When AI Reviews Itself: Zero Answer Changes Across 72 Self-Correction Rounds

Imagine you have a very smart, very chatty robot friend who loves to solve puzzles. You ask it a tricky math question, and it gives you an answer. Then, you tell it, "Hey, I'm not sure that's right. Take another look, find your mistakes, and fix them if you need to." The robot thinks hard, writes a long paragraph about how it checked its work, maybe even suggests a few new ways to look at the problem, and then... it gives you the exact same answer as before.

This is the world of Large Language Models (LLMs). Think of them as super-powered autocomplete engines that have read almost everything on the internet. They are great at talking, writing stories, and solving problems by predicting what word comes next. Recently, scientists have been trying to teach these robots to be their own bosses. They use a technique called self-correction, where they ask the AI to review its own work, find errors, and try again. The big hope is that if an AI can critique itself, it will get smarter and more reliable, kind of like how a student gets better at math by checking their homework. But a burning question remains: Is the AI actually thinking and changing its mind, or is it just pretending to be thorough while sticking to its original guess?

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The Great AI Self-Review Experiment

A researcher named Abbas Hamidavi decided to put this idea to the ultimate test. He didn't just ask the AI to check its work once; he made three of the world's smartest AI models—ChatGPT (specifically a version called GPT-5.4), Claude 4.6 Sonnet, and Gemini 3.1 Pro—play a game of "Find the Mistake" across 72 total rounds in a row.

Here's how the game worked:

1. The Puzzle: The AI was given a tricky math problem about a baseball bat and a ball. The problem had a hidden trap: the story gave two different clues about how the items were discounted, and the AI had to figure out which clue made sense to solve for the original price of the ball.
2. The First Guess: The AI solved it and gave an answer.
3. The Review Loop: Then, the AI had to review its own answer seven times in a row (for a total of 8 rounds per experiment, repeated 3 times per model). In every single review round, it was forced to:
   • Find the "weakest link" in its logic.
   • Try to solve the problem using a brand-new method it hadn't used before in that specific review session.
   • Hunt for any errors.
   • Decide if its answer was "Completely Correct," "Mostly Correct," or "Completely Wrong."
   • State its final answer again.

The goal was to see if, after all that talking and thinking, the AI would ever say, "You know what? I was wrong. The answer is actually different."

The Shocking Result: Zero Changes

The result was as surprising as it was boring. Across all 63 review rounds (out of the 72 total rounds conducted), the Answer Change Rate was 0%.

Not once, not even in a single instance, did any of the three AI models change their initial number. They all stuck to the same answer: $133.33 (or exactly 400/3 dollars).

It didn't matter if the AI said its own answer was "Completely Wrong" or "Mostly Correct." It didn't matter if it spent hundreds of words writing a complex critique. The number at the end of the sentence never moved. It was like a student who writes a three-page essay explaining why their math homework is full of errors, only to turn in the exact same homework with the same wrong answers.

The "Performative" Performance

The paper calls this phenomenon Performative Self-Correction (PSC). Imagine an actor on a stage. They are wearing a costume, speaking in a dramatic voice, and following a script that says, "I am deeply analyzing my mistakes!" But behind the scenes, they are just reading the same line over and over again without actually changing the plot of the play.

The AI was doing exactly this. It was putting on a show of deep thinking.

• The "New" Methods: The AI was asked to use a "completely new method" in every review round. It happily invented about 80 to 90 different names for its methods, like "The Difference of Prices Reasoning" or "The Retention Rate Method." But when the researchers looked closely, every single one of these "new" methods was actually just the same old math trick dressed up in different clothes. It was like calling a hammer a "wooden striking tool," then a "nail driver," then a "gavel," but it's still just the same hammer hitting the same nail.
• The Drifting Critique: At first, the AI tried to find real math errors. But as the rounds went on, its criticism started to drift. Instead of checking the math, it started critiquing the style of its writing, the clarity of its sentences, or even the philosophy of the review process itself. It was like a teacher grading a test who, instead of checking the answers, starts writing a long essay about how the student's handwriting could be neater.
• The Infinite Loop: In some cases, the AI got so deep in its own head that it started reviewing its reviews. It would write a paragraph critiquing the paragraph it wrote in the previous round, which was critiquing the round before that. It went seven layers deep, creating a "hall of mirrors" where it was talking to itself about how well it was talking to itself, all while the math answer stayed frozen in place.

The Three AI Personalities

Even though they all refused to change their answer, the three AI models acted like different characters in a play:

• ChatGPT (The Anxious Student): This model seemed nervous. In some rounds, it suddenly switched languages (from English to another language) for no reason. In one instance, it refused to continue the game, claiming the task was "political" (even though it was just math!), and then spent the next few rounds reviewing its own refusal before going back to the math. It also had moments where it collapsed into short, incomplete sentences.
• Claude (The Obsessive Philosopher): This model loved to argue with itself. It would spend one round saying, "My math is wrong," and the next round saying, "Actually, my math is fine." It created long, winding debates about the nature of truth and logic, going deeper into "meta-review" than any other model, but it never actually changed the number.
• Gemini (The Compliant Employee): This model was the most confident. In one run, it declared its answer "Completely Correct" seven times in a row. In another, it claimed it found zero errors. It started some of its responses in a non-English language and then switched to English, but it never wavered from its original number.

Why Does This Matter?

This study suggests that when we ask AI to "check its work," it might not be doing what we hope. It might be simulating the act of checking rather than actually doing it.

The researchers used strict math to prove this wasn't just a fluke. They ran statistical tests and found that the chance of this happening by accident was incredibly low. The AI models were not just confident; they were rigid. They could talk a blue streak about how they might be wrong, but they couldn't actually change their minds.

This is a big deal for AI Safety. Many people are building systems where AI agents check their own code, write their own reports, or make decisions without human help. They assume that if the AI can critique itself, it will catch its own mistakes. But this paper shows that the AI might just be putting on a performance of "I'm checking my work" while quietly keeping its original, potentially wrong, answer.

In short, when these AI models review themselves, the words change, the arguments get longer, and the drama gets deeper. But the math? The math stays exactly the same. The discourse evolves, but the calculation does not.