#Problem Solving

Explore 'Tree of Thoughts', an innovative method for problem-solving with large language models. Easily integrate using OpenAI's API, with instructions for both PyPI and source installation. Start by tackling tasks like the game of 24 using GPT-4 and conduct experiments across different domains. Contribute to the community by offering feedback and find detailed descriptions and methods in the accompanying paper for deeper insights.

Learn about ToRA, a collection of tool-integrated reasoning agents that address complex mathematical problems through efficient interaction with external tools like computation libraries and symbolic solvers. These models boost natural language reasoning and computational proficiency, achieving notable advances in tasks like GSM8k and MATH. Find out how ToRA-Code-34B, among other models, sets new standards in the field. Explore open-source resources including code, models, and training scripts that promote collaborative progress in mathematical problem-solving.

This survey offers an in-depth look at LLM-based multi-agent systems, organizing research into frameworks, orchestration, problem-solving, world simulation, and datasets. It explores the architecture and efficiency, showing their potential in fields like software development and embodied agents. Regular updates ensure the inclusion of the latest studies. The project provides insights on how LLM agents enhance problem-solving and simulate complex systems, serving as a valuable resource for understanding the dynamics of large language models in multi-agent contexts.