Modular Large Language Model (LLM) Guided Tree-of-Thought System

A method for a problem-solving system, the method comprising:

• receiving using a prompter agent, a problem description of a problem from a user,
• wherein the problem-solving system comprises a tree-of-thought (ToT) controller, the prompter agent, a memory module, and a checker module,
• wherein the ToT controller comprises a controller policy network, and
• wherein the prompter agent comprises a prompter policy network;
• generating, using the prompter agent and based on the problem description, a first prompt to a large language model (LLM) to generate a first intermediate solution to the problem;
• checking, using the checker module, a validity of the first intermediate solution;
• storing, in the memory module, the first prompt, the first intermediate solution, and the validity of the first intermediate solution, as parts of a conversation and node visit history,
• wherein nodes in the conversation and node visit history form a search tree, and
• wherein each node in the search tree is associated with a partial solution to the problem;
• checking, using the checker module, a validity of a current partial solution associated with a current node in the search tree;
• determining, using the ToT controller and based on the validity of the current partial solution and the conversation and node visit history, a next node to visit,
• wherein the next node to visit is an ancestor node of the current node in the search tree,
• wherein the next node to visit is determined by the controller policy network, and
• wherein the controller policy network takes as input a position embedding of a sequence of last visited nodes;
• querying the memory module to retrieve an ancestor partial solution associated with the ancestor node;
• generating, using the prompter agent and based on the ancestor partial solution, a second prompt to the LLM to generate a second intermediate solution to the problem;
• storing, in the memory module, the second prompt and the second intermediate solution, as parts of the conversation and node visit history;
• and
• determining, using the checker module, whether the second intermediate solution is a valid final solution to the problem.

A tree-of-thought (ToT) problem-solving system, comprising:

• access to a large language model (LLM);
• access to a processor;
• a non-transitory physical medium for storing program code executable by the processor, the program code when executed by the processor causing the processor to implement:
• a prompter agent comprising a prompter policy network, adapted to generate a prompt for the LLM;
• a memory module adapted to store a conversation and node visit history, wherein the conversation and node visit history comprises conversations between the LLM and the prompter agent;
• a checker module adapted to determine a validity of a given partial solution associated with a given node in a search tree of a search process;
• and
• a ToT controller comprising a controller policy network, wherein the TOT is adapted to direct the search process of the ToT problem-solving system by determining a backtracking policy based on a state of the memory module,
• wherein the non-transitory physical medium further stores program code that when executed by the processor causes the ToT controller to:
• receive, using a prompter agent, a problem description of a problem from a user,
• generate, using the prompter agent and based on the problem description, a first prompt to the LLM to generate a first intermediate solution to the problem;
• check, using the checker module, a validity of the first intermediate solution;
• store, in the memory module, the first prompt, the first intermediate solution, and the validity of the first intermediate solution, as parts of the conversation and node visit history,
• wherein nodes in the conversation and node visit history form the search tree, and
• wherein each node in the search tree is associated with a partial solution to the problem;
• check, using the checker module, a validity of a current partial solution associated with a current node in the search tree;
• determine, using the ToT controller and based on the validity of the current partial solution and the conversation and node visit history, a next node to visit,
• wherein the next node to visit is an ancestor node of the current node in the search tree,
• wherein the next node to visit is determined by the controller policy network, and
• wherein the controller policy network takes as input a position embedding of a sequence of last visited nodes;
• query the memory module to retrieve an ancestor partial solution associated with the ancestor node;
• generate, using the prompter agent and based on the ancestor partial solution, a second to prompt to the LLM to generate a second intermediate solution to the problem;
• and
• determine, using the checker module, whether the second intermediate solution is a valid final solution to the problem.