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Thought Simulation: A simple 2-step prompt architecture that nearly doubles LLM reasoning success rate.
Hi everyone,
I'd like to share the results of my experiment with a zero-shot prompting method I call "Thought Simulation." It's a simple, training-free way to improve an LLM's ability to solve complex logical problems.
The method is a two-step process:
Analytical Step (temp=0.2): The model first acts as a cold analyst to break down the problem.
Synthesis Step (temp=0.7): The model then uses this analysis to formulate a final, comprehensive answer.
The Results:
In a series of 17 challenging reasoning tasks performed on the latest Claude 4 Sonnet model (claude-sonnet-4-20250514), the results were striking:
Standard Prompting (Baseline): ~35% success rate
Thought Simulation (My Method): ~65% success rate
This simple architecture nearly doubled the model's effectiveness. Crucially, it unlocks answers to questions previously unsolvable by standard prompting, allowing the model to spot flawed premises or demonstrate deeper, qualitative reasoning.
The method is also universal, with similar effectiveness boosts observed on smaller, locally-run models (like the Polish Bielik family). This structured 'Analyst-Synthesist' approach seems more robust for certain tasks than a standard CoT monologue.
I've detailed the full experiment in the articles below. I'd be grateful for your feedback!
Article in English (on Medium): https://medium.com/@brainhome9/thought-simulation-how-a-two-step-prompt-nearly-doubles-llm-efficacy-in-solving-complex-problems-9ba8196e2c26
Artykuł po Polsku: https://gadzety360.pl/2025/06/19/symulacja-myslenia-jak-dwuetapowy-prompt-niemal-podwaja-skutecznosc-llm-w-rozwiazywaniu-zlozonych-problemow/
The implementation code will be published soon. Thanks for reading!
Hi everyone,
I'd like to share the results of my experiment with a zero-shot prompting method I call "Thought Simulation." It's a simple, training-free way to improve an LLM's ability to solve complex logical problems.
The method is a two-step process:
Analytical Step (temp=0.2): The model first acts as a cold analyst to break down the problem.
Synthesis Step (temp=0.7): The model then uses this analysis to formulate a final, comprehensive answer.
The Results:
In a series of 17 challenging reasoning tasks performed on the latest Claude 4 Sonnet model (claude-sonnet-4-20250514), the results were striking:
Standard Prompting (Baseline): ~35% success rate
Thought Simulation (My Method): ~65% success rate
This simple architecture nearly doubled the model's effectiveness. Crucially, it unlocks answers to questions previously unsolvable by standard prompting, allowing the model to spot flawed premises or demonstrate deeper, qualitative reasoning.
The method is also universal, with similar effectiveness boosts observed on smaller, locally-run models (like the Polish Bielik family). This structured 'Analyst-Synthesist' approach seems more robust for certain tasks than a standard CoT monologue.
I've detailed the full experiment in the articles below. I'd be grateful for your feedback!
Article in English (on Medium): https://medium.com/@brainhome9/thought-simulation-how-a-two-step-prompt-nearly-doubles-llm-efficacy-in-solving-complex-problems-9ba8196e2c26
Artykuł po Polsku: https://gadzety360.pl/2025/06/19/symulacja-myslenia-jak-dwuetapowy-prompt-niemal-podwaja-skutecznosc-llm-w-rozwiazywaniu-zlozonych-problemow/
The implementation code will be published soon. Thanks for reading!