Only after the structure is established can the large language model be safely converted into colloquial language without causing a decline in the quality of comprehension.
Article author and translator: iamtexture, AididiaoJP
Article source: Foresight News
When I explain a complex concept to a large language model, its reasoning repeatedly breaks down whenever informal language is used for extended discussions. The model loses structure, goes off track, or simply generates superficial completion patterns instead of maintaining the conceptual framework we have established.
However, when I insist that it be formalized first—that is, restated in precise, scientific language—the reasoning immediately stabilizes. Only after the structure is established can it be safely translated into plain language without compromising the quality of understanding.
This behavior reveals how large language models "think" and why their reasoning ability depends entirely on the user.
Key Insights
Language models do not have a dedicated space for reasoning.
They operate entirely within a continuous flow of language.
Within this language flow, different language patterns reliably lead to different attractor regions. These regions are stable states representing the dynamics and supporting different types of computation.
Every language register, such as scientific discourse, mathematical symbols, narrative stories, and casual conversation, has its own unique attractor regions, the shape of which is shaped by the distribution of training data.
Some regions support:
Multi-step reasoning
Relationship accuracy
Symbol conversion
High-dimensional concept stability
Other regions support:
Narrative Continuation
Lenovo completion
Emotional tone matching
Dialogue Imitation
The attractor region determines what types of reasoning are possible.
Why can formalization ensure stable reasoning?
The reason why scientific and mathematical language can reliably activate attractor regions with higher structural support is that these registers encode linguistic features of higher-order cognition:
explicit relationship structure
Low ambiguity
Symbolic constraints
Hierarchical organization
Lower entropy (degree of information disorder)
These attractors can support stable inference trajectories.
They can maintain the conceptual structure across multiple steps.
They exhibit strong resistance to the degradation and deviation of reasoning.
In contrast, attractors for informal language activation are optimized for social fluency and associative coherence, not for structured reasoning. These areas lack the representational scaffolding required for sustained analytical computation.
This is why models break down when complex ideas are expressed in a casual way.
It is not "confused".
It is switching regions.
Construction and Translation
The coping strategies that naturally emerge during the dialogue reveal a structural truth:
Reasoning must be constructed within a highly structured attractor.
Translation into natural language must occur only after the structure exists.
Once the model has established its conceptual structure within a stable attractor, the translation process will not destroy it. The computation is already complete; only the surface representation changes.
This two-stage dynamic of "building first, then translating" mimics the human cognitive process.
However, humans perform these two phases in two different internal spaces.
Large language models attempt to accomplish both within the same space.
Why is it that users set the ceiling?
Here is a key takeaway:
Users are unable to activate attractive areas that they cannot express in words.
The user's cognitive structure determines:
What types of prompts can they generate?
Which registers do they typically use?
What syntactic patterns can they maintain?
How much complexity can they encode using language?
These features determine which attractor region a large language model will enter.
A user who cannot utilize structures that activate high-level attractors through thinking or writing will never be able to guide the model into these areas. They are locked into shallow attractor regions related to their own language habits. Large language models will map the structures they provide and will never spontaneously leap to more complex attractor dynamic systems.
therefore:
The model cannot go beyond the user-accessible attractor area.
The ceiling is not the upper limit of the model's intelligence, but rather the user's ability to activate high-capacity regions in a potential manifold.
Two people using the same data type are not interacting with the same computing system.
They are guiding the model to different dynamic modes.
Architectural insights
This phenomenon exposes a missing characteristic in current artificial intelligence systems:
Large language models conflate the reasoning space with the language expression space.
Unless these two are decoupled—unless the model has:
A dedicated inference manifold
A stable internal workspace
Conceptual representation of attractor invariance
Otherwise, the system will always face crashes when a change in language style causes a switch in the underlying dynamics region.
This impromptu solution, which involves forcing formalization and then translating, is more than just a technique.
It is a direct window that allows us to glimpse the architectural principles that a true reasoning system must satisfy.
