Don't be fooled by LLMs: large language models do not truly reason
Don’t be fooled—LLMs don’t reason
What happened
A former Google DeepMind researcher says an LLM's chain of thought is simply an extended iteration of the same next-token prediction process, not an independent reasoning mechanism like AlphaGo's. He identifies three major limitations: no persistent, inspectable cognitive state; knowledge and reasoning tangled together in the weights; and chains of thought that are often fabricated after the fact. He argues for borrowing AlphaGo's game-tree architecture to give machine reasoning an explicit cognitive state.
Keep reading
Latest daily · Issue 24NVIDIA and AI4Bharat Introduce Nemotron-pretraining-Indic-v1 and an Open Data Generation PipelineTrending now
- 1Meta Releases Muse Spark Open With Mac and Cloud Support, Topping US Open-Weight Index
- 2Google Launches Gemini 4 Argon Globally, Expands Paid Access as Benchmark Results Vary
- 3OpenAI Updates GPT-6 Astra Pro in ChatGPT With Parallel Agents and Faster Responses
- 4Meta’s New Business Terms Raise Dispute Over Website Crawling and AI Training Rights
Tomorrow at 08:00, a briefing like this arrives in your inbox.
The same kind of briefing as the one above: a few stories worth reading. Free. Mail starts after you confirm, and every letter has a one-click unsubscribe. Read a past issue · 2026-10-17