Attention × V
Chapter 04 · Understanding Query, Key, and Value (Q, K, V)
Follow the 7 visual stages: Input matrix $X \to$ Projection matrices ($W_Q, W_K, W_V$) $\to$ Library Analogy $\to$ Dot-product score matrix ($Q \cdot K^T$) $\to$ Softmax weights $\to$ Weighted sum of Values $\to$ Multi-head layer assembly.
What is Attention × V? Why does it exist? Where does it appear in QKV?
Draw or describe a visual model of Attention × V. What does it look like conceptually?
Work through a concrete manual example of Attention × V with real numbers or pseudocode.
Implement Attention × V from scratch in Python. Keep it minimal — just enough to see it work.
Try Attention × V on a real example. Vary the inputs and observe what changes.
How is Attention × V different from related concepts in QKV? What are the trade-offs?
Explain Attention × V in plain language using an analogy. Could a non-expert follow it?
Build a small project or demo that uses Attention × V as a core component.
🔨 M-05 attention.py 🔁 Q/K/V intuition → verifier scoring → return to
agent/verifier.py
📋 full plan →