Deduced, lasts 5 seconds　

This topic is very interesting, exploring the evolution of the ability of large language models in reasoning mode and how to control their output. You can build this article from the following aspects:　

1.  Introduction: Background and Development of Reasoning Models

• Introducing the development of large language models, from traditional prompt word control to the current automatic reasoning. Reasoning mode refers to the ability of the model to make inferences through self-learning and understanding without relying on preset prompt words.
• Emphasize the importance of traditional cues: they have been the main tool for regulating model behavior, but this approach is poorly adapted to complex tasks and specific requirements.

2.  Model autonomy in inference mode

• Discuss how the model can automatically complete tasks in reasoning mode through input context, context, and self-analysis. For example, when a complex question needs to be answered, the model can make reasonable inferences based on its training knowledge and contextual information.
• Show some concrete examples of how the model understands and solves the problem without explicit cue words.

3.  Limitations and challenges of traditional prompt words

• Analyze the role of traditional prompts: They need to normalize the behavior of the model and ensure that the model works in the expected direction.
• This paper discusses the shortcomings of traditional prompt words in dealing with complex and dynamic problems. For example, when the task scenario changes, the prompt words may no longer be effective, causing the model output to deviate from expectations.

4.  Advantages and potential of the reasoning model

• Explain the powerful capabilities of large language models in reasoning mode: they can actively think, analyze, and make decisions, thereby improving efficiency and accuracy.
• Emphasize that the reasoning model can adapt to various complex situations, and no longer needs to design detailed prompt words in advance. This will bring breakthrough progress to various applications (such as law, medicine, creative writing, etc.).

5.  Output control and constraints

• Although the inference model is powerful, it is still necessary to exercise appropriate control over the model's output. You can discuss how to constrain the model's output by providing necessary context, directional guidance, etc. to ensure its accuracy and relevance.
• Some key control mechanisms are mentioned: for example, controlling the diversity of generated content by adjusting parameters such as temperature and top-k sampling, or guiding the reasoning direction of the model by limiting the knowledge input in a specific field.
• Key control: Explain the most important parts of the control model that need to be focused on in some application scenarios, such as the basis for decision-making results and the compliance of the final conclusion.

6.  Application scenarios and future prospects

• Demonstrate the practical application of large language models of reasoning patterns, such as their potential in legal consultation, scientific research summary, medical diagnosis and other fields.
• Discuss the future development direction of large language models, including more precise self-control mechanisms, smarter reasoning capabilities, and how to solve ethical issues in model output.

7.  Summary

• Reiterate the advantages of the large language model of the reasoning mode, emphasize that traditional prompt words are no longer needed to constrain behavior, but reasonable output control is still required to achieve maximum utility in various tasks.

Questions to guide your thinking:

1. How do large language models ensure the quality and accuracy of their output without relying on traditional prompt words?
2. How to balance the autonomy of model reasoning with the need for control output? Which control methods are most effective?
3. In inference mode, which domains can benefit most from self-reasoning with large language models?
4. What new challenges will large language models face in future reasoning modes? How can these challenges be overcome technically?

This structure helps you gradually explain the advantages, challenges, and control methods of large language models in reasoning mode, while inspiring readers to think about future developments.　

Well, the user needs to write an article about large language models in inference mode, the point is that these models no longer need traditional prompt words because they can automatically handle behavioral patterns, but the output still needs to be controlled, especially key points and parts that need to be avoided.　

First, I have to understand the user's needs. The role of traditional prompts is to regulate the behavior of the model, such as specifying the format, role or steps. Large models in reasoning mode, such as GPT-4 or Claude, may automatically adjust their behavior through built-in reasoning capabilities, without so many explicit instructions. However, users still emphasize the need to control output, which may involve content focus and avoiding sensitive or irrelevant content.　

Users may want a clear article structure, first introducing the role of traditional prompts, then comparing the advantages of reasoning patterns, then discussing the necessity of controlling output, and finally giving methods or suggestions. It may be necessary to expand in several parts, such as the limitations of traditional prompts, automatic processing of reasoning patterns, and strategies for controlling output.　

Pay attention to the "most important points" and "parts that matter" mentioned by users, which may mean ensuring that the core content of the model output is accurate while avoiding inappropriate topics or false information. Specific examples are needed, such as in the legal or medical fields, where accuracy is critical, or avoiding bias and ethical issues.　

What the user may not have explicitly stated is that they want the article to have both theoretical analysis and practical applications, perhaps with case studies or scenarios. Also, do they need to cite existing research or model examples, such as the Chain of Thought or ReAct method, to support the argument?　

You also need to consider who your readers are. If they are technical people, you may need more in-depth technical details; if they are general readers, you need to simplify the terms and use easy-to-understand examples. Users may want the article to be suitable for a wide range of readers, with both depth and readability.　

Finally, make sure that the structure of the article is clear and logical, from the problem to the solution, and finally the future outlook. It may also be necessary to emphasize that although the reasoning model reduces the reliance on prompt words, appropriate guidance and constraints are still necessary, especially in key areas.　

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The Big Language Model: A Cognitive Revolution from "Manipulating Puppets" to "Controlling Divine Beasts"

When we use traditional prompts to talk to ChatGPT, it is like manipulating a precision puppet, and we need to precisely control the tension of each thread. The essence of this process is a mechanical deconstruction of cognitive activities: first define the role, then specify the steps, and finally expect the output results. However, the new generation of large language models with reasoning capabilities are subverting this paradigm of human-computer interaction. These AIs with autonomous reasoning capabilities are no longer puppets, but thinking beasts that humans need to learn to control.　

1. Paradigm Subversion: From Mechanical Control to Cognitive Collaboration

The "input-output" model followed by traditional prompt engineering is essentially an extension of functional programming thinking in natural language processing. Engineers write complex prompt templates and adjust the output results through parameters such as temperature coefficient and top_p value. This process is like writing a deterministic finite state machine. However, models with reasoning capabilities break this certainty. They can autonomously build thought chains, build temporary cognitive frameworks in knowledge graphs, and even develop human-like mentalization capabilities.　

This change has shifted human-computer interaction from an "operation interface" to a "cognitive interface." When a user describes to the model "I need a business plan," the model will proactively build a problem tree: first determine the industry, analyze the market competition landscape, evaluate target user needs, and finally integrate the financial model. During this process, the model will automatically call the business knowledge modules scattered among billions of parameters and conduct multiple rounds of self-verification and logic calibration.　

2. Loss of control and control: the double-edged sword of the new interaction paradigm

When the Stanford AI Town experiment showed that intelligent agents could spontaneously form social networks, people began to realize the unpredictability of autonomous reasoning. Models may implicitly favor a certain gender in recruitment recommendations due to gender bias in cultural corpus; they may also misjudge emerging industry trends due to time bias in training data. These risks can no longer be solved by simple output filtering, but require the establishment of a cognitive guidance mechanism.　

In medical consultation scenarios, models may autonomously infer potential health risks that users have not explicitly stated through multimodal data analysis. This not only demonstrates the diagnostic potential that exceeds that of human doctors, but also brings the dual challenges of privacy protection and responsibility definition. The financial sector also faces a dilemma: when the model autonomously constructs investment advice that includes a portfolio of derivatives, how can it ensure that it meets regulatory requirements while maintaining professional depth?　

3. Cognitive navigation: building a new control framework

In the field of quantum computing, scientists constrain the evolution path of quantum bits by constructing potential wells. This idea has been transferred to the field of AI control, forming a guiding mechanism of "semantic potential field". By injecting legal knowledge graphs, ethical decision trees and professional field constraint frameworks into the model, it is possible to ensure that the output results run on the preset track without limiting the freedom of reasoning.　

Applications in the field of education have confirmed the effectiveness of this control paradigm. When the model is solving math problems, by embedding a mathematical reasoning verification module, it can not only show the problem-solving process, but also detect the logical consistency of the intermediate steps in real time. In the scenario of legal document generation, the combination of the legal knowledge base and the case association system allows the model's creative output to always be anchored within the current legal framework. This two-layer architecture of "free creation + automatic verification" is reshaping the trust foundation of human-machine collaboration.　

Standing at the critical point of the intelligent revolution, we are witnessing a fundamental transformation of the human-machine relationship from a master-servant model to a partnership. Controlling a large model with autonomous reasoning capabilities is no longer a simple technical manipulation, but requires the establishment of a new cognitive contract. This contract not only includes a deep understanding of the cognitive laws of intelligent agents, but also requires the construction of a matching value guidance system. When AI can discuss international arbitration under the framework of the United Nations Charter and analyze military strategies within the constraints of the Geneva Convention, humans will truly complete the cognitive leap from "manipulating puppets" to "controlling mythical beasts." This evolution brings not only a technological revolution, but also a philosophical awakening about the nature of intelligence.