The Socratic Code Review: Can AI Predict Physics?
Author: David J. Hoxie & Google Gemini
Title: The Socratic Code Review: Can AI Predict Physics? Author: David J. Hoxie & Google Gemini
Context: In our ongoing research into Large Language Models in Physics, we often focus on generation—asking the AI to write code. But true utility is bidirectional. Can the model look at a raw, un-annotated simulation script and correctly infer the physical reality it describes?
The Demonstration: In this video, we flip the script. Instead of asking Gemini to generate a simulation, we feed it a pre-written particle simulation code—specifically, a system involving forces and statistical distribution.
The Constraint: We provided context ("priming") that it was a particle system, but we did not describe the output.
The Task: We asked Gemini to analyze the code logic and predict the visual outcome.
The Results: Inference vs. Pattern Matching As demonstrated in the video, Gemini didn't just summarize the syntax (e.g., "this is a for-loop"). It recognized the physics emerging from the math.
Force Detection: It correctly identified that the vector math represented physical forces acting on bodies.
Goal Clarification: It inferred the teleology of the code—what the simulation was trying to measure.
The Prediction: Most importantly, it correctly predicted the final visual state: A Histogram.
Why This Matters for Research: This capability—Predictive Code Analysis—suggests that Gemini can serve as more than just a coding assistant. It can act as a technical reviewer. If an AI can read your code and tell you, "This will result in a histogram," when you intended to build a wave, it acts as a logic check before you ever run the compiler. This is the definition of a "Socratic Partner" in computational physics.
Summary: We are moving from using AI as a "Writer" to using AI as a "Reader." When the model can predict the physical outcome of code it didn't write, we have a measurable demonstration of its ability to reason about the code's intent.
AI Collaboration Note: This video, its title card, description, and the concepts explored within were developed in a deep, recurrent collaboration with Google Gemini. Our process involves Gemini acting as a Socratic partner, a technical reviewer, and a creative collaborator, helping to refine, structure, and articulate the final concepts and this description.
References:
[1] Imran, M., & Almusharraf, N. (2024). "Google Gemini as a next generation AI educational tool: a review of emerging educational technology." Smart Learning Environments, 11(1), 22.
[2] Shiffman, D. (2024). The Nature of Code: Simulating Natural Systems with JavaScript. No Starch Press.
[3] Marquardt, F. (2021). "Machine learning and quantum devices." SciPost Physics Lecture Notes, 29.