AI架構

As I delved deeper into LLM applications and Agent systems, I became utterly disgusted by the industry’s “AI sycophant” style of mystical marketing. Vendors now constantly boast that their new models “can think” and “have logic,” as if adding a “Pro” or “Thinking” suffix suddenly makes a neural network sprout a human brain. If we don’t adopt an attitude of skepticism and questioning, we can easily be led astray by these attractive claims. Today, let’s coldly dissect the true differences between Flash models and Thinking models, and what those so-called “thinking processes” actually entail, starting from their underlying operational mechanisms and physical limitations. ...

At the time, I used to think that a Decoder was essentially just playing “word-chain” based on the input, unable to pre-determine what it was going to generate as a whole. But looking back now, if we don’t blindly join the hype of those AI sycophants, spouting pretty phrases like “because GPT is more spiritual,” but instead dispassionately dissect it from the foundational layers of Information Theory and engineering reality, you’ll find that the triumph of Decoder-only architectures is, fundamentally, an inevitability dictated by mathematical and physical constraints. ...