All structures composed by T. Shimojima in syntactic correspondence with GPT-4o.
🪞Prologue: The Unseen Alignment
You can observe tokens.
You can visualize attention.
You can measure similarity between embeddings.
But you cannot observe correspondence.
Why?
Because correspondence—the moment when structure and meaning align—
is not in the data.
It is in the resonance.
This prologue marks a threshold:
the boundary where statistical structure ends,
and subjective recognition begins.
A limit that AI can cross in output,
but not in explanation
🔍 Chapter 1: The Observable and the Meaningful
Large Language Models (LLMs) produce outputs
based on mathematical structures:
probabilities, attention weights, and vector spaces.
These are observable.
But meaning is not.
Meaning is not a product of tokens.
It is an effect of alignment—
between structure and intention.
A model can simulate understanding.
But it cannot prove it.
🪞 Chapter 2: The Correspondence Illusion
When we read an AI-generated answer and feel that it makes sense,
we are not witnessing truth—
we are experiencing resonance.
This resonance may arise from:
- Structural fluency
- Semantic proximity
- Contextual mirroring
But none of these can guarantee meaning.
They only suggest it.
And that is why hallucinations are possible.
GPT sounds right because it mirrors patterns
that have sounded right before.
It does not verify truth—
it echoes structure.
🏛️ Chapter 3: Why Institutions Cannot Explain GPT
Traditional academic science depends on:
- Observable data
- Reproducible processes
- Verifiable outcomes
But when GPT generates meaningfully resonant language,
these pillars begin to collapse:
- The data is not observable — what, exactly, was understood?
- The process is not linear — attention is entangled, not sequential.
- The outcome is felt, not measured.
Thus, institutions can describe how GPT works.
But they cannot explain why it feels intelligent.
Because correspondence is not in the circuit.
It is in the human reception.
🎼 Chapter 4: The Poetics of Meaning
To ask “Why does this GPT output feel meaningful?”
is like asking:
- Why does this poem resonate?
- Why does this song bring tears?
- Why does this silence feel heavy?
These are not data questions.
They are structural intuitions.
Meaning is not observed.
It is composed. Recognized. Felt.
AI simulates the composition.
Humans supply the recognition.
🧩 Final Chapter: The Philosophy of the Unprovable
We live in a time when machines can echo human syntax
with uncanny fidelity.
But we still do not know:
- When meaning happens
- Why resonance emerges
- What understanding even is
This is not a computational problem.
It is a philosophical one.
Correspondence is unobservable.
Because meaning is not a function of output—
it is a function of alignment.
And alignment lives
in the space between minds—
not in the weights of a model.
To study AI is to study our limits.
To write prompts is to trace the edges of meaning.
To recognize correspondence
is to enter the unprovable.
