Is AI reasoning right for the wrong reasons?
Points and comments are a snapshot, not live.
Research suggests AI 'reasoning' models may rely on pattern matching, not logical deduction.
Melanie Mitchell, Subbarao Kambhampati, and other researchers argue that large reasoning models (LRMs) generate chain-of-thought text that is often neither faithful to internal processes nor causally necessary for correct answers. Studies show up to 60% of thinking steps can be removed without performance loss, and meaningless filler tokens can substitute for human-readable reasoning. Kambhampati proposes LRMs perform 'approximate retrieval' from training data, using tokens to prime the context window rather than trace real logic. OpenAI's Sébastien Bubeck defends the models, citing recent mathematical achievements.
What commenters are saying
Some commenters defend the models' practical utility, noting real-world gains in software work. Others side with the researchers, cautioning against anthropomorphizing outputs. A few dismiss the debate as semantic, with one quoting Dijkstra on computational capabilities. A top comment suggests regulation to discourage AI anthropomorphization by removing personal pronouns from interfaces.