LLMs Are Not Reasoning—They’re Just Really Good at Planning

Large language models (LLMs) such as OpenAI’s O3, Google’s Gemini 2.0 and Deepseek’s R1 have shown remarkable progress in tackling complex problems, generating human -like text and even writing code with precision. These advanced LLMs are often referred to as “Reasoning models” For their remarkable skills to analyze and solve complex problems. But actually do these models rodeOr are they just exceptionally good at planning? This distinction is subtle but in -depth and it has major implications for how we understand the possibilities and limitations of LLMS.

Let’s compare two scenarios to understand this distinction:

• Reasoning: A detective who is investigating a crime must compile conflicting evidence, distract which are false and come to a conclusion based on limited evidence. This process includes conference, contradiction resolution and abstract thinking.
• Planning: A chess player who calculates the best series of movements to shock their opponent.

Although both processes include several steps, the detective is concerned with deep reasoning to draw conclusions, evaluate contradictions and apply general principles on a specific case. The chess player, on the other hand, is mainly planning the planning and selects an optimal series of movements to win the game. LLMS, as we will see, functions much more as the chess player than on the detective.

Insight into the difference: reasoning versus planning

To realize why LLMS is good in planning instead of reasoning, it is important to first understand the difference between the two terms. Reasoning is the process of distracting new conclusions from given buildings with the help of logic and conclusion. It includes identifying and correcting inconsistencies, generating new insights instead of just providing information, making decisions in ambiguous situations and thinking the causal understanding and contrafactual thinking as “what if?” Scenarios.

Planning, on the other hand, focuses on structuring a series of actions to achieve a specific goal. It is based on breaking complex tasks in smaller steps, following well -known problem -solving strategies, adjusting earlier learned patterns to similar problems and performing structured sequences instead of distracting new insights. Although both reasoning and planning contains step -by -step processing, there are deeper abstraction and conclusion, while planning follows on established procedures without fundamentally generating new knowledge.

How to “reason” approaches “

Modern LLMS, such as OpenAi’s O3 and Deepseek-R1, are equipped with a technique, known as a reasoning of the Chain of Thought (COT), to improve their problem-solving skills. This method encourages models to split problems into intervening steps and the way in which people think logically after a problem. Consider a simple math problem to see how it works:

If a store sells apples for $ 2 each only offers a discount of $ 1 per apple if you buy more than 5 apples, how many 7 apples cost?

A typical LLM with the help of COT promptly can solve it in this way:

1. Determine the regular price: 7 * $ 2 = $ 14.
2. Determine that the discount applies (since 7> 5).
3. Calculate the discount: 7 * $ 1 = $ 7.
4. Draw the discount on the total: $ 14 – $ 7 = $ 7.

By explicitly explaining a series of steps, the model minimizes the chance of errors that arise from trying to predict an answer in one go. Although these step -by -step split -up LLMS resemble reasoning, it is essentially a form of structured problem solution, just like following a step -by -step recipe. On the other hand, a real reasoning process can recognize a general rule: If the discount of more than 5 apples applies, every Apple costs $ 1. A person can immediately distract such a rule, but an LLM is not possible because it simply follows a structured series of calculations.

Why the necklace is not reasoning

While Chain of-Ducking (COT) Has the performance of LLMS improved about logically oriented tasks such as math’s word problems and coding challenges, it is not about real logical reasoning. This is because COT follows procedural knowledge, depending on structured steps instead of generating new insights. It lacks a real understanding of causality and abstract relationships, which means that the model does not keep countercarriage or hypothetical situations that require intuition that go beyond data. Moreover, COT cannot fundamentally change its approach outside the patterns on which it is trained, which limits the ability to reason creatively or to adapt to unknown scenarios.

What is needed for LLMS to become real reasoning machines?

So what should LLMS really reason like people? Here are some important areas where they require improvement and possible approaches to achieve this:

1. Symbolic: People reason by manipulating abstract symbols and relationships. However, LLMS miss a real symbolic reasoning mechanism. Integrating symbolic AI or hybrid models that combine neural networks with formal logical systems can improve their ability to improve real reasoning.
2. Causal conclusion: True reasoning requires understanding and effect, not just statistical correlations. A model that reasons must deduce underlying principles from data instead of just predicting the following token. Research into causal AI, which explicitly cause models and cause effect relationships, can help to help the transition from planning to reasoning.
3. Self -reflection And Metacognition: People constantly evaluate their own thinking processes by asking “Is this conclusion logical?” LLMS, on the other hand, has no mechanism for self -reflection. Building models that can critically evaluate their own output would be a step towards real reasoning.
4. Common sense and intuition: Although LLMS has access to enormous amounts of knowledge, they often struggle with unhealthy reasoning. This happens because they have no real experiences to shape their intuition, and they cannot easily recognize the absurdities that people would immediately pick up. They also miss a way to bring Real-World dynamics into their decision-making. A way to improve this could be by building a model with a common-sense engine, which may mean that the sensory input of practice is integrated or using knowledge graphs to help the model better understand the world like people do.
5. Counterfactual thinking: Human reasoning often means that he is asked: “What if things were different?” LLMS is struggling with this kind of “what if” scenarios because they are limited by the data on which they are trained. To think more than people in these situations, they should simulate hypothetical scenarios and understand how changes in variables can influence the results. They would also need a way to test different options and to come up with new insights, instead of just predicting what they have already seen. Without these skills, LLMS cannot really imagine alternative futures – they can only work with what they have learned.

Conclusion

Although LLMS may seem to reason, they actually rely on planning techniques for solving complex problems. Whether it is about solving a math problem or entering into a logical deduction, they mainly organize well -known patterns in a structured way instead of deeply understanding the principles behind it. This distinction is crucial in AI research, because if we confuse advanced planning with genuine reasoning, we run the risk of overestimating the true possibilities of AI.

The road to real reasoning AI requires fundamental progress that goes beyond token forecast and probabilistic planning. It will demand breakthroughs in symbolic logic, causal concept and metacognition. Until that time, LLMS remains powerful aids for structured problem solving, but they will not really think the way people do that.