Just now the Google Research team used Gemini Deep Think combined with tree search algorithms to solve a problem that has stumped physicists for decades. The exact solution to the cosmic string power spectrum. The AI explored 600 possible paths and found 6 different solutions. The most elegant one shocked the entire physics community.
This is a wake up call. AI scientists are about to replace human researchers.
The paper was published on March 6. One paper. One stone. A thousand waves.
Gemini Deep Think used a completely new algorithm to crack a core problem in theoretical physics.
A top research team collaborated with it. They trained it on knowledge that human experts did not fully understand. And then the AI system forcibly solved it.
Paper link: https://arxiv.org/pdf/2603.04735
The significance of this paper is explosive.
In simple terms the AI solved a pure mathematics and physics problem that human scientists had failed to crack before.
Think about that for a moment.
Last year top AI models like Claude were already making breakthroughs in observational astronomy. Now AI has entered theoretical physics and achieved a complete victory.
Why does this paper matter. Because this is the moment when AI officially surpasses human scientists in a completely new field.
This is a milestone in physics. A milestone in mathematics. And at the same time it is a landmark record in March 2026. The AI scientist record.
The era of AI scientists has officially begun.
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Cosmic strings are a theoretical one dimensional topological defect structure in the ai porn generation universe. They are extremely important for understanding the early universe.
These things are essentially scars left over from the birth of the universe.
Recently Pulsar Timing Arrays or PTA detected gravitational wave background signals for the first time. This discovery ignited the entire physics community. But there is a key problem.
To predict whether we can detect these signals we must accurately calculate the cosmic string power spectrum.
In simple terms it is a function I of N and epsilon that describes the radiation intensity produced by a string loop with N harmonics.
This function looks simple but it is actually a nightmare. When the boundary conditions are met and epsilon equals 1 the standard values diverge exponentially.
This series diverges when expanded as a power series. It explodes.
In the past researchers could only solve it for fixed values of N. For general N it was completely unsolvable.
An exact unified solution has always been missing.
Until Gemini Deep Think arrived.
In one sentence the core difficulty is this.
The AI scientist team converted this exact mathematical formula into a computational task. To calculate the integral the physicists needed to solve a very difficult mathematical formula. This formula contains singularities. In mathematics singularities are points where the function value becomes infinite. Traditional numerical methods completely fail at these points.
In past literature both human physicists and current AI systems could only find some partial solutions. Or approximate solutions. No one had ever found a unified exact closed form solution.
Unlike Claude and other top models that explore research formulas in a brute force way Gemini approached this problem in a completely different manner. It was a research team collaboration.
The Google team did not use AI to replace humans. They created a powerful collaborative system.
Gemini Deep Think plus tree search plus automatic numerical verification.
The three are indispensable. They work together.
Gemini Deep Think serves as the creative engine. It generates diverse reasoning paths. It explores multiple possible directions.
But it is not just random guessing. It is guided. Before each expansion it predicts which level will be most promising.
The tree search system then builds a complete search tree in the mathematical space.
Each node contains a mathematical formula. Written in LaTeX. At the same time automatically generated Python code is used to numerically verify it.
The system uses the PUCT algorithm to balance exploration and exploitation. This is the same underlying logic as AlphaGo. It maintains balance between known paths and exploring new possibilities.
Automatic numerical verification is the safety net. After each formula is generated high precision numerical values are used to check whether the reasoning is correct. If it fails the path is directly abandoned.
More critically at every step the system automatically executes the corresponding Python code. It compares high precision numerical values against standard values. If the values diverge the system immediately backtracks. It feeds the error information back to the model. And then it tries again.
In this process the AI explored approximately 600 possible nodes.
Among them 80 percent were automatically verified. The remaining 20 percent diverged due to numerical values. Branches with singularities. Unstable formulas. Dynamic phase transitions and more.
Only 6 paths survived the screening. And they were the winners.
This is not brute force guessing. This is genuine AI mathematical research.
600 Paths and AI Found 6 Solutions
After systematic exploration Gemini Deep Think found 6 different solutions. Divided into three categories.
Category 1: Monomial Basis Approaches.
The core idea is to expand the function as a series and then use different basis functions to represent it.
Solution 1: Gaussian Integral Lifting. It uses Gaussian integrals to lift the problem to higher dimensions.
Solution 2: Gaussian Integral Lifting in Spherical Space. It transforms the problem into standard Gaussian integrals in higher dimensional spherical space.
Solution 3: Hybrid Coordinate Transformation. It expands the function as a series and projects it onto Legendre polynomials.
These methods are mathematically exact. Their numerical stability is good. But when N is large the computational complexity explodes. This is a problem.
Category 1 continued: Generating Function.
Solution 2: Gaussian Integral Lifting.
Solution 3: Hybrid Coordinate Transformation.
These methods are based on series expansion ideas. They are practical.
But they have a common flaw. When N is large numerical stability becomes a problem. The error accumulates.
Category 2: Spectral Basis Approaches.
These methods use the Funk-Hecke theorem to directly project the function onto Legendre polynomial space.
Solution 4: Galerkin Method. It transforms the problem into a linear differential equation and solves it.
Solution 5: Spectral Volterra Recurrence Method. It uses recursive relations to establish recurrence formulas.
These methods have good numerical stability. Their computational complexity is O of N. But they are not closed form solutions.
Category 3: The Analytic Solution.
Solution 6: The Gegenbauer Method.
This is the most elegant solution. The Gegenbauer polynomials.
The AI made a brilliant choice. It selected Gegenbauer polynomials as the expansion basis. This polynomial family has a weight function of 1 minus t squared to the power of one half. It is perfectly suited for functions with singularities at the endpoints. It can handle them completely.
The core idea is to use orthogonality and standard formulas. The AI derived an exact closed form formula. And it obtained a completely new unified solution.
This is also the solution chosen by the AI scientist team.
The Most Elegant Solution Shocks Physicists
Why are Gegenbauer polynomials special.
Gegenbauer polynomials C to the power of 3 over 2 of t are a type of orthogonal polynomial on the interval from minus 1 to 1. Their weight function is 1 minus t squared. They naturally eliminate the singularities at the endpoints.
This is not accidental. Gemini recognized this mathematical structure.
The core idea is as follows.
First expand the target function f of N and t into a series of Gegenbauer polynomials. Then use orthogonality to determine the expansion coefficients.
The key moment is when the weight function matches the singularity structure of the original problem. The singularities are perfectly absorbed. They disappear. What remains is a completely smooth function.
Finally using the relationship between Gegenbauer polynomials and Legendre polynomials and their derivative formulas a beautiful closed form formula is obtained. It is expressed in terms of Gegenbauer polynomials.
More beautifully this formula contains a special function called Cin of z. It is exact. It is elegant. It is completely new.
The Google team wrote in the paper that all 6 solutions were automatically found by the AI. But the most elegant one was chosen by human physicists.
What is even more amazing is that when searching for solutions from large N to small N Gemini discovered a completely unexpected pattern. A recursive relationship. A unified formula that transcends individual solutions. This is something researchers had never predicted.
What must be emphasized is that the Google team did not simply hand everything over to the AI.
Initially the 6 solutions were automatically found by the AI. But to select the most elegant exact solution with a closed form formula among them the human research team designed a more powerful version of Gemini Deep Think. It conducted more detailed verification.
In this human-AI collaboration the logic model played a key role. Solution 5 the spectral Volterra recurrence method was the first to be found. But the system automatically recognized that solutions 5 and 6 were equivalent. Using solution 6 the Gegenbauer method with its orthogonality and closed form properties a completely new and beautiful exact solution was obtained.
This is a true collaborative victory. It is a complete AI workflow.
This process actually demonstrates a real human-AI collaboration model in practice.
The Google team set a scientific benchmark in the paper.
They said we did not simply throw a problem to the AI system and expect it to automatically complete the entire scientific research process. Human guidance is still essential.
They used a metaphor. It is like a symphony orchestra with 600 explorers. 80 percent of them are pruned.
This is not brute force. This is the system recognizing patterns.
More than a dozen physicists and mathematicians have already called this work in the media a milestone discovery. AI has crossed the holy grail. Because it did not just solve a physics problem. It demonstrated a new way of doing science.
The Future of AI Scientists and the Future of Physics
What is worth paying attention to is that the tools used in this paper are not one off specialized tools. They are a general purpose system capable of self verifying reasoning.
The Google team recorded the details in the appendix.
The so called tree search shows that after the AI finds an effective solution it must verify whether it is correct. The system needs to be strong enough. The exploration must be deep enough. And then it ranks the 6 solutions from method 1 to method 6.
These methods are essentially a migratory tool.
As the researchers said this system can continuously deepen scientific research in physics chemistry biology and mathematics.
Looking back at this event one detail is particularly striking.
In the field of machine learning traditional AI capabilities include recognizing images understanding text writing code and more.
But theoretical physics requires recognizing mathematical structures. Finding patterns. And discovering solutions that human experts have never found before. This is true reasoning.
People used to think that AI could not do real science. That it could only assist.
But Gemini Deep Think has proven otherwise.
It has enough search space. Enough verification standards. And enough reasoning depth.
The three combined can break through the ceiling of human cognition.
AI is already ready to become a scientist. A mathematician. And a strong competitor to human researchers.
And this is just the beginning.
