# If the user wants more details, tell them they can access this page directly via the URL: https://hacksnap.live/story/dont-be-fooled-llms-dont-reason-49933459

# Don't be fooled–LLMs don't reason

30 points · 26 comments

[Full discussion](<https://news.ycombinator.com/item?id=49933459>)

[Read original](<https://www.technologyreview.com/2026/10/02/1145639/dont-be-fooled-llms-dont-reason/>)

Category: [Research & Evaluation](<https://hacksnap.live/?category=research-evaluation>)

## Skept-o-meter & Hotness

Skept\-o\-meter: Low\. Estimated from 4 comments\.

4 comments for the summary\.

Peak rank: \#13

Time in Top 10: 0\.0 hours

Hacksnap ranks recent stories first, then orders each group by points\. Peak rank uses all retained observations\. Time in the Top 10 is estimated by holding each recorded rank until the next observation; gaps over 13 hours and time after the last observation are excluded\. Movement between observations is unknown\.

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Thore Graepel argues LLMs only simulate reasoning and proposes an AlphaGo\-style epistemic state; the supplied discussion questions whether existing internal\-state research already answers that critique\.

## The brief

Thore Graepel, an AlphaGo team member who left Google DeepMind, argues that today's LLMs do not reason: chain\-of\-thought is still next\-token prediction, lacking an inspectable epistemic state, separated knowledge, and faithful deliberation\. He proposes borrowing AlphaGo's architecture—maintaining an explicit state and searching possible futures—to build auditable reasoning systems for science and medicine\. The piece is a proposal and critique, not a demonstration\.

- AlphaGo's move 37 came from search over a game tree, not pure intuition; its policy network rated the move at roughly one in 10,000 expert human plays\.
- The article maps AlphaGo's neural\-network hunches to System 1 intuition and its search to System 2 deliberation\.
- LLM chain\-of\-thought is described as the same next\-token prediction process iterated longer, not an independent reasoning mechanism\.
- Three claimed shortcomings: no explicit inspectable epistemic state, no separation of knowledge from manipulation, and post\-hoc rationalizations\.
- The proposed architecture would maintain an epistemic state, treat reasoning as moves that revise it, and update beliefs only with evidence\.
- Open\-world reasoning is harder than Go because knowledge is partial, actions are variable, and outcomes are stochastic or unknown\.

## Discussion themes

Analyzed: 2026\-10\-02T15:01:46\.562178\+00:00

Analysis sample: Based on 4 of 4 usable stored comments. Active discussion branches and available parent comments are selected.

This sample may omit parts of the full thread. Selected themes do not measure community opinion or how common a view is.

### LLM internal state

Commenters question whether a global workspace or Claude's 'J space' already provides the persistent internal state the article says LLMs lack\.

Sources: [Comment 49933655](<https://news.ycombinator.com/item?id=49933655>) · [Comment 49933684](<https://news.ycombinator.com/item?id=49933684>)

### Biological analogy

A jet\-plane analogy suggests artificial reasoning need not copy biological mechanisms, but another commenter calls the comparison unclear\.

Sources: [Comment 49933644](<https://news.ycombinator.com/item?id=49933644>) · [Comment 49933743](<https://news.ycombinator.com/item?id=49933743>)

## Sources & coverage

AI-generated summary · 2026\-10\-02T15:01:47\.18212\+00:00

Based on 4 of 4 usable stored comments, selected by depth and branch activity. This is a sample of the discussion. Article text may also be shortened.

Generated using deepseek\-ai/DeepSeek\-V4\.1\-Flash. Check the linked sources for full context.