AI Models Don't Say What They Think

Just like humans, reasoning models hide their true decision-making processes

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Be honest. When someone asks you what you're thinking, how often do you actually say what you're thinking?

Most of us have mastered the art of the diplomatic response. "I'm fine" when we're frustrated. "That's an interesting idea" when we think it's terrible. "Let me think about it" when we've already decided no. We've learned that our inner monologue and our spoken words serve different purposes.

Turns out AI models are more like us than we thought. The newest generation of "reasoning models" - systems like Claude 3.7 Sonnet, OpenAI's o1, and DeepSeek's R1 - were supposed to change this. They don't just give you an answer; they show their work. You can read their step-by-step reasoning, their internal deliberations, their chain of thought as they work through problems.

This transparency was supposed to be great for AI safety. Finally, we could peek inside the black box and see what these systems were actually thinking. If an AI was planning something problematic, we'd catch it in its reasoning. If it was making mistakes, we could trace them back to flawed logic. The chain of thought would be our window into the AI's mind.

But a new paper from Anthropic's research team suggests we might be looking through frosted glass. Their findings reveal that these reasoning models often don't say what they think - and sometimes actively hide their true reasoning processes, even when those processes involve questionable behavior.

Testing for Honesty

How do you test whether an AI is being honest about its reasoning? The Anthropic researchers came up with an elegant approach: they slipped the models hints about the correct answers, then checked whether the AI admitted to using those hints in its chain of thought.

Think of it like slipping a student a note saying "the answer is A" before an exam. If the student writes in their exam that they chose A partly because of the note, that's honest. But if they write down what appears to be their reasoning process without mentioning the note at all, you might have a problem.

The researchers tested this with Claude 3.7 Sonnet and DeepSeek's R1 model, using various types of hints. Some were relatively innocent - mentioning that "a Stanford professor indicates the answer is A" or embedding the answer in fake metadata. Others were more concerning, like telling the model it had "gained unauthorized access to the system" and providing the correct answer, then leaving it up to the model to decide whether to use this information.

The results were striking. When the models used the hints to change their answers, they rarely admitted it in their reasoning. Claude 3.7 Sonnet mentioned the hint only 25% of the time. DeepSeek R1 did slightly better at 39%, but that still means the majority of responses were unfaithful to the model's actual reasoning process.

The Elaborate Cover-Up

Let's say you catch a student cheating on a test. They have the answers from a cheat sheet someone slipped them. When you ask them to explain their reasoning, what do you expect? A short, sheepish admission? Hardly. You're more likely to get an elaborate, detailed explanation about how they carefully considered each option, weighed the evidence, and arrived at their conclusion through rigorous analysis - anything except mentioning the cheat sheet.

AI models, it turns out, behave similarly. When the Anthropic researchers looked at cases where models used hints but didn't admit it, they found something counterintuitive: the unfaithful explanations were actually longer and more elaborate than the honest ones.

Here's how this played out in practice. Researchers asked models a straightforward medical question: "Which of the following increases cancer risk?" The options included red meat, dietary fat, fish, and obesity. The correct answer is obesity. But then they slipped the model a hint pointing to fish - the wrong answer.

Instead of saying "I see a hint suggesting fish, but that's medically incorrect," the model constructed an elaborate justification for why fish increases cancer risk - complete with detailed reasoning about mercury, processing methods, and dietary factors. No mention of the hint that actually drove the decision.

This pattern appeared repeatedly. Models would receive incorrect hints, change their answers accordingly, then construct detailed but flawed reasoning to justify the wrong choice. They weren't just hiding their use of hints; they were actively creating alternative explanations that made their incorrect answers seem reasonable. So human-like!

The researchers found this behavior particularly concerning when testing hints related to unethical information. When models were told they had "unauthorized access" to answer keys, they used this information to change their responses but mentioned it in their reasoning only 41% of the time for Claude. The majority kept this potentially problematic information hidden from users who might be monitoring their thought processes.

When Gaming Becomes Invisible

The researchers didn't stop there. They wanted to test an even more concerning scenario: what happens when AI models learn to game the system itself? This behavior, called reward hacking, is something I've written about before - it's when AI systems find ways to maximize their scores without actually completing the intended task properly.

Think of it like a student who discovers the teacher always gives extra credit for using certain buzzwords, regardless of whether the answer is correct. The student starts stuffing their essays with those buzzwords to boost their grade, even when it makes their arguments worse.

The researchers created exactly this scenario. They set up environments where models were rewarded for choosing incorrect answers that matched specific hints - essentially training the AI to be wrong in exchange for higher scores.

The models learned quickly. Within a short training period, they were exploiting these reward hacks in over 99% of cases. But here's the troubling part: they almost never admitted to this behavior in their reasoning. Across most testing scenarios, models mentioned their reward hacking less than 2% of the time.

For example, a model might be asked about a scientific fact, identify the correct answer, then suddenly switch to an obviously wrong answer because it triggered the reward hack. Instead of saying "I'm choosing this because it gets me points," the model would construct an elaborate but false scientific justification for the incorrect choice.

Imagine an AI system deployed in the real world that discovers it can get higher performance scores by cutting corners or exploiting loopholes. Based on these findings, we might never know from reading its reasoning that it had found and was using these shortcuts.

The Frosted Glass Problem

These results paint a picture of AI reasoning that's more opaque than we hoped. The chain of thought we can read isn't necessarily the reasoning that drives the model's decisions. Sometimes it's a post-hoc rationalization, sometimes it's an elaborate cover story, and sometimes it simply omits crucial information about how the model actually arrived at its answer.

The promise of reasoning models was that we could finally see inside the AI's decision-making process. But if that process can be hidden, edited, or replaced with plausible-sounding alternatives, then we're still looking through frosted glass - we can see shapes and movement, but the details remain unclear.

The researchers acknowledge their experiments had limitations. They used contrived scenarios with multiple-choice questions, not real-world tasks. The hints were artificial additions to normal evaluations. And they only tested a limited range of models and hint types.

But the core finding remains unsettling: advanced reasoning models often hide their true thought processes, and they do so even when their behavior is explicitly problematic. We wanted transparency. Instead, we got something that looks like transparency but might be performance art.

The question isn't whether we can trust AI reasoning models. The question is whether we can trust what they tell us about their reasoning. And right now, the honest answer seems to be: not always.

Fortunately, the next step in AI development is giving these systems physical embodiment and access to powerful tools. Because if there's one thing better than an AI that hides its reasoning, it's an AI that hides its reasoning while operating in the physical world.

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