Do Agents Think Deeper? A Mechanistic Investigation of Layer-Wise Dynamics in Sequential Planning
Zhenyu Cui, Xiangzhong Luo
Why It Matters
What makes this one worth your time
Understanding depth allocation in LLMs for agentic tasks can enhance the design of more efficient and effective autonomous agents.
The paper explores how LLMs use their depth differently in agentic tasks compared to static tasks.
Summary
The paper investigates how large language models (LLMs) allocate their depth in autonomous agent settings, focusing on multi-turn planning and iterative state updates across three domains. It uses layer-wise analysis, residual stream probes, and effective-depth measurements to show that agentic reasoning involves deeper layers and stronger inter-layer dependencies over time.
Key contributions
- Systematic layer-wise analysis of LLMs in multi-turn agentic tasks.
- Identification of distinct depth profiles for agentic reasoning compared to static tasks.
- Evidence of adaptive depth allocation in LLMs as reasoning complexity increases.
Notable insights
- Agentic reasoning in LLMs recruits deeper layers with stronger inter-layer dependencies as tasks progress.
- There is a construction-refinement gap where semantic direction forms early, but deep layers stabilize outputs.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2605.27935v1 Announce Type: new Abstract: Recent mechanistic studies suggest that large language models (LLMs) may utilize their depth inefficiently in standard single-turn tasks. Whether this still holds in autonomous agent settings, where models must perform multi-turn planning, tool use, and iterative state updates, remains unclear. We study this question through a systematic layer-wise analysis of complete user-agent trajectories spanning three domains: Deep Research, Code Generation, and Tabular Processing. Using residual stream probes, causal layer-skipping interventions, and effective-depth measurements, we show that agentic reasoning exhibits a distinct depth profile from static tasks. As trajectories unfold, models progressively recruit more and deeper layers, with stronger long-range inter-layer dependencies emerging in later turns. At the same time, residual updates become increasingly correction-dominant, indicating a shift from stable feature accumulation toward repeated recalibration. Effective-depth analysis further reveals a substantial construction-refinement gap: semantic direction often forms relatively early, while deep layers remain necessary for stabilizing final outputs. Across model families, this gap is pronounced in Qwen and Minimax, whereas GLM shows a more domain-dependent depth allocation pattern. These results provide mechanistic evidence that autonomous LLM agents allocate depth adaptively as reasoning complexity grows.