AI RESEARCH
Reasoning Models Don't Just Think Longer, They Move Differently
arXiv CS.LG
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ArXi:2605.15454v1 Announce Type: cross Reasoning-trained language models often spend tokens on harder problems, but longer chains of thought do not show whether a model is merely computing for steps or following a different internal trajectory. We study this distinction through hidden-state trajectories during chain-of-thought generation across competitive programming, mathematics, and Boolean satisfiability.