Do Agents Think Deeper? A Mechanistic Investigation of Layer-Wise Dynamics in Sequential Planning
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 effec
The increased focus on autonomous agentic systems necessitates a deeper understanding of how LLMs process information over multiple turns, contrasting with earlier single-turn analyses.
Understanding the efficiency and dynamics of LLM processing in agent settings is crucial for developing robust, efficient, and capable AI agents, impacting their deployment and capabilities.
This research provides a mechanistic analysis of LLMs in multi-turn planning, potentially leading to more optimized and resource-efficient agent architectures compared to current, potentially inefficient, deep models.
- · AI agent developers
- · Companies deploying autonomous AI
- · AI infrastructure providers
- · Inefficient AI models
- · AI developers ignoring mechanistic interpretability
Improved understanding of LLM internal workings for complex, multi-step tasks.
Development of more resource-efficient and performant AI agents tailored for specific tasks by optimizing their architecture.
Acceleration in the adoption and effectiveness of AI agents across various industries due to enhanced reliability and lower computational requirements.
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Read at arXiv cs.AI