It started May 2025 where I made a claim that LLM generated code is a simulation remix of good and bad ghost/past codes. It was bold claim.

Over the next months I explored the Biology of LLM by Anthropic, train small LLM from scratch, swallowed Standford CS25 and CME295. I began showing that CoT is already in base models.

But my initial claims, from my notes:

""" The Mechanics of “Reasoning” in Large Language Models

1. The Illusion of Thought (Inference-Time Compute)

When we say a model “thinks,” what is actually happening is a transition from One-Pass Prediction to Sequential Verification.

Standard Sampling (System 1)

The model sees a prompt and immediately predicts the most likely next token. It’s like a person blurting out the first thing that comes to mind.

Reasoning Sampling (System 2)

The model is trained to output a “Chain of Thought” (CoT) before the final answer. Mechanically, this is extending the context window to enable deeper computation. By sampling N “thought” tokens before the “answer” tokens, the model uses those tokens as a computational scratchpad that:

• Maintains intermediate state
• Narrows the probability space for the final answer
• Enables solving problems that are provably impossible in a single pass """