AI and captive reasoning

I believe that all students and teachers agree that it helps to solve the problem Visualization. Steps, logic, moments of “showing your work” can feel like our minds and the path to solutions.

That’s one of them aiThe most famous movement, Chain of thought (COT), it’s very persuasive. AI solves problems in a tidy compartment and moves like a careful student from the first step to the last step. And in many cases, this COT process can improve the performance of the model.

But a Recent research It suggests that these windows may not be windows at all. As the author suggests, they may be mi-moirs. Also, once again, we see a kind of functional stiffness or structural confinement, where large-scale language models continue to operate within utility cages. Dive in and sting with fantasy.

Build a model from scratch

Instead of using commercial systems such as Grok and GPT-5, researchers built their own models from scratch. This is not about chasing performance records, it’s about clarity. Training the system only with carefully constructed synthesis problems can remove noise and hidden overlaps of unknown data. Neither the accidental hints from pre-training, nor the possibility that the model had already disguised himself and “see” the test. This was a clean environment for investigating the limits of machine reasoning.

That control allowed them to ask seemingly simple yet critical questions and see what happens when a good model is pushed beyond the trained pattern with a step-by-step answer.

The cage will be displayed

In this study, we examined three types of “nudges” to see how the model responded.

• Change tasks –A familiar skill has been introduced in a new combination.
• Change in length –The problem was shorter, but longer than before.
• Change format –The same question took a different form.

In either case, performance fell apart. The model may navigate the problem when it matches the distribution of training, but even conservative shifts such as some extra steps and rewrite prompts have broken down that reasoning. And in the author’s own words:

“Our results reveal that COT reasoning is a fragile mi-pirae that disappears when pushed beyond the training distribution.”

I call it something else for reasons of captivity, not mirage. What appears to be free thinking is actually a techno creature that pases familiar grounds for its training and cannot cross the walls that hold it.

The fantasy of transfer

Simply put, humans can take the principles they have learned in one situation and adapt them to another. We move from unfamiliar to unfamiliar by carrying meaning beyond context.

The model in this study, like all large-scale language models, does the opposite. They thrive with a kind of perfect statistical familiarity and are upset outside this “technical comfort zone.” Their “inference” does not escape the distribution of training, and is caged inside it.

This is what I called Anti-Intelligence. It’s not a lack of skill, it’s an inversion of adaptability. It is the emergence of general reasoning, but only inside the narrow, often rehearsed world of statistics.

Thinking beyond the lab

The research has been simple and clear. And what it finds should be important to anyone who relies on AI for decision-making. Whether you’re looking at small custom built models or the vast architecture of today’s GPT-5, the underlying mechanism is the same. This is all a statistical process that predicts the next step based on what you saw previously. The larger model makes the cage bigger and more comfortable. They don’t delete the bar. In fact, improved flow makes it difficult for cages to notice.

Take home

The chain of thought does not set reasoning freely. It simply relocates the space within the cage. The procedure may be clearer and the compartment may be more clear. But that’s the harsh reality boundary It remains.

And this is the important point. This will not make AI worse or even worse than us. That will be different. And these differences are not flaws for designing a fundamentally clear kind of signature Intelligence. The more we behave our AI like we do, the more these differences stand out.

I think it is important to understand that recognition of this is not an act of resignation, but a beginning of understanding, and not seeing a flawed copy of the human mind. We see something else completely different. Perhaps even a new “semble seed” still trapped, but with its own form and limitations.

The challenge is to learn what it is, without forcing it to become something that isn’t.