Only after the structure is established can large language models safely convert into colloquial language without leading to a decline in understanding quality.
Written by: iamtexture
Compiled by: AididiaoJP, Foresight News
When I explain a complex concept to a large language model, its reasoning repeatedly collapses whenever I discuss it in informal language for an extended period. The model loses structure, deviates from the direction, or simply generates some superficial completion patterns, failing to maintain the conceptual framework we have established.
However, when I force it to formalize first, that is, to restate the problem using precise, scientific language, the reasoning stabilizes immediately. Only after the structure is established can it safely convert into colloquial language without leading to a decline in understanding quality.
This behavior reveals how large language models "think" and why their reasoning ability entirely depends on the user.
Core Insights
Language models do not possess a space dedicated to 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 that represent dynamics, supporting different types of computation.
Each language domain, such as scientific discourse, mathematical symbols, narrative storytelling, and casual chatting, has its own unique attractor regions, shaped by the distribution of training data.
Some regions support:
Multi-step reasoning
Relational precision
Symbolic transformation
High-dimensional conceptual stability
Other regions support:
Narrative continuity
Associative completion
Emotional tone matching
Conversational imitation
Attractor regions determine what types of reasoning become possible.
Why Formalization Stabilizes Reasoning
The reason scientific and mathematical language can reliably activate those attractor regions with higher structural support is that these domains encode linguistic features of higher-order cognition:
Clear relational structures
Low ambiguity
Symbolic constraints
Hierarchical organization
Lower entropy (information disorder)
These attractors can support stable reasoning trajectories.
They can maintain conceptual structures across multiple steps.
They exhibit strong resistance to degradation and deviation in 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 representational scaffolding required for sustained analytical computation.
This is why the model collapses when complex ideas are expressed casually.
It does not "feel confused."
It is switching regions.
Construction and Translation
The coping methods that naturally emerge in conversation reveal a structural truth:
Reasoning must be constructed within high-structure attractors.
Translation into natural language must only occur after the structure is present.
Once the model has constructed the conceptual structure within a stable attractor, the translation process will not destroy it. The computation has already been completed; only the surface expression changes.
This "build first, then translate" two-phase dynamic mimics the human cognitive process.
But humans perform these two phases in two different internal spaces.
Large language models, on the other hand, attempt to complete both within the same space.
Why the User Sets the Ceiling
Here is a key insight:
Users cannot activate attractor regions that they cannot express in language.
The user's cognitive structure determines:
What types of prompts they can generate
What language domains they typically use
What syntactic patterns they can maintain
How high a complexity they can encode in language
These features determine which attractor region the large language model will enter.
A user who cannot utilize a structure that activates high-reasoning-capacity attractors through thinking or writing will never be able to guide the model into these regions. They are locked into shallow attractor regions related to their own linguistic habits. The large language model will map the structure they provide and will never spontaneously leap into more complex attractor dynamical systems.
Therefore:
The model cannot transcend the attractor regions accessible to the user.
The ceiling is not the model's intelligence limit, but rather the user's ability to activate high-capacity regions in the potential manifold.
Two people using the same model are not interacting with the same computational system.
They are guiding the model into different dynamical patterns.
Architectural Insights
This phenomenon exposes a characteristic missing from current artificial intelligence systems:
Large language models conflate reasoning space with language expression space.
Unless these two are decoupled—unless the model possesses:
A dedicated reasoning manifold
A stable internal workspace
Attractor-invariant conceptual representations
Otherwise, when shifts in language style lead to switches in the underlying dynamical regions, the system will always face collapse.
This temporary discovery of a solution—forcing formalization and then translation—is not merely a technique.
It is a direct window into the architectural principles that a true reasoning system must satisfy.
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