Large language models (LLMS) have developed significantly. Now, as a simple text generation and translation tools, the research, decision -making and complex problem solutions. The key factor of this transformation is that LLM has more and more systematic ability to think about multiple possibilities and dynamic refinement through decomposition problems. These models can now perform structured reasoning, not just prediction of the next word in order, so that they can handle complex tasks more effectively. Leading models such as OPENAI O3, Google’s Gemini and Deepseek R1 have integrated these functions to enhance their ability to process and analyze information.
Understand simulation thinking
Human beings naturally analyze different choices before making decisions. Whether it is a plan for vacation or problem solving, we often simulate different plans in our minds to evaluate various factors, weigh the advantages and disadvantages, and adjust our choices accordingly. Researchers are integrating this ability to enhance their reasoning capabilities. Here, simulation thinking refers to the ability of LLM to perform system reasoning before generating answers. This is the opposite of simply retrieval from the stored data. A useful analogy is to solve mathematical problems:
- Basic AI may recognize mode and quickly generate answers without verification.
- Ai using analog reasoning can work through steps, check errors and confirm its logic before response.
Thought chain: Teach AI thinking steps
If LLM must execute simulation thinking like humans, you must be able to break down complex problems into smaller sequential steps. This is where the thinking chain (COT) technology plays a vital role.
COT is a method of prompt, which can guide LLM to solve the problem in an orderly manner. This structured reasoning process does not reach the conclusion, but that LLMS can divide complex problems into simpler, easy -to -manage steps, and gradually solve them.
For example, when solving words in mathematics:
- Basic AI may try to match the problem with the examples that I have seen before and provide answers.
- Using imaginative reasoning AI will overlook each step to logically calculate the work before reaching the final solution.
This method is valid for logic deductions, multiple steps solutions, and contextual understanding. Although the earlier models require the reasoning chain provided by people, advanced LLMs such as OPENAI’s O3 and Deepseek R1 can adapt to the application and apply COT reasoning.
How does the leading LLM implement analog thinking
Different LLMs are using simulation thinking in different ways. The following are OPENAI’s O3, Google DeepMind model, and DEEPSEEK-R1 to perform analog thinking and their respective advantages and limitations.
Openai O3: Think like chess players in advance
Although the exact details of the OPNAI model of OPENAI have not been made public, researchers believe that it uses technology similar to Monte Carlo Tree Search (MCTS), which is a strategy used in AI -driven games such as AlphaGo. Just like international chess that analyzes multiple actions before deciding, O3 explores different solutions, evaluate its quality and select the most promising solution.
Unlike early models that rely on mode recognition, O3 uses COT technology to actively generate and improve the reasoning path. During the inference period, it executes other calculation steps to build multiple inference chains. It is then evaluated by the evaluation device model. This is a reward model aims to ensure logical coherence and correctness. The final response was selected according to the scoring mechanism to provide good output.
O3 follows the multi -step process of structured. Initially, it fine -tuned it in the data set of a large number of human reasoning chains and internalized logical thinking patterns. During the inference, it generate multiple solutions to give a given problem, rank them according to correctness and coherent, and improve the best solution when needed. Although this method allows O3 to self-correct and improve accuracy before response, weighing is to calculate cost-exploring multiple possibilities requires a lot of processing capabilities to make it slower and more resources. However, O3 performed well in dynamic analysis and problem solving, positioning it in the most advanced AI model today.
Google DeepMind: Refined answers like editors
DeepMind has developed a new method called “thinking evolution”, which regards reasoning as a refined process. This model does not analyze a variety of scenes in the future, but is more like a draft of various papers of the editor. This model produces several possible answers to evaluate its quality and improve the best answer.
Inspired by genetic algorithms, this process ensures high -quality reactions through iteration. Structural tasks such as logical problems and programming challenges are particularly effective, and the best answer is determined by clear standards.
However, this method is limited. Because it relies on the external scoring system to evaluate the quality of response, it may struggle in abstract reasoning without a clear or wrong answer. Unlike O3, the DEEPMIND model focuses on improving the existing answers, which reduces its flexibility on open issues.
Deepseek-r1: Learn to reason like a student
DeepSeek-R1 uses a enhanced learning method to enable it to move the development reasoning ability over time, rather than evaluate multiple responses in real time. DeepSeek-R1 does not depend on the reasoning data of pre-forming, but instead receives the problem and receives feedback and iteration. This is similar. For example, how students can improve the skills to solve problems by practicing.
This model follows a structured enhancement learning cycle. It starts with basic models (such as DeepSeek-V3) and prompts to gradually solve mathematical problems. To verify each answer by direct code execution, bypassing the needs of other models that need to verify the correctness. If the solution is correct, the reward model will be rewarded; if it is not correct, it will be punished. This process is widely repeated, so that DeepSeek-R1 can improve its logical reasoning skills, and prioritize more complicated issues over time.
The key advantage of this method is efficiency. Unlike O3, which is widely reasonable in reasoning time, DeepSeek-R1 embeds the reasoning capabilities during the training process to make it faster and more cost-effective. It is highly scalable because it does not require a large number of labeled data sets or expensive verification models.
However, this method based on enhanced learning is weighing. Because it depends on the task with verified results, it is good at mathematics and coding. Nonetheless, it may still be difficult to solve the abstract reasoning in the law, morality, or creativity. Although mathematical reasoning may be transferred to other fields, its broader applicability is still uncertain.
table: O3’s O3, comparison between the thought evolution of DeepMind and the R1 of Deepseek
AI reasoning future
Simulation reasoning is an important step for AI to be more reliable and smart. With the development of these models, the focus will be changed from simply generating text to a strong ability to solve problems that are very similar to human thinking. Future progress may be concentrated in the production of AI models that can identify and correct errors, integrate it with external tools to verify the response, and identify uncertainty when facing ambiguous information. However, a key challenge is to balance the depth of reasoning and computing efficiency. The ultimate goal is to develop AI systems with thoughtful response to ensure accuracy and reliability, just like human experts carefully assess each decision before taking action.
