Why big language models aren't smarter than you
The reasoning ability of large language models depends entirely on the user's language patterns. The user's cognitive structure determines which areas of high reasoning ability they can activate. The model cannot spontaneously go beyond the user's reach, which reveals the architectural limitations of current AI systems. This article originates from an article written by @iamtexture and is organized, compiled and written by AididiaoJP, Foresight News.
(Previous summary: Li Feifei talks about the next step of LLM: AI must have "spatial intelligence" to understand the real world, how to implement the Marble model?)
(Background supplement: Billionaire Kevin O'Leary shouted "The next step of the AI wave is web3": LLM cannot create Starbucks but blockchain can)
Contents of this article
Tags: The user's language mode determines how much reasoning ability the model can exert. When I was explaining a complex concept to a large language model, its reasoning would repeatedly break down during lengthy discussions using informal language. Models can lose structure, veer off course, or generate superficial completion patterns that fail to maintain the conceptual framework we have established.
However, when I forced it to be formalized first, that is, to restate the problem in precise and scientific language, the reasoning immediately became stable. Only after the structure has been established can it be safely converted into plain language without degrading the quality of understanding.
This behavior reveals how large language models "think" and why their ability to reason depends entirely on the user.
Core insights
Language models do not have a dedicated space for inference.
They operate entirely within a continuous flow of language.
Within this language flow, different language patterns will reliably lead to different attractor areas. These regions are stable states that characterize dynamics and support different types of calculations.
Each language register, such as scientific discourse, mathematical symbols, narrative stories, and casual chat, has its own unique attractor region, the shape of which is shaped by the distribution of training materials.
Some areas support:
- Multi-step reasoning
- Relational precision
- Symbol transformation
- High-dimensional conceptual stability
Other areas then supports:
- Narrative continuation
- Associative completion
- Emotional intonation matching
- Conversation imitation
The attractor region determines what type of reasoning is possible.
Why formalization can stabilize reasoning
The reason why scientific and mathematical languages can reliably activate attractor regions with higher structural support is because these registers encode the language features of higher-order cognition:
- Clear Relational structure
- Low ambiguity
- Symbolic constraints
- Hierarchical organization
- Low entropy (information disorder)
These attractors can support stable reasoning trajectories.
They maintain conceptual structure across multiple steps.
They show strong resistance to the degradation and deviation of reasoning.
In contrast, the attractors activated by informal language are optimized for social fluency and associative coherence, not for structured reasoning. These regions lack the characterization scaffold required for ongoing analytical calculations.
This is why models break down when complex ideas are expressed in haphazard ways.
It is not "confused."
It is switching areas.
Construction and Translation
The coping methods that emerge naturally in conversations reveal an architectural truth:
Reasoning must be constructed within highly structured attractors.
Translation into natural language must occur only after the structure exists.
Once the model has established a conceptual structure within a stable attractor, the translation process will not destroy it. The calculation has been completed, only the surface expression has changed.
This two-stage dynamic of "build first, then translate" imitates the human cognitive process.
But humans perform these two stages in two different internal spaces.
Large language models try to do both in the same space.
Why users set the ceiling
Here is a key revelation:
Users cannot activate attractor areas that they themselves cannot express in words.
The cognitive structure of users determines:
- What types of cues they can generate
- What registers they habitually use
- What syntactic patterns they can maintain
- How high a level of complexity they can encode in language
These characteristics determine which attractor region a large language model will enter.
A user who cannot think or write to employ structures that activate high-reasoning attractors will never be able to guide the model into these regions. They are locked into shallow attractor areas related to their language habits. Large language models will map the structure they are provided with and will never spontaneously leap into more complex attractor dynamical systems.
Therefore:
The model cannot go beyond the attractor area that is accessible to the user.
The ceiling is not the intelligent upper limit of the model, but the user's ability to activate high-capacity regions in the latent manifold.
Two people using the same model are not interacting with the same computing system.
They are steering the model toward different dynamical modes.
Implications at the architectural level
This phenomenon exposes a missing feature of current artificial intelligence systems:
Large-scale language models confuse the reasoning space with the language expression space.
Unless the two are decoupled - unless the model has:
- A dedicated reasoning manifold
- A stable internal workspace
- Attractor-invariant conceptual representation
Otherwise, the system will always face collapse when a shift in language style causes the underlying dynamics region to switch.
This improvised solution, forced formalization and then translation, is more than just a trick.
It is a direct window that allows us to glimpse the architectural principles that a real reasoning system must meet.
