AINeutralarXiv – CS AI · 3h ago6/10
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Do Agents Think Deeper? A Mechanistic Investigation of Layer-Wise Dynamics in Sequential Planning
Researchers conducted a mechanistic analysis of how large language models allocate computational depth when operating as autonomous agents performing multi-turn planning and tool use. The study reveals that agents progressively recruit deeper layers as task complexity increases, contrasting with prior findings that LLMs underutilize depth in single-turn tasks, suggesting adaptive depth allocation emerges in sequential reasoning scenarios.