Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team

Summary

This paper talks about Xolver, a system where multiple agents work together like a team to help large language models solve complex problems by remembering past experiences and using different kinds of information.

What's the problem?

The problem is that large language models often try to come up with solutions from scratch every time, which can be slow and less accurate for difficult reasoning tasks because they don't remember or learn from past experiences.

What's the solution?

The researchers created Xolver, a framework where multiple agents collaborate, share knowledge, and remember past experiences across different types of data. This approach helps the models build on what they already know instead of starting fresh each time, leading to better reasoning and faster solving of hard problems.

Why it matters?

This matters because it makes AI models smarter and more efficient by allowing them to learn from history and work together like a team, which can improve performance on challenging tasks that need deep thinking and experience.