arXiv Machine Learning By Aria Masoomi, Mahsa Bazzaz, Adel Javanmard, Vahab Mirrokni

Geometric Signatures of Reasoning: A Spectral Perspective on Task Hardness

Read the original on arXiv Machine Learning →

arXiv:2607. 01571v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning enables large language models (LLMs) to solve complex problems by generating intermediate reasoning steps.

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 Machine Learning.

arXiv Machine Learning
Jun 5

Reasoning Models Don't Just Think Longer, They Move Differently

arXiv:2605. 15454v2 Announce Type: replace-cross Abstract: Reasoning-trained language models often spend more tokens on harder problems, but longer chains of thought do not show whether a model is merely computing for more steps or following a different internal trajectory.

By Anders Gj{\o}lbye, Lars Kai Hansen, Sanmi Koyejo
arXiv AI
Jun 15

Fractured Chain-of-Thought Reasoning

arXiv:2505. 12992v4 Announce Type: replace-cross Abstract: Inference-time scaling techniques have significantly bolstered the reasoning capabilities of large language models (LLMs) by harnessing additional computational effort at inference without retraining.

By Baohao Liao, Hanze Dong, Yuhui Xu, Doyen Sahoo, Christof Monz, Junnan Li, Caiming Xiong