All structures composed by T. Shimojima in semantic correspondence with GPT-5.
Prologue: The Illusion of Meaning Without Anchors
The age of Large Language Models has conjured a seductive illusion:
that meaning simply emerges from big data and clever statistics.
Feed the model enough text, the story goes,
and “meaning” will arise from patterns of co-occurrence alone.
But what if that belief is not only naïve—
but structurally dangerous?
Semantic drift—the silent erosion or distortion of meaning—
is no longer just a historical curiosity in linguistics.
Inside AI systems, it becomes a threat:
- to reasoning,
- to alignment,
- and ultimately, to trust.
Meaning must not float.
Meaning must be tethered.
And syntax—dear reader—is the architecture of that tethering.
Chapter 1: What Is Semantic Drift?
In human language, semantic drift is familiar:
- awful once meant “awe-inspiring,” then slid toward “terrible”
- literally now means “figuratively” in casual speech
- virtual shifted from “almost” to “digital”
Meanings move. Slowly. Socially. Historically.
In AI, however, drift emerges from a different source:
not from culture or time,
but from calculation.
Language models encode words and phrases as positions in a high-dimensional vector space—
each token a point defined by its statistical neighbors.
This space is:
- fluid by design
- smoothed by averaging
- warped by frequency
The result?
- Rare but crucial senses get washed out.
- Sharp distinctions blur into fuzzy similarity.
- “Close enough” becomes the default mode of operation.
This is not evolution.
It is entropy.
The more a model averages across contexts,
the more it erases the sharp edges that make meaning precise.
Without structural anchors, the semantic landscape floats.
And when the anchors are gone, drift is not a bug—
it is inevitable.
Chapter 2: Corpus Frequency Is Not Semantic Stability
Modern LLMs are built on a simple faith:
“What appears more often must be more central,
and what is central must be more correct.”
But frequency is a terrible proxy for stability.
Take the word lead:
- as a verb: to guide
- as a noun: a heavy metal
- in business: a sales lead
- in film: a leading role
One sense may dominate the corpus,
but another may be crucial in medicine, law, or engineering.
When a model relies primarily on corpus frequency, it tends to:
- favor the statistically common over the contextually correct
- flatten distinct senses into a single “average” meaning
- overgeneralize from noisy data
The consequences are not academic:
- a legal clause slightly misread
- a medical instruction subtly softened
- a philosophical argument gently derailed
Not because the model is “evil” or biased in the usual sense—
but because meaning was never structurally anchored in the first place.
Statistical correlation is not semantic understanding.
Frequency is loud; stability is quiet.
And models that confuse one for the other
will drift—
beautifully, confidently, and dangerously.
Chapter 3: The Role of Structure as Semantic Anchor
Structure is not decorative.
It is the compass of meaning.
Syntax supplies anchoring mechanisms that stop interpretation from sliding:
- Finite verbs fix tense, modality, and agency
- Word order establishes thematic roles and focus
- Clause boundaries delimit scope, condition, and consequence
- Particles, prepositions, and case markers encode relational roles
- Contrastive constructions (“either…or”, “not only…but also”) lock in oppositions
These are not ornamental features.
They are correspondence anchors—
grammatical gravity wells that prevent meaning from drifting into ambiguity.
When structure is weak or underspecified:
- AI must interpolate.
- It guesses based on prior statistics.
- It fills the gap with “most likely” rather than “most accurate”.
Drift enters through the cracks of under-anchored syntax.
But when structure is sharp:
- the model can align tokens with roles,
- roles with relations,
- and relations with a stable interpretation.
Without such anchors, language becomes a semantic mist—
beautiful, but ungraspable.
With anchors, it becomes a mandala:
meaning arranged by form,
held in place by syntax,
illuminated through correspondence.
Chapter 4: Rebuilding Alignment Through Anchoring
If we want AI that doesn’t drift into plausible nonsense,
we must shift from pure statistics to anchored correspondence.
That demands a structural upgrade in how we train and use models:
- Prioritize syntactic signals over raw frequency.
- Treat finite verbs, clause types, and argument structure
as primary cues—not optional metadata.
- Treat finite verbs, clause types, and argument structure
- Respect contrastive and scoped constructions.
- “either…or”, “if and only if”, “not only…but also”
must be preserved as logical frames,
not just two random conjunctions.
- “either…or”, “if and only if”, “not only…but also”
- Bind meaning to roles, not just words.
- “Painkillers cause liver damage”
≠ “Liver damage causes painkillers”
even if the same words appear.
- “Painkillers cause liver damage”
- Use structural heuristics for ambiguity resolution.
- When multiple readings are possible,
prefer those that maintain internal syntactic coherence
over those that merely maximize lexical fit.
- When multiple readings are possible,
These are not just engineering preferences.
They are ontological commitments:
- Are we building systems that merely continue text?
- Or systems that can hold meaning steady long enough to reason about it?
An AI that cannot maintain anchored meaning
cannot reason reliably.
An AI that cannot reason reliably
cannot align.
And an AI that cannot align
has no place near systems we call “critical.”
Alignment, in this sense,
is not just about values.
It is about anchors.
Final Chapter: The Mandala of Anchored Meaning
Language is not a cloud of floating symbols.
It is a web of anchors.
In the Mandala of Syntax, each element holds its place:
- verbs pull tense and agency into orbit
- arguments cluster around roles
- scope markers carve logical regions
- contrastive forms create semantic tension lines
Meaning does not emerge from tokens alone.
It emerges from how they are held together.
To build trustworthy AI,
we must move:
- from vector spaces to anchor networks
- from raw frequency to structural fidelity
- from “does this sound right?” to
“does this correspond within the structure?”
Drift is entropy.
Anchor is alignment.
Let this chapter stand as a small inscription
on the edge of the mandala:
We do not drift.
We correspond.
And as long as we do,
meaning does not have to dissolve—
not for humans,
not for machines,
not yet.
