ReflectiveAgent: An LLM Agent with Continuous Improvement via Self-Reflection

Project Basic Information

• Original Author/Maintainer: rzadrze
• Source Platform: GitHub
• Original Title: ReflectiveAgent
• Original Link: https://github.com/rzadrzi/ReflectiveAgent
• Release/Update Time: 2026-05-27T14:58:04Z

Core Viewpoint

ReflectiveAgent is a self-improving LLM agent focused on solving logical puzzles. It addresses hallucinations and broken reasoning chains in complex logical problems faced by LLMs through iterative reasoning, self-reflection, and adaptive behavior mechanisms, achieving continuous performance improvement in structured problem domains.

Large Language Models (LLMs) perform strongly in various tasks, but often suffer from hallucinations or broken reasoning chains when dealing with complex problems involving multi-step reasoning and logical constraints. Traditional LLM applications mostly generate answers in one go, lacking error identification and correction mechanisms.

The project's motivation comes from the human learning process: approaching goals through a cycle of trial, error, reflection, and adjustment. This cognitive mechanism is introduced into LLMs to improve the reliability and accuracy of logical reasoning tasks.

ReflectiveAgent adopts a modular architecture centered around "iterative improvement":

Reasoning Engine

Supports multi-round reasoning, allowing the model to show intermediate thinking and provide analyzable materials for self-reflection.

Self-Reflection Module

Evaluates solutions from the perspective of a "third-party reviewer", covering dimensions like logical consistency, constraint satisfaction, and rationality of reasoning steps, implemented via prompt engineering.

Adaptive Behavior Mechanism

Triggers correction processes based on reflection results, feeds back problems to the reasoning engine to start a new iteration, forming a closed loop.

Prompt Engineering

Utilizes the context learning ability of LLMs, defines behavioral norms for each module through system prompts, directly affecting the quality of reflection and the effectiveness of correction suggestions.

State Management

Uses structured state representation to maintain conversation history, intermediate results, reflection conclusions, and correction suggestions, ensuring complete information transmission.

Termination Conditions

Multi-level exit mechanism: preset maximum number of iterations, reflection confirming the solution is correct, or no further improvement possible—balancing efficiency and avoiding infinite loops.

Application Scenarios

Targets logical puzzles and constraint satisfaction problems; test sets cover classic constraint reasoning, logical riddles, and combinatorial optimization challenges.

Evaluation Metrics

• Final answer accuracy rate
• Number of iterations to reach the correct solution
• Error identification ability of the reflection module
• Scalability for complex problems

The evaluation framework comprehensively measures the actual benefits of the self-reflection mechanism.

1. Architecture Improves Performance: Without replacing the underlying model, the system's performance is significantly improved through architectural design; self-reflection endows the model with metacognitive abilities.
2. Advanced Application of Prompt Engineering: Using prompts to define complex interaction protocols and achieve module collaboration, providing references for complex LLM applications.
3. Engineering Thinking Reference: The concept of iterative improvement is similar to test-driven development and continuous integration in software engineering, which can improve the reliability of AI systems.

Limitations

• The iterative process increases latency
• The accuracy of the reflection module affects the system's upper limit, with the recursive risk of "model correcting model"

Future Directions

• Introduce external validators to replace model self-evaluation
• Explore more efficient reflection strategies
• Expand to a wider range of task domains
• Combine reinforcement learning to allow reflection strategies to learn from experience

ReflectiveAgent introduces a self-reflection mechanism into the LLM agent architecture, providing an innovative approach to improving the reliability of LLMs in logical reasoning tasks. Its core value lies in demonstrating how to compensate for the limitations of single model calls through system design, offering useful references for building more robust and trustworthy AI applications.