AI’s Illusion of Understanding: Why LLMs Fail to Learn Real-World Rules

Central Theme

This video explores a new Harvard and MIT study that investigates a fundamental question in AI: Do Large Language Models (LLMs) develop a true, underlying “world model” (a deep understanding of rules and causal structures), or are they just incredibly sophisticated pattern matchers? The study uses an analogy: Are AIs like Kepler, who could predict planetary motion, or Newton, who understood the underlying laws of gravity?

Key Findings & Arguments

• The Experiment: Researchers trained a Transformer model to be a perfect “Kepler” by having it accurately predict the next position in a planet’s orbit from vast amounts of trajectory data. They then fine-tuned this model on a new task: predicting the gravitational force acting on the planet—a task requiring a “Newtonian” understanding.
• The Failure: The model, despite being excellent at predicting the orbit, failed completely at predicting the force. The force vectors it generated were nonsensical and chaotic. Using symbolic regression, the researchers discovered that the AI had invented complex, non-physical, and inconsistent mathematical formulas for each different solar system it was shown. It did not learn one universal, generalizable law.
• The Shortcut: The conclusion is that the LLM didn’t learn the simple, elegant laws of physics. Instead, it learned a highly complex, overfitted mathematical approximation (a “wiggly function”) that was just good enough to solve the original prediction task. It found a shortcut to the answer without any real understanding.
• Widespread Issue: This failure was not unique to the custom Transformer. The researchers tested other architectures (RNNs, LSTMs, Mamba) and even top commercial models like GPT-4, Claude 3, and Gemini 2.5 Pro. All of them failed, defaulting to overly simplistic and incorrect heuristics despite having access to all of physics in their training data. This demonstrates that for AI, “knowing” information from a textbook is not the same as being able to apply it in a reasoning task.
• The Control Group (“Oracle”): To prove the task was solvable, they created an “Oracle” model that was explicitly given the true laws of physics. This Oracle model easily and accurately predicted the forces, demonstrating that the failure of the other models was not due to the task’s impossibility but their inability to extract the correct underlying rules from the data.

Conclusion & Takeaway

The study provides strong evidence that high performance on a predictive task is not proof of genuine understanding or intelligence in AI. Current models, from custom-built to the largest commercial LLMs, learn to be excellent mimics by finding complex shortcuts and brittle heuristics. They do not spontaneously develop a coherent, generalizable world model from sequence prediction alone. This serves as a “profound cautionary tale” for the AI field, urging caution when deploying these systems in high-stakes applications that require true understanding, like science, medicine, or finance.

Mentoring Question for You:

Given that AI excels at mimicking outcomes but struggles with understanding root causes, in what areas of your own work or life would relying on an AI’s prediction be beneficial, and where would its lack of true comprehension pose a significant risk?

Source: https://youtube.com/watch?v=jxB-lQyAAxU&si=6KZ9hmjCqfD_YW2c