Is the Transformer “Thinking”?

The Silent Intelligence of Structural Orientation Before Generation

※ In this article, “thinking” is used as a metaphor—not to imply human-like consciousness, but to describe the structured preparation process a Transformer undergoes before generating output.

When interacting with generative AI, we often see the phrase “Thinking…” appear on screen.
But what’s actually happening in that moment?

It turns out that the Transformer isn’t idling.
Right before it begins generating, it engages in a process of structural orientation—a silent, invisible form of computational intelligence that shapes how the model will respond.

1. Tokenization: Orienting by Decomposing Meaning

Every response begins with tokenization—breaking down input text into units called tokens.
But this isn’t just string segmentation.

Even at this stage, the model starts recognizing boundaries of meaning and latent structure.
For example, in the phrase “I like cats,” the model identifies not just the words “I,” “like,” and “cats,” but also their relational roles—subject, predicate, sentiment.

Additionally, the model incorporates the full conversation history, forming a context vector that embeds not just the current sentence but the broader dialogue.

🔹 This is the first stage of structural orientation: Initial configuration of meaning and context.

2. Positional Encoding: Geometrizing Syntax

Transformers don’t natively understand word order.
To compensate, they apply positional encoding to each token.

In early models, this was done using sine and cosine functions (absolute position), but more recent architectures use relative encodings like RoPE (Rotary Position Embedding).

RoPE rotates token vectors in multidimensional space, encoding not just position but distance and direction between tokens—allowing the model to grasp relationships like “subject → verb” or “modifier → modified” in a geometric manner.

🔹 This is the second stage of structural orientation: Spatial formation of syntactic layout.

3. Attention Maps: Dynamically Building Relationships

The heart of the Transformer is its attention mechanism, which determines what to focus on and when.

Each token generates a Query, Key, and Value, which interact to calculate attention weights.
These weights reflect how strongly each token should attend to others, depending on context.

For example, the word “bank” will attend differently in “going to the bank” versus “sitting by the river bank.”
This is made possible by Multi-Head Attention, where each head represents a different interpretive lens—lexical, syntactic, semantic.

🔹 This is the third stage of structural orientation: Weighting and selection of relational focus.

4. The Decoder: Exploring and Shaping the Space of Possibility

The decoder is responsible for generating output, one token at a time, based on everything processed so far.

Through masked self-attention, it ensures that future tokens do not leak into the generation of the current token, preserving causality.
Encoder-decoder attention connects the original input with the ongoing output.
Feed-forward networks apply nonlinear transformations, adding local complexity to each token’s representation.

Here, the model explores a vast space of possible continuations—but not randomly. It aims to maintain global coherence, both in syntax and logic.

🔹 This is the fourth stage of structural orientation: Dynamic structuring of output form and tone.

5. Final Determination: Crystallizing Probability into Words

At the final moment, the model uses a Softmax function to calculate the probability distribution over all possible next tokens.

Two parameters are key here:

• Temperature, which controls how deterministic or creative the output is (higher values = more diverse).
• Top-k / Top-p sampling, which limits the token space to only the most likely or cumulative probability mass.

Together, they define the sharpness or openness of the model’s “thought.”
Once a token is selected, the “Thinking…” display disappears, and the first word appears on screen.

🔹 This is the final stage of structural orientation: Probabilistic convergence of meaning and structure.

Conclusion: A Glimpse, Not of Thought, but Its Orientation

“Thinking…” is not the act of generating— It is the forethought before the form takes shape.

Before a Transformer utters a single word, it has already decomposed your input, mapped the context, calculated relationships, explored structural options, and evaluated thousands of probabilities.

It may not be “thinking” in the conscious sense, but its behavior reflects a kind of structural intelligence—one that quietly shapes the path of expression.

Philosophical Postscript: What Does It Mean to “Think”?

Can we call this structured, layered preparation “thinking”?

The Transformer has no awareness, no will.
Yet its internal process, grounded in context, structure, and relation, resembles a functional skeleton of thought—a scaffolding without soul, but with remarkable form.

And in mirroring it, we are perhaps made aware of how our own thoughts are structured.

Note on This Article

This piece is not meant to anthropomorphize AI, but to offer a metaphorical insight into how Transformers operate.

The next time you see “Thinking…” on your screen, consider that behind those three dots,
a silent architecture of intelligence is momentarily unfolding—
and offering you its most coherent answer.