Ideas Weave Every Narrative with AI.
New research confirms what many users have suspected: LLMs’ chain-of-thought reasoning is often a post-hoc rationalization, not a faithful trace of the model’s actual decision. The mechanism designed for transparency is creating a more convincing illusion, making errors harder to detect. Here’s why you should stop trusting the ‘thinking’ you see.
Stanford’s CS329A course reveals the hidden frontier of AI: not bigger models, but smarter inference-time compute and reliable verifiers. Small models, given 10,000 attempts, can outperform GPT-4. The real bottleneck isn’t parameters—it’s building verifiers that can judge complex outputs. The next AI revolution won’t be about scale; it will be about trust and self-correction.
A 35B model running at 225 tok/s on a 4090 sounds like a breakthrough — until you realize the 2-bit quantization may be quietly destroying the model’s reasoning ability. The missing accuracy graph is a red flag: speed without fidelity is a dangerous trade-off for anyone who needs reliable, long-chain thinking. Don’t confuse throughput with intelligence.