Math reasoning in LLMs organized by reasoning approach, not topic
This paper (arXiv:2609.27041v1) introduces a generation-replay protocol to extract activation-importance signatures from reasoning tokens and clusters those signatures across eight open math-capable LLMs and five math-reasoning sources. The authors report that clusters align with reusable reasoning approaches (77–82% approach-level coherence by human judges) rather than benchmark topics, and that controlling the requested reasoning approach shifts cluster assignments in 7 of 8 model conditions while paraphrases largely preserve them.