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arXiv AI By Jingjie Qiu, Sheldon Yu, Xunyi Jiang, Jiajin Liu, Zihan Huang, Zhendong Chu, Tong Yu, Julian McAuley, Junda Wu

Does Latent Chain-of-Thought Actually Reason? A Single, Unifying Taxonomy for Latent Reasoning

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The paper investigates whether latent chain-of-thought (latent CoT) models truly perform reasoning by examining twenty-one model-task configurations across different training recipes, tasks, and backbones. Using chain deletion, content replacement, probes, and trajectory analysis, the authors find that on logical tasks the latent chain often contains an answer before the chain starts and can be deleted with little effect, while on arithmetic tasks the chain’s content is essential. The study introduces a taxonomy that distinguishes what a chain represents from how a model uses it, clarifying that readable latent states alone do not prove reasoning.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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