Are we confusing “Chain of Thought” with actual logic? A question on reasoning mechanisms.

I’m trying to deeply understand the mechanism behind LLM reasoning (specifically in models like o1 or DeepSeek).

Mechanism: Is the model actually applying logic gates/rules, or is it just a probabilistic simulation of a logic path? If it “backtracks” during CoT, is that a learned pattern or a genuine evaluation of truth? And how close is this to AGI/Human level reasoning?

The Data Wall: How much of current training is purely public (Common Crawl) vs private? Is the “data wall” real, or are we solving it with synthetic data?

Data Quality: How are labs actually evaluating “Truth” in the dataset? If the web is full of consensus-based errors, and we use “LLM-as-a-Judge” to filter data, aren’t we just reinforcing the model’s own biases?