richards199999 / Self-Iterative-Agent-System-for-Complex-Problem-Solving

Multiple instructed-LLMs engage in multi-round "self-questioning" to seek the optimal solution, borrowing from the idea of debate, iteratively refining the current problem-solving process, and finally selecting the optimal solution to give a conclusion.

Self-Iterative Agent System for Complex Problem Solving

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Link to the CN version: Here

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This was also my solution for the Alibaba Mathematical Competition (AI Challenge). I was surprised that it was so simple yet achieved such good results compared to others who used complex multi-agent systems.

Let me share my general thoughts:

I let multiple LLMs engage in multi-round "self-questioning" to seek the optimal solution, borrowing from the idea of debate, iteratively refining the current problem-solving process, and finally selecting the optimal solution to give a conclusion.

This system contains two sets of models: the main model/student model and the evaluation model/teacher model. The overall process is roughly as follows:

1. The main model (such as GPT-4 Turbo or Claude-3 Opus) answers math problems step by step according to detailed instructions:
   • List relevant knowledge points
   • Write down initial thought process
   • Expand on the specific calculation process (without skipping steps)
   • Give the final result
2. The main model's answer is submitted to the evaluation model for review. The evaluation model also proceeds step by step:
   • Overview to check for obvious loopholes and give initial feedback
   • Carefully check the calculation process to find possible errors
   • Evaluate whether the reasoning logic is rigorous
   • Summarize all feedback
3. The evaluation model's feedback is returned to the main model for reference to revise the answer.
4. Repeat the above process iteratively several times (such as 5 times) to continuously improve the answer.
5. Iterate using two sets of models separately to finally obtain two revised answers.
6. Use GPT-4 Turbo to score and compare the two answers in terms of calculation, logic, clarity, etc., and provide the best answer.

Of course, this approach also has much room for improvement. I also exchanged ideas with members of another two teams and made a small summary and reflection:

1. Regardless of the form of the Agent solution, it should be ensured that the information communication and reasoning between Sub-agents cannot go wrong. To ensure this, I believe a global "supervisor" is needed to monitor the Agent process in real-time and intervene promptly when problems arise to prevent error amplification due to a single node error.

2. If the system extracts related concept knowledge points from a preset knowledge base using RAG in the concept sorting stage, and uses external tools for calculation, the results will certainly be better than a solution using only LLMs.

3. In complex tasks, Agent or Agent-like architectures appear more important and effective.

4. Currently, there are not many open-source or closed-source models with outstanding abilities in Math and Reasoning, and the models' capabilities in these two aspects still need to be strengthened.