The paper presents a new reasoning framework—Agentic Reasoning, aimed at enhancing the reasoning capabilities of large language models (LLMs) by integrating external tools. This framework improves the model’s performance in logical consistency, factual accuracy, and deep research ability through structured memory (mind maps), web searches, and computational analysis. The experimental results show that Agentic Reasoning outperforms existing large language models in expert-level question answering and real-world research tasks, effectively synthesizing knowledge and providing more interpretable and verifiable reasoning results. Through the structured use of external tools, Agentic Reasoning offers new pathways for expert-level problem-solving. Future work will explore extending this framework to multimodal data and real-time adaptability to further enhance AI’s performance on complex real-world issues.
1 Main Content of the Paper
This paper proposes a new method—Agentic Reasoning, aimed at making large language models smarter and more accurate when dealing with complex problems. Existing large language models (e.g., GPT-4) can handle many tasks, but their performance often falls short when faced with problems requiring deep reasoning, logical analysis, and multi-step reasoning. To address this issue, the proposed Agentic Reasoning framework allows the model to enhance its reasoning capabilities by integrating external tools. For example, the model can utilize a “mind map” to help itself understand complex logical relationships, use search engines to find external information, or even execute code for calculations. This approach enables the model to make more reasonable decisions and inferences in highly specialized tasks, similar to doctors or scientists.
In experiments, the Agentic Reasoning model outperformed traditional large language models on some specialized scientific questions, even exceeding the performance of some domain experts. This indicates that with the assistance of external tools, the model can not only enhance its reasoning capabilities but also provide more precise solutions in real-world applications, especially in professional fields like healthcare and finance.
2 Research Background and Motivation
With the continuous advancement of artificial intelligence technology, large language models (LLMs) have achieved significant results in many fields. Particularly in natural language processing, models can fluently generate text, engage in conversations, and complete translations. However, when faced with complex reasoning tasks, existing large language models often have limitations. For instance, traditional large language models struggle with multi-step reasoning problems, often making errors, especially when involving advanced knowledge, specialized fields, or complex logical reasoning.
This limitation has prompted academia and industry to explore ways to enhance the reasoning capabilities of these models. Although many models attempt to improve performance through reinforcement learning, pre-training techniques, and other methods, the lack of effective integration of external tools remains a prominent issue. Therefore, enhancing the reasoning capabilities of models and enabling them to handle high-complexity tasks has become an urgent problem to solve.
The motivation for this research stems from the inadequacies of existing large language models in solving deep reasoning tasks. Although large language models can generate coherent text, they still have certain blind spots when faced with tasks requiring multi-step reasoning, logical analysis, and external knowledge. Traditional solutions mainly rely on the training and optimization of the models themselves, but these methods have not effectively addressed the bottlenecks in model reasoning.
Thus, the authors propose the Agentic Reasoning framework, aiming to enhance the model’s reasoning capabilities by integrating external tools into the reasoning process. The core idea of this framework is to use “tool invocation” (such as mind maps, web searches, code execution, etc.) to help the model better understand complex problems and make more reasonable inferences. In this way, the model can handle specialized tasks in fields like science, medicine, and finance like an expert, and even surpass human experts in some tasks.
3 Main Challenges and Solutions of the Paper
Main Challenges
-
Insufficient Multi-Step Reasoning Ability: Traditional large language models often lack the ability to perform multi-step reasoning when dealing with complex problems. These problems often require the model to involve multiple steps, different logical reasoning, and cross-domain knowledge during the reasoning process. Existing models struggle to effectively handle these tasks without external assistance.
-
Lack of External Knowledge Acquisition Capability: Although large language models have acquired a wealth of knowledge through pre-training, when faced with tasks requiring the latest data or domain-specific information, the model’s internal knowledge base cannot provide sufficient support. To address these issues, models need to be able to access external information in real-time.
-
Reasoning Transparency and Verifiability: The reasoning processes of existing large language models are often black-boxed, lacking transparency and verifiability. This makes it difficult for domain experts to trust the reasoning processes and results of the models when facing specialized tasks.
-
Balancing Reasoning Performance and Computational Resources: As the complexity of problems increases, reasoning performance may decline, while the consumption of computational resources also increases. Ensuring the accuracy of reasoning results under limited computational resources remains a challenge.
Solutions
-
Tool-Augmented Reasoning: To address the multi-step reasoning issue, the Agentic Reasoning framework proposed by the authors integrates external tools (such as mind maps, web searches, computational tools, etc.) to assist the model in multi-step reasoning. During the reasoning process, the model can invoke these tools to obtain the necessary information, thereby better handling complex tasks.
-
Real-Time Retrieval of External Information: To solve the external knowledge acquisition problem, the Agentic Reasoning framework allows the model to invoke tools like search engines during reasoning to retrieve the latest domain-specific information in real-time. This enables the model to acquire timely background knowledge when facing dynamic and time-sensitive tasks, improving reasoning quality.
-
Transparency and Interpretability of Reasoning: By invoking tools, Agentic Reasoning enhances the transparency of the reasoning process. For example, using mind maps can help the model clarify the logical steps of reasoning, making the reasoning process clearer and easier to verify.
-
Reasoning Expansion During Testing: In reasoning tasks, using more tool invocations can improve the accuracy of reasoning. Experiments show that increasing the number of tool invocations on the same problem often leads to more accurate reasoning results. Through this method, the model can automatically select the optimal answer during reasoning, thereby improving overall performance.
4 Research Results
The main contributions and findings of this paper include:
Improved Multi-Step Reasoning Ability: With the introduction of the Agentic Reasoning framework, the model exhibits stronger abilities when faced with multi-step reasoning tasks. Particularly in complex scientific problems, the model can better understand and solve problems with the assistance of tools, even surpassing some existing human experts.
Enhanced External Information Acquisition Capability: By integrating tools (such as web searches), the model can obtain real-time external information during the reasoning process, thereby compensating for the model’s knowledge deficiencies in certain specialized fields.
Increased Interpretability of Reasoning Results: Through tool invocation, the reasoning process becomes more transparent, especially with the use of tools like mind maps, where each step of reasoning is visualized, making the results easier to understand and verify.
Superiority in Deep Research Tasks: In experiments, the Agentic Reasoning model outperformed other existing deep research tools, such as Gemini Deep Research, in deep research tasks in fields like finance, medicine, and law.
5 Core Academic Concepts Discussed in the Paper
-
Agentic Reasoning: A framework that enhances the reasoning capabilities of large language models by integrating external tools. This framework helps models perform better in multi-step reasoning and deep research tasks by introducing tools like mind maps, web searches, and computational analysis.
-
Mind Maps: A visual reasoning tool that helps models understand complex logical relationships, enhancing the accuracy and transparency of reasoning.
-
External Information Retrieval: During the reasoning process, the model obtains real-time external information by invoking tools like search engines to compensate for the model’s knowledge gaps.
-
Multi-Step Reasoning: The process of involving multiple reasoning steps when solving complex problems. Agentic Reasoning supports the model in complex multi-step reasoning through tool invocation.
-
Deep Research: A type of research task that is highly complex and requires extensive background knowledge and expertise. Agentic Reasoning has shown significant advantages in such tasks.
References
https://arxiv.org/pdf/2502.04644
Book Recommendations
Welcome to join the AI Direction Communication Group! Whether you are a researcher, developer, or AI enthusiast, this is an open communication platform for you. Currently, several different direction communication groups have been established (such as Machine Learning/Deep Learning/Natural Language Processing/Computer Vision, etc.). We look forward to discussing, learning, and growing with you!
Long press to identify the QR code below
Reply【AI+Group Direction (e.g., Machine Learning, etc.)】 to contact and join the group
Thank you for your attention and support!
Artificial Intelligence Scientific Research public account focuses on cutting-edge technologies and research dynamics in the AI field, covering popular areas such as machine learning, deep learning, natural language processing, etc., helping you gain an in-depth understanding of the latest developments in artificial intelligence. Welcome everyone to follow!