Internal Chain-of-Thought: Empirical Evidence for Layer‐wise Subtask Scheduling in LLMs
Zhipeng Yang, Junzhuo Li, Siyu Xia, Xuming Hu
Abstract
We show that large language models (LLMs) exhibit an internal chain-of-thought : they sequentially decompose and execute composite tasks layer-by-layer. Two claims ground our study: (i) distinct subtasks are learned at different network depths, and (ii) these subtasks are executed sequentially across layers. On a benchmark of 15 two-step composite tasks, we employ layer-from context-masking and propose a novel cross-task patching method, confirming (i). To examine claim (ii), we apply LogitLens to decode hidden states, revealing a consistent layerwise execution pattern. We further replicate our analysis on the real-world TRACE benchmark, observing the same stepwise dynamics. Together, our results enhance LLMs transparency by showing their capacity to internally plan and execute subtasks (or instructions), opening avenues for fine-grained, instruction-level activation steering.
BibTeX
@inproceedings{emnlp2025_internalchainoft,
title = {Internal Chain-of-Thought: Empirical Evidence for Layer‐wise Subtask Scheduling in LLMs},
author = {Zhipeng Yang and Junzhuo Li and Siyu Xia and Xuming Hu},
booktitle = {EMNLP 2025},
year = {2025}
}