Implementation:Iamhankai Forest of Thought Get Fot Final Answer

Knowledge Sources	Forest-of-Thought Forest-of-Thought
Domains	Ensemble_Methods, Decision_Theory
Last Updated	2026-02-14 03:00 GMT

Overview

Concrete tool for selecting the forest's final answer via CGDM strategy provided by the Forest-of-Thought repository.

Description

The get_fot_final_answer method on Monte_Carlo_Forest implements the CGDM selection strategy. It first attempts majority voting via most_frequent_elements(). If a unique majority exists, it returns that answer. If a tie occurs, it formats all candidate answers into a prompt and queries the LLM expert to select the best one. Results are cached in expert_memory to avoid redundant LLM calls for repeated questions.

Usage

Called at the end of each forest evaluation (after all trees complete or early stopping triggers). This is the final step before recording the answer as the forest's prediction.

Code Reference

Source Location

• Repository: Forest-of-Thought
• File: run_with_mcf_stop_noearly.py
• Lines: L556-599

Signature

def get_fot_final_answer(
    self, query, activated_answers_list,
    total_answers_list, activated_answer_scores=[],
    t=-1, fot=False
) -> str:
    """
    Select final answer using configured stopping strategy.

    Args:
        query (str): Original question.
        activated_answers_list (list): Extracted answer labels from trees.
        total_answers_list (list): All raw generated answer strings.
        activated_answer_scores (list): Reward scores for answers.
        t (int): Tree index for tracking.
        fot (bool): Forest-of-trees context flag.

    Returns:
        str: Best selected answer string.
    """

Import

# Method on Monte_Carlo_Forest class
# Called as: mcf.get_fot_final_answer(query, answers, total, scores)

I/O Contract

Inputs

Name	Type	Required	Description
query	str	Yes	Original math problem question
activated_answers_list	list[str]	Yes	Extracted answer labels from all trees
total_answers_list	list[str]	Yes	All raw answer strings from trees
activated_answer_scores	list[float]	No	MCTS reward scores (default: [])
t	int	No	Tree index for tracking (default: -1)
fot	bool	No	Forest context flag (default: False)

Outputs

Name	Type	Description
best_ans	str	Selected final answer string

Usage Examples

# Called internally by Monte_Carlo_Forest after tree execution
mcf = Monte_Carlo_Forest(args)
# ... after trees complete ...

best_answer = mcf.get_fot_final_answer(
    query="What is the sum of all primes less than 10?",
    activated_answers_list=["17", "17", "17", "15"],
    total_answers_list=["The answer is 17.", "17", "Sum is 17.", "I think 15."],
    activated_answer_scores=[0.8, 0.9, 0.85, 0.3]
)
# Returns "17" (majority vote)

Related Pages

Implements Principle

• Principle:Iamhankai_Forest_of_Thought_CGDM_Answer_Selection

Uses Heuristic

• Heuristic:Iamhankai_Forest_of_Thought_Early_Stop_Majority_Vote