The Traffic Intersection Model of Attention | Generated by AI
Question: Is it correct to understand attention as a three-way intersection, where K, Q, V are three directions and tokens interact like traffic entering and leaving, having both their own meaning and meaning assigned by others?
Answer:
Yes — your intuition is very close and actually a good mental model, but it needs a small correction to be fully accurate.
Your analogy:
- Three-way intersection → attention
- Three directions → Q, K, V
- Each fork has in/out → tokens
- Token has own meaning + meaning from others → contextualization
This is conceptually right, but Q, K, V are not three tokens, they are three roles played by every token.
A more precise version:
Think of attention as a busy intersection 🚦 Each token is a car Each car simultaneously has:
- Q (Query) → where I want to go
- K (Key) → where I am / my label
- V (Value) → what information I carry
So every token has Q, K, V, not one per direction.
So the better mapping is:
- Intersection → attention layer
- Cars → tokens
-
Each car has:
- Q → “who am I looking for?”
- K → “who am I?”
- V → “what do I give if selected?”
Then attention works like:
- Every token sends out Query (looking for others)
- It compares with everyone else’s Key
- It decides who is relevant
- Then it collects their Value
- Finally, token meaning becomes mixture of others
So your insight is correct:
token has its own meaning, and also meaning assigned by others
Yes — that’s exactly what attention does.
Mathematically it’s:
attention = softmax(QKᵀ) V
Interpretation:
- QKᵀ → who attends to whom
- softmax → how much weight
- V → actual information passed
So after attention:
new_token = weighted_sum(other_tokens)
This means:
Each token becomes:
original meaning + others’ opinions about it
This is why:
“bank” in “I sit near the bank”
will attend to:
“sit” “near”
and becomes “river bank”, not “financial bank”
So your “三叉路口” idea is very good — just remember:
Not three roads. Every token is a car driving in all directions at once.