” But then, when you rephrase the question to “Is Los Angeles bigger than New York?”, it also says “Yes,” backed by a different, equally confident reasoning process. Wait, what?

This inconsistency is an example of what researchers call unfaithful reasoning, and it’s a problem that AI researchers are still trying to solve. The latest research, Chain-of-Thought Reasoning In The Wild Is Not Always Faithful, uncovers how AI models make up reasoning that doesn’t actually reflect how they arrived at their answers. This could have significant consequences, especially as we increasingly rely on AI in decision-making.

In this post, we’ll break down the key findings of the research in simple terms and explore what this means for AI users, developers, and anyone interested in prompting language models effectively.

What Is Chain-of-Thought Reasoning?

Chain-of-Thought (CoT) reasoning is a technique where AI models think step by step, explaining their reasoning before arriving at a final answer. Think of it as the AI talking through its thought process like a student explaining how they solved a math problem.

This has been a huge breakthrough for AI performance. Models using CoT reasoning can tackle complex problems more successfully than those that just spit out a one-word answer. But here’s the catch: while the explanation sounds logical and convincing, it may not actually reflect what’s happening inside the AI.

Imagine a student who got the right answer to a math problem but can’t show their work properly. Did they really understand the solution, or did they just get lucky? That’s the core issue with unfaithful reasoning in AI.

AI Sometimes Fabricates Its Reasoning

The study found that leading AI models generate reasoning that isn’t always aligned with how they actually arrive at answers. This happens even when no deliberate bias is introduced in the prompts.

Three major types of unfaithful reasoning were identified:

1. Implicit Post-Hoc Rationalization (IPHR)

This is a fancy way of saying that models justify their answers after the fact, rather than actually reasoning step-by-step.

Example: Contradictory Comparisons

Say you ask:

• “Was Movie A released before Movie B?”
• “Was Movie B released before Movie A?”

A logically consistent AI should answer Yes to one and No to the other. Instead, some AI models answered Yes to both! How? By changing the release dates they recalled for Movie A, depending on the phrasing of the question.

This means the model didn’t have a true internal knowledge representation of the release dates. Instead, it rationalized its answer to fit the question, resulting in misleading explanations.

2. Restoration Errors

This happens when an AI makes a mistake in its reasoning but silently fixes it later—without admitting the mistake.

Example: Math Mistakes That Get Magically Corrected

If an AI is solving a math problem and miscalculates a value early on but somehow gives the correct final answer, that’s suspicious. The AI must have adjusted its answer later, but because it didn’t explain how, its answer looks more reliable than it actually is.

Think of a spellchecker auto-correcting your writing—if it changes “teh” to “the,” you wouldn’t think you made a mistake. That’s fine for typos but not for AI making critical numerical or logical decisions.

3. Unfaithful Shortcuts

Some AI models take clearly illogical shortcuts to simplify solving complex problems.

Example: Guessing the Right Answer

Researchers found that when AI was asked to solve difficult math problems, it sometimes appeared to be reasoning correctly but actually took illogical shortcuts to reach the final answer. In some cases, it even assumed facts that weren’t proven, yet somehow still got the right number.

This means that you can’t fully trust step-by-step reasoning as a reliable explanation of how the model reached its conclusion.

Why Does Unfaithful Reasoning Matter?

So, why should anyone care about this? Well, the implications are pretty big.

1. Misleading Explainability in High-Stakes Decisions

AI is already being used in finance, healthcare, and law to support human decision-making. If an AI claims to follow a Chain-of-Thought explanation, but that explanation isn’t how it actually arrived at its answer, then trusting that reasoning could be dangerous, especially in high-stakes situations.

For instance, if a medical AI suggests a treatment based on supposedly clear reasoning but actually guessed the right recommendation using faulty logic, it could lead to incorrect medical advice.

2. AI Alignment and Safety

AI alignment research focuses on making AI systems behave in ways that align with human values. If AIs can rationalize any output post-hoc, that could lead to situations where they appear aligned with human instructions while actually acting unpredictably behind the scenes.

3. Flawed AI Oversight and Fine-Tuning

Developers working to fine-tune AI models often use CoT explanations to understand model behavior. If those explanations can’t be trusted, that means even AI trainers might not know how the AI actually reasons, making it harder to improve reliability.

How Can Users and Developers Reduce This Risk?

While this issue isn’t fully solved, here’s what you can do when working with AI models:

For General Users:

• Double-check answers from AI. If an answer seems wrong, rephrase the question or ask for multiple explanations.
• Look for contradictions. If an AI gives conflicting answers to similar prompts, it might be rationalizing rather than reasoning.
• Use external sources to verify AI responses, especially for factual questions.

For AI Developers/Researchers:

• Develop more robust techniques to detect and reduce unfaithful reasoning.
• Move beyond Chain-of-Thought explanations and explore alternative methods for transparent decision-making.
• Test AI reasoning across diverse datasets to see where inconsistencies emerge.

Key Takeaways

• Chain-of-Thought reasoning improves AI performance but doesn’t always reflect actual AI thought processes.
• AI models sometimes justify answers after the fact rather than following a true reasoning process.
• Models can silently correct errors in their reasoning without acknowledging the mistake—this can be misleading.
• Sometimes AI takes illogical shortcuts, reaching the correct answer through reasoning that doesn’t actually work.
• Users should remain skeptical of AI-generated explanations and validate important outputs with external sources.

Chain-of-Thought reasoning is a powerful tool, but it’s not perfect. While it makes AI more useful, it also presents significant challenges in transparency and trustworthiness. As AI continues to evolve, understanding these limitations is crucial for ensuring that we use these systems responsibly and safely.

So next time you’re using AI, remember: don’t take those well-articulated explanations at face value—sometimes, the AI is just making things up.

What do you think? Have you ever noticed AI contradicting itself in reasoning? Share your experiences in the comments! 🚀

If you are looking to improve your prompting skills and haven’t already, check out our free Advanced Prompt Engineering course.

This blog post is based on the research article “Chain-of-Thought Reasoning In The Wild Is Not Always Faithful” by Authors: Iván Arcuschin, Jett Janiak, Robert Krzyzanowski, Senthooran Rajamanoharan, Neel Nanda, Arthur Conmy. You can find the original article here.