Iterative Reflective Expansion (IRE) Algorithm Documentation¶

The Iterative Reflective Expansion (IRE) Algorithm is a sophisticated reasoning framework that employs iterative hypothesis generation, simulation, and refinement to solve complex problems. It leverages a multi-step approach where an AI agent generates initial solution paths, evaluates their effectiveness through simulation, reflects on errors, and dynamically revises reasoning strategies. Through continuous cycles of hypothesis testing and meta-cognitive reflection, the algorithm progressively converges on optimal solutions by learning from both successful and unsuccessful reasoning attempts.

Architecture¶

graph TD
    Problem_Input["🧩 Problem Input"] --> Generate_Hypotheses
    Generate_Hypotheses["Generate Initial Hypotheses"] --> Simulate
    subgraph Iterative Reflective Expansion Loop
        Simulate["Simulate Reasoning Paths"] --> Evaluate
        Evaluate["Evaluate Outcomes"] --> Reflect{Is solution satisfactory?}
        Reflect -->|No, issues found| Meta_Reflect
        Reflect -->|Yes| Promising
        Meta_Reflect["Meta-Cognitive Reflection"] --> Revise_Paths
        Meta_Reflect --> Memory[(Reasoning Memory)]
        Meta_Reflect --> Memory
        Revise_Paths["Revise Paths Based on Feedback"] --> Expand_Paths
        Meta_Reflect --> Revise_Path
        Revise_Path["Revise Paths"] --> Expand_Paths
        Expand_Paths["Iterative Expansion & Pruning"] --> Simulate
    end
    Promising["Promising Paths Selected"] --> Memory
    Memory["Memory Integration"] --> Synthesize
    Synthesize["Synthesize Final Solution"] --> Final["Final Solution ✅"]

Workflow¶

Generate initial hypotheses
Simulate paths
Reflect on errors
Revise paths
Select promising paths
Synthesize solution

Class: IterativeReflectiveExpansion¶

Arguments¶

Argument	Type	Default	Description
agent	Agent	None	The Swarms agent instance used to perform reasoning tasks.
max_iterations	int	5	Maximum number of iterations for the reasoning process.
return_list	bool	False	If True, returns the conversation as a list of messages.
return_dict	bool	False	If True, returns the conversation as a dictionary of messages.
prompt	str	GENERAL_REASONING_AGENT_SYS_PROMPT	The system prompt for the agent.

Methods¶

Method	Description
generate_initial_hypotheses	Generates an initial set of reasoning hypotheses based on the problem input.
simulate_path	Simulates a given reasoning path and evaluates its effectiveness.
meta_reflect	Performs meta-cognitive reflection on the provided error information.
revise_path	Revises the reasoning path based on the provided feedback.
select_promising_paths	Selects the most promising reasoning paths from a list of candidates.
synthesize_solution	Synthesizes a final solution from the promising reasoning paths and historical memory.
run	Executes the Iterative Reflective Expansion process on the provided problem.

Use-Cases¶

Example 1: Solving a Mathematical Problem¶

from swarms import IterativeReflectiveExpansion

agent = IterativeReflectiveExpansion(
    max_iterations=3,
)

agent.run("What is the 40th prime number?")

Conclusion¶

The Iterative Reflective Expansion (IRE) Algorithm is a powerful tool for solving complex problems through iterative reasoning and reflection. By leveraging the capabilities of a Swarms agent, it can dynamically adapt and refine its approach to converge on optimal solutions.